Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature

Bibliometric analysis on CRISPR/Cas: a potential Sherlock Holmes for disease detection

CRISPR has revolutionized illness detection by using precision gene editing to identify specific sequences in recent years. Using the Scopus database, this study performs a comprehensive bibliometric analysis, looking at academic papers on CRISPR that were published between 1992 and 2023. After screening a dataset of 1407 articles using Zotero, trends in annual publishing, citation patterns, author affiliations, and keyword co-occurrence are revealed using analysis tools such as VOSviewer, RStudio, and MS Excel. According to the report, there was only one CRISPR publication in 1992. By 2017, there were a meager 64 papers. Nonetheless, there is a notable upsurge between 2018 and 2023. Leading nations involved in CRISPR-based illness detection research include Germany, the United States, China, India, and the United Kingdom. Chongqing University Three Gorges Hospital, Chongqing University Medical University, and Chongqing University Bioengineering College are a few of the top institutions. With the greatest publication numbers (1688 and 1616) and strong total link strengths (TLS) of 42 and 77, respectively, authors Liu, C., and Li, Y., stand out. The field with the greatest citation counts as of 2023 is Broughton's 2020 study on CRISPR-based SARS-CoV-2 detection in Nature Biotechnology, with 1598 citations. Biosensors and Bioelectronics comprise 14.99% of papers. Researchers, decision-makers, and interested parties can use this thorough summary to help them make well-informed decisions about future CRISPR-based disease detection studies.

Darkweb research: Past, present, and future trends and mapping to sustainable development goals

The Darkweb, part of the deep web, can be accessed only through specialized computer software and used for illegal activities such as cybercrime, drug trafficking, and exploitation. Technological advancements like Tor, bitcoin, and cryptocurrencies allow criminals to carry out these activities anonymously, leading to increased use of the Darkweb. At the same time, computers have become an integral part of our daily lives, shaping our behavior, and influencing how we interact with each other and the world. This work carries out the bibliometric study on the research conducted on Darkweb over the last decade. The findings illustrate that most research on Darkweb can be clustered into four areas based on keyword co-occurrence analysis: (i) network security, malware, and cyber-attacks, (ii) cybercrime, data privacy, and cryptography, (iii) machine learning, social media, and artificial intelligence, and (iv) drug trafficking, cryptomarket. National Science Foundation from the United States is the top funder. Darkweb activities interfere with the Sustainable Development Goals (SDG) laid forth by the United Nations to promote peace and sustainability for current and future generations. SDG 16 (Peace, Justice, and Strong Institutions) has the highest number of publications and citations but has an inverse relationship with Darkweb, as the latter undermines the former. This study highlights the need for further research in bitcoin, blockchain, IoT, NLP, cryptocurrencies, phishing and cybercrime, botnets and malware, digital forensics, and electronic crime countermeasures about the Darkweb. The study further elucidates the multi-dimensional nature of the Darkweb, emphasizing the intricate relationship between technology, psychology, and geopolitics. This comprehensive understanding serves as a cornerstone for evolving effective countermeasures and calls for an interdisciplinary research approach. The study also delves into the psychological motivations driving individuals towards illegal activities on the Darkweb, highlighting the urgency for targeted interventions to promote pro-social online behavior.

A Comprehensive, Valid, and Reliable Tool to Assess the Degree of Responsibility of Digital Health Solutions That Operate With or Without Artificial Intelligence: 3-Phase Mixed Methods Study

BACKGROUND

Clinicians' scope of responsibilities is being steadily transformed by digital health solutions that operate with or without artificial intelligence (DAI solutions). Most tools developed to foster ethical practices lack rigor and do not concurrently capture the health, social, economic, and environmental issues that such solutions raise.

OBJECTIVE

To support clinical leadership in this field, we aimed to develop a comprehensive, valid, and reliable tool that measures the responsibility of DAI solutions by adapting the multidimensional and already validated Responsible Innovation in Health Tool.

METHODS

We conducted a 3-phase mixed methods study. Relying on a scoping review of available tools, phase 1 (concept mapping) led to a preliminary version of the Responsible DAI solutions Assessment Tool. In phase 2, an international 2-round e-Delphi expert panel rated on a 5-level scale the importance, clarity, and appropriateness of the tool's components. In phase 3, a total of 2 raters independently applied the revised tool to a sample of DAI solutions (n=25), interrater reliability was measured, and final minor changes were made to the tool.

RESULTS

The mapping process identified a comprehensive set of responsibility premises, screening criteria, and assessment attributes specific to DAI solutions. e-Delphi experts critically assessed these new components and provided comments to increase content validity (n=293), and after round 2, consensus was reached on 85% (22/26) of the items surveyed. Interrater agreement was substantial for a subcriterion and almost perfect for all other criteria and assessment attributes.

CONCLUSIONS

The Responsible DAI solutions Assessment Tool offers a comprehensive, valid, and reliable means of assessing the degree of responsibility of DAI solutions in health. As regulation remains limited, this forward-looking tool has the potential to change practice toward more equitable as well as economically and environmentally sustainable digital health care.

