Search results

This paper aims to examine the global pattern of growth and development of eHealth research based on publication headcount, and analysis of the characteristics, of the keywords used by authors and indexers to represent their research content during 1945–2019.

This study adopted a bibliometric research design and a quantitative approach. The source of the data was Elsevier’s Scopus database. The search query involved multiple search terms because researchers’ choice of keywords varies very significantly. The search for eHealth research publications was limited to conference papers and research articles published before 2020.

eHealth originated in the late 1990s, but it has become an envelope term for describing much older terms such as telemedicine, and its variants that originated much earlier. The keywords were spread through the 27 Scopus Subject Areas, with medicine (44.04%), engineering (12.84%) and computer science (11.47%) leading, while by Scopus All Science Journal Classification Health Sciences accounted for 55.83% of the keywords. Physical sciences followed with 30.62%. The classifications social sciences and life sciences made only single-digit contributions. eHealth is about meeting health needs, but the work of engineers and computer scientists is very outstanding in achieving this goal.

This study demonstrates that eHealth is an unexplored aspect of health literature and highlights the nature of the accumulated literature in the area. It further demonstrates that eHealth is a multidisciplinary area that is attractive to researchers from all disciplines because of its sensitive focus on health, and therefore requires pooling and integration of human resources and expertise, methods and approaches.

Image Processing is an emerging field that is used to extract information from images. In recent years, this field has received immense attention from researchers, especially in the research domains of object detection, Biomedical Imaging and Semantic segmentation. In this study, a bibliometric analysis of publications related to image processing in the Science Expanded Index Extended (SCI-Expanded) has been performed. Several parameters have been analyzed such as annual scientific production, citations per article, most cited documents, top 20 articles, most relevant authors, authors evaluation using y-index, top and most relevant sources (journals) and hot topics.

The Bibliographic data has been extracted from the Web of Science which is well known and the world's top database of bibliographic citations of multidisciplinary areas that covers the various journals of computer science, engineering, medical and social sciences.

The research work in image processing is meager in the past decade, however, from 2014 to 2019, it increases dramatically. Recently, the IEEE Access journal is the most relevant source with an average of 115 publications per year. The USA is most productive and its publications are highly cited while China comes in second place. Image Segmentation, Feature Extraction and Medical Image Processing are hot topics in recent years. The National Natural Science Foundation of China provides 8% of all funds for Image Processing. As Image Processing is now becoming one of the most critical fields, the research productivity has enhanced during the past five years and more work is done while the era of 2005–2013 was the area with the least amount of work in this area.

This research is novel in this regard that no previous research focuses on Bibliometric Analysis in the Image Processing domain, which is one of the hot research areas in computer science and engineering.

Acute and chronic obstructive pulmonary disease (COPD) is a common and progressive lung disease that makes breathing difficult over time and can even lead to death. Despite this, there is no definitive treatment for it yet. This study aims to evaluate the studies on single and combined herbal interventions affecting COPD.

In this study, all articles published in English up to 2020 were extracted from the Web of Science (WoS) database and collected using Boolean tools based on keywords, titles and abstracts. Finally, the data required for bibliographic analysis, such as the author(s), publication year, academic journal, institution, country of origin, institution, financial institution and keywords were extracted from the database.

A total of 573 articles were analyzed. The number of papers in the lung disease field showed an upward trend from 1984 to 2021, and there was a surge in paper publications in 2013. China, Korea and Brazil published the highest number of studies on COPD, and Chinese medical universities published the most papers. Three journals that received the highest scores in this study were the Journal of Ethnopharmacology, International Immunopharmacology and Plos One. In the cloud map, expression, activation and expression were the most frequently researched subjects. In the plus and author keywords, acute lung injury was the most commonly used word. Inflammation, expression of various genes, nitric oxide-dependent pathways, NFkappa B, TNFalpha and lipopolysaccharide-dependent pathways were the mechanisms underlying COPD. Scientometric analysis of COPD provides a vision for future research and policymaking.

This study aimed to evaluate the studies on single and combined herbal interventions affecting COPD.

The COVID-19 pandemic has had a significant impact on medical education and training, with many medical schools and training programs having to adapt to remote or online learning, social distancing measures and other challenges. This paper aimed to examine the disruption for clinical training, as it has reduced the opportunities for students and trainees to gain hands-on experience and interact with patients in person.

The ethnographic qualitative research design was chosen as the research methodology. Using Gibbs' reflective cycle, the researcher explored the psychiatry clerks' (final-year medical students) reflections on the disruption of their clinical training during the COVID-19 pandemic.

The findings demonstrated that the students had a significant psychological impact on their coping capacities as the crisis progressed from shock and depression to resilience. The students being the key stakeholders provided a concrete foundation for the development of a framework for improving practices during uncertain times.

Students' reflections provided valuable insight into the pandemic’s impact on their psychosocial lives with uncertainty and incapacity to cope up with changing stressful dynamics. The results will assist in planning how to best support medical students' well-being during interruptions of their educational process brought about by similar future crises.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

This study aims to explore the experiences of living through the COVID-19 pandemic for people who faced homelessness and dealt with mental health and/or substance use challenges.

This qualitative study was comprised of 26 1:1 interviews (16 men and 10 women), conducted between February and May 2021 with people who experienced homelessness in North East England during the COVID-19 pandemic. An inductive reflexive thematic analysis was undertaken, with input from individuals with lived experience who were involved throughout the study.

Four themes were developed. The first theme, lack of support and exacerbation of mental health and substance use difficulties, highlighted how the lack of in-person support and increased isolation and loneliness led to relapses or new challenges for many people’s mental health and substance use. The second theme, uncertainty and fear during the pandemic, explored how the “surreal” experience of the pandemic led to many people feeling uncertain about the future and when things would return to normal. The third theme, isolation and impacts on social networks, discussed how isolation and changes to relationships also played a role in mental health and substance use. Finally, opportunity for reflection and self-improvement for mental health and substance use, explored how some people used the isolated time to re-evaluate their recovery journey and focus on self-improvement.

The experiences shared within this study have important implications for planning the future delivery and commissioning of health and social care services for people facing homelessness, such as sharing information accessibly through clear, consistent and simple language.

As one of the few papers to involve people with lived experience as part of the research, the findings reflect the unique narratives of this population with a focus on improving services.

Studying the shear stress and pressure resulting on the walls of blood vessels, especially during high-pressure cases, which may lead to the explosion or rupture of these vessels, can also lead to the death of many patients. Therefore, it was necessary to try to control the shear and normal stresses on these veins through nanoparticles in the presence of some external forces, such as exposure to some electromagnetic shocks, to reduce the risk of high pressure and stress on those blood vessels. This study aims to examines the shear and normal stresses of electroosmotic-magnetized Sutterby Buongiorno’s nanofluid in a symmetric peristaltic channel with a moderate Reynolds number and curvature. The production of thermal radiation is also considered. Sutterby nanofluids equations of motion, energy equation, nanoparticles concentration, induced magnetic field and electric potential are calculated without approximation using small and long wavelengths with moderate Reynolds numbers.

The Adomian decomposition method solves the nonlinear partial differential equations with related boundary conditions. Graphs and tables show flow features and biophysical factors like shear and normal stresses.

This study found that when curvature and a moderate Reynolds number are present, the non-Newtonian Sutterby fluid raises shear stress across all domains due to velocity decay, resulting in high shear stress. Additionally, modest mobility increases shear stress across all channel domains. The Sutterby parameter causes fluid motion resistance, which results in low energy generation and a decrease in the temperature distribution.

Equations of motion, energy equation, nanoparticle concentration, induced magnetic field and electric potential for Sutterby nano-fluids are obtained without any approximation i.e. the authors take small and long wavelengths and also moderate Reynolds numbers.

Emotional intelligence (EI) can improve patient–doctor relationships, foster empathy, develop teamwork in the workplace, and boost communication skills. This study aims to determine whether a cohort of residents has different profiles to their demographic and EI.

A cross-sectional survey was conducted in 2017–2018 (n = 440). The Trait Emotional Intelligence Questionnaire was used to measure EI. Cluster analysis was used to identify different profile groups.

Cluster analysis identified three clusters with different profiles. The residents in cluster A (n = 146, 33.2%) were older, more males, studying in their final year, and perceived lower EI. Cluster B (n = 184, 41.8%) were predominantly young females, more single, studying in year one, and perceived moderate EI. Cluster C (n = 110, 25.0%) were predominantly married females studying their year 1 in surgical and perceived higher EI.

Study limitations include respondent honesty, cross-sectional design, and lack of a comparison site. Including EI education can improve emotional regulation, well-being, and sociability and should be assessed as part of residents' development.

Medical residents differ in emotional profiles, with higher EI improving coping and problem-solving skills. EI training should be integrated into the medical curriculum, particularly given the high-stress levels and unique stressors of hospital practicums.

The study suggested that three groups of residents exist, and they differ in demographic, EI, and subscale levels. This study recommended that residents be taught EI-related concepts to help them develop their EI through training on emotionality, self-control, well-being, and sociability.

The purpose of this study is to create a methodological approach for identifying priority areas for science and technology (S&T) development and its empirical application within the city of Moscow. This research uncovers a wide range of multicultural and multidisciplinary global trends that will affect the development of major cities in an era of complexity and uncertainty, including the inherent complexity of urban contexts, demographic and socioeconomic trends, as well as scientific and ecological factors.

The methodological approach is based on classic foresight instruments. Its novelty lays in the blending of qualitative and quantitative methods specially selected as the most appropriate for the identification of S&T areas in an era of complexity and uncertainty, including horizon scanning, bibliometric analysis, expert surveys and the construction of composite indexes with respect to the scope and resources of the research and the selected object for empirical application – Moscow, which is one of the world’s largest megacities. The analysis was performed for the period of 2009–2018 and expert procedures took place in 2019.

As a result, 25 global trends were identified, evaluated and discussed over the course of an expert survey and subsequent expert events. Ten priority areas of S&T development were determined, including 62 technological sub-areas within them and the most important market niches for all identified technological sub-areas, which could be useful for the world’s megacities. The results of this study are illustrated using the construction sector. Based on the conducted research and results, a list of recommendations on S&T policy measures and instruments were suggested, including the creation of the Moscow Innovation Cluster, which by the end of 2023 contained more than 6,000 projects and initiatives, selected using the findings of this investigation.

This research contributes to the existing literature and research agenda of setting priorities for S&T development and shows how it can be done for a megacity. The blended foresight methodology that was created within the study satisfies the criteria of scientific originality, is repeatable for any interested researcher, is applicable to any other city in the world and demonstrates its high efficiency in empirical application. It could be used for creating new agenda items in S&T policy, setting S&T priorities for a megacity and integrating the results into decision-making processes. This study provides recommendations on the further implementation of the designed methodology and results into a policymaking system. Moreover, the example of the Moscow Innovation Cluster, which was created based on the results of our research, demonstrates these recommendations’ practical significance in real life, which is quite valuable. The limitation of this study is that it is not devoted to urban planning issues directly or the promotion of R&D areas; it is about setting promising S&T priorities in an era of complexity and uncertainty for megacities.

For the provision of smart library services to end users, tools of the Internet of Things (IoT) play a significant role. The study aims to discover the factors influencing the adoption of IoT in university libraries, investigate the impact of IoT on university library services and identify challenges to adopt IoT applications in university libraries.

A systematic literature review was carried out to address the objectives of the study. The 40 most relevant research papers published in the world’s leading digital databases were selected to conduct the study.

The findings illustrated that rapid growth in technology, perceived benefits, the networked world and the changing landscape of librarianship positively influenced the adoption of IoT in university libraries. The study also displayed that IoT supported library professionals to initiate smart library services, assisted in service efficiency, offered context-based library services, provided tracking facilities and delivered effective management of library systems. Results also revealed that a lack of technical infrastructure, security and privacy concerns, a lack of technological skills and unavailability of policy and strategic planning caused barriers to the successful adoption of IoT applications in university libraries.

The study has provided theoretical implications through a valuable addition to the current literature. It has also offered managerial implications for policymakers to construct productive policies for the implementation of IoT applications in university libraries for the attainment of fruitful outcomes. Finally, the study provides a baseline for understanding the adoption of IoT in academic libraries.