Producción Científica

Bibliometric mapping analysis of Pickering emulsion applied in 3D food printing

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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.

  • Autor/es: Patrícia Marques de Farias, Julia Rabelo Vaz Matheus, Bianca Chieregato Maniglia, Patricia Le-Bail, Alain Le-Bail, Markus Schmid, Ana Elizabeth Cavalcante Fai
  • Año de publicación: 2024
  • País: Brasil, Francia, Alemania
  • Idioma: Inglés
  • Fuente de indexación: Scopus

CORNCOB-DERIVED SULFONATED MAGNETIC SOLID CATALYST SYNTHESIS AS HETEROGENEOUS CATALYST IN THE ESTERIFICATION OF WASTE COOKING OIL AND BIBLIOMETRIC ANALYSIS

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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.

  • Autor/es: Primata Mardina, Hesti Wijayanti, Rinna Juwita, Meilana Dharma Putra, Iryanti Fatyasari Nata, Rowina Lestari, Muhammad Faqih Al-Amin, Regi Abizar Suciagi, Oktefani Kusuma Rawei, Liza Lestari
  • Año de publicación: 2024
  • País: Indonesia, Corea del Sur
  • Idioma: Inglés
  • Fuente de indexación: Scopus

The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis

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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.

  • Autor/es: Chuheng Chang, Wen Shi, Youyang Wang, Zhan Zhang, Xiaoming Huang, Yang Jiao
  • Año de publicación: 2024
  • País: China
  • Idioma: Inglés
  • Fuente de indexación: Scopus

Scientific Mapping of Chatbot Literature: A Bibliometric Analysis

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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.

  • Autor/es: Tanwar, Manju; Verma, Harsh V.
  • Año de publicación: 2024
  • País: India
  • Idioma: Inglés
  • Fuente de indexación: Scopus

Occupancy and N-mixture modeling applications in ecology: A bibliometric analysis

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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.

  • Autor/es: Laurentiu Rozylowicz, Viorel D. Popescu, Steluta Manolache, Andreea Nita, Simona R. Gradinaru, Marian D. Mirea, Raluca I. Bancila
  • Año de publicación: 2024
  • País: Rumanía, Estados Unidos
  • Idioma: Inglés
  • Fuente de indexación: Scopus

Global research evolution and frontier analysis of artificial intelligence in brain injury: A bibliometric analysis

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Research on artificial intelligence for brain injury is currently a prominent area of scientific research. A significant amount of related literature has been accumulated in this field. This study aims to identify hotspots and clarify research resources by conducting literature metrology visualization analysis, providing valuable ideas and references for related fields. The research object of this paper consists of 3000 articles cited in the core database of Web of Science from 1998 to 2023. These articles are visualized and analyzed using VOSviewer and CiteSpace. The bibliometric analysis reveals a continuous increase in the number of articles published on this topic, particularly since 2016, indicating significant growth. The United States stands out as the leading country in artificial intelligence for brain injury, followed by China, which tends to catch up. The core research institutions are primarily universities in developed countries, but there is a lack of cooperation and communication between research groups. With the development of computer technology, the research in this field has shown strong wave characteristics, experiencing the early stage of applied research based on expert systems, the middle stage of prediction research based on machine learning, and the current phase of diversified research focused on deep learning. Artificial intelligence has innovative development prospects in brain injury, providing a new orientation for the treatment and auxiliary diagnosis in this field.

  • Autor/es: Mengqi Qu, Yang Xu, Lu Lu
  • Año de publicación: 2024
  • País: China
  • Idioma: Inglés
  • Fuente de indexación: Scopus

Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics

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Advancements in artificial intelligence (AI) have driven extensive research into developing diverse multimodal data analysis approaches for smart healthcare. There is a scarcity of large-scale analysis of literature in this field based on quantitative approaches. This study performed a bibliometric and topic modeling examination on 683 articles from 2002 to 2022, focusing on research topics and trends, journals, countries/regions, institutions, authors, and scientific collaborations. Results showed that, firstly, the number of articles has grown from 1 in 2002 to 220 in 2022, with a majority being published in interdisciplinary journals that link healthcare and medical research and information technology and AI. Secondly, the significant rise in the quantity of research articles can be attributed to the increasing contribution of scholars from non-English speaking countries/regions and the noteworthy contributions made by authors in the USA and India. Thirdly, researchers show a high interest in diverse research issues, especially, cross-modality magnetic resonance imaging (MRI) for brain tumor analysis, cancer prognosis through multi-dimensional data analysis, and AI-assisted diagnostics and personalization in healthcare, with each topic experiencing a significant increase in research interest. There is an emerging trend towards issues such as applying generative adversarial networks and contrastive learning for multimodal medical image fusion and synthesis and utilizing the combined spatiotemporal resolution of functional MRI and electroencephalogram in a data-centric manner. This study is valuable in enhancing researchers’ and practitioners’ understanding of the present focal points and upcoming trajectories in AI-powered smart healthcare based on multimodal data analysis.

  • Autor/es: Xieling Chen, Haoran Xie, Xiaohui Tao, Fu Lee Wang, Mingming Leng, Baiying Lei
  • Año de publicación: 2024
  • País: China, Hong Kong, Australia
  • Idioma: Inglés
  • Fuente de indexación: Scopus

Roles of innovation in achieving the Sustainable Development Goals: A bibliometric analysis

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As we approach the midpoint of the Agenda 2030 programme, scientists are increasingly reliant on innovative solutions to help bring us closer to achieving the Sustainable Development Goals (SDGs). This study aims to analyse the intellectual structure of academic literature on the SDGs, Innovation, and Science, Technology and Innovation (STI). Using a database of 544 English-language publications from Scopus and Web of Science published between 2015 and 2023, we employ a three-pronged approach comprising bibliometric analyses, SDG mapping and text-mining techniques. Our findings indicate that innovations in one cluster defined in the analysis display economic, social and environmental dimensions. Furthermore, the underlying roles of innovation in the literature are found to relate to promoting sustainable development, driving economic growth, enhancing enterprise performance and strengthening policies. Within the sample literature, all 17 goals were identified by the SDG Mapper. Among the 5Ps (People, Planet, Prosperity, Peace and Partnerships), there was a clear preponderance of articles on Prosperity. The text mining of titles and abstracts indicates that the term “sti” is less commonly associated with the SGDs than “innovation”. However, there is some evidence that the term “innovation” is used in titles and abstracts to attract a broader audience. Our study highlights research gaps and identifies opportunities for future studies.

  • Autor/es: Gulnara Dzhunushalieva, Ramona Teuber
  • Año de publicación: 2024
  • País: Kirguistán, Alemania
  • Idioma: Inglés
  • Fuente de indexación: Scopus
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