A bibliometric analysis of building information modelling implementation barriers in the developing world using an interpretive structural modelling approach

Over the past 20 years, the phrase "Building Information Modelling" (BIM) has spread throughout the Architecture, Engineering and Construction (AEC) industries. BIM usage in the construction industry is vital in the revolution towards Industry 4.0 in the AEC Industry. BIM contributes to this change due to its automatization and sustainability features. However, there are growing concerns about its implementation in the developing world context. The BIM Implementation Barriers (BIMIBs) in individual countries and on a global scale have been examined in a variety of studies and works of literature, but two research questions are still open; (1) what specific BIMIBs are the AEC industries in the developing world encountering the most, and (2) what is the interrelationship between these barriers? Through a combination of expert interviews and a bibliometric analysis of published relevant empirical studies on the subject, the aim of this study is to identify these frequently occurring BIMIBs in the developing world and to determine the interrelationships between these barriers using an Interpretive Structural Modelling (ISM) approach and MICMAC analysis. The study identified the 14 BIMIBs with 'high associated cost' as the most fundamental of all. A comparison of the study's findings and a proposed 3-level barrier mitigation strategy with other studies identified the lack of governmental support for BIM implementation and research as a root cause of majority of the BIMIBs identified in the developing world. This study lays forth the knowledge base for future studies in the area of BIM implementation in the developing world.

A scientometric analysis of global scientific literature on learning resources in higher education

There is a significant increase in the literature on learning resources in Higher Education (HE) but very limited evidence of studies that have taken a global overview of the context, range, and emerging trends from the previous research. This study aims to conduct a Scientometric analysis of research articles to accommodate a global overview and research trends under the theme of learning resources in HE. 4489 scientific articles were obtained as the dataset from the Web Of Science database between 1970 and 2022. Network maps and critical data were obtained by conducting co-authorship analysis for authors, organisations and countries and co-occurrence analysis for keywords from the VOSviewer software. The study revealed that the USA had a significant research input, and Salamin, N. from the University of Lausanne was recognised as the most frequently published author. The University of Illinois, USA, has the highest contribution to research articles, and the most popular research hotspots and trends were e-learning, Education, Academic libraries, Learning resources, and Cloud computing. However, the most critical finding from the study is that there needs to be real collaboration within the research theme and suggests ways to improve collaborations to enhance learning resources in HE. This study may be the first to conduct a scientometric analysis of Learning Resources in Higher education. This study offers valuable insight to academics, academic institutions, researchers, policymakers and pedagogical statutory bodies to understand the current context of learning resources in HE and recognise further develop research, collaborations and policies by considering critical findings from the study.

Tools to foster responsibility in digital solutions that operate with or without artificial intelligence: A scoping review for health and innovation policymakers

BACKGROUND

Digital health solutions that operate with or without artificial intelligence (D/AI) raise several responsibility challenges. Though many frameworks and tools have been developed, determining what principles should be translated into practice remains under debate. This scoping review aims to provide policymakers with a rigorous body of knowledge by asking: 1) what kinds of practice-oriented tools are available?; 2) on what principles do they predominantly rely?; and 3) what are their limitations?

METHODS

We searched six academic and three grey literature databases for practice-oriented tools, defined as frameworks and/or sets of principles with clear operational explanations, published in English or French from 2015 to 2021. Characteristics of the tools were qualitatively coded and variations across the dataset identified through descriptive statistics and a network analysis.

FINDINGS

A total of 56 tools met our inclusion criteria: 19 health-specific tools (33.9%) and 37 generic tools (66.1%). They adopt a normative (57.1%), reflective (35.7%), operational (3.6%), or mixed approach (3.6%) to guide developers (14.3%), managers (16.1%), end users (10.7%), policymakers (5.4%) or multiple groups (53.6%). The frequency of 40 principles varies greatly across tools (from 0% for 'environmental sustainability' to 83.8% for 'transparency'). While 50% or more of the generic tools promote up to 19 principles, 50% or more of the health-specific tools promote 10 principles, and 50% or more of all tools disregard 21 principles. In contrast to the scattered network of principles proposed by academia, the business sector emphasizes closely connected principles. Few tools rely on a formal methodology (17.9%).

CONCLUSION

Despite a lack of consensus, there is a solid knowledge-basis for policymakers to anchor their role in such a dynamic field. Because several tools lack rigour and ignore key social, economic, and environmental issues, an integrated and methodologically sound approach to responsibility in D/AI solutions is warranted.

Trends and characteristics of employing cavitation technology for water and wastewater treatment with a focus on hydrodynamic and ultrasonic cavitation over the past two decades: A Scientometric analysis

Cavitation-based technologies have emerged as a sustainable and effective way to treat natural waters and wastewater, considering their increasing scarcity due to pollution and climate change. For this reason, this work aimed to conduct a scientometric analysis on the topic of cavitation for water and wastewater treatment during the last 20 years, from 2001 to August 2022. We focused on hydrodynamic and ultrasonic cavitation as the prevalent methods of inducing cavitation. Furthermore, an in-depth study on the main trends regarding the number of publications and citations, keywords co-occurrence and evolution, and countries' publication trends was carried out to investigate the future direction of this research topic. The data was gathered from the Web of Science database and analyzed by the Visualization Of Similarities software. This work focused on: i) publication and citation trends, ii) scientific categories, iii) countries' contribution to the topic of cavitation, iv) prominent journals, v) keyword co-occurrence and cluster analysis, and vi) keyword evolution analysis. Results showed a significant increase in publications during the past 5 years. The scientific categories with the highest number of publications were "environmental sciences" and "environmental engineering," with a combined share of 19.4 % of publications. Keywords evolution analysis showed that limited focus was given to topics related to "energy" and "energy efficiency" in the field of cavitation, but with the rising importance of each process's sustainability, the attention given to these concepts will increase in the future. Future directions for the topic of cavitation-related water and wastewater treatments will shift towards more environmentally friendly applications of hydrodynamic and ultrasonic cavitation as well as towards more green and sustainable approaches to address the increasing water pollution problems and shortage. Moreover, it will include other uses besides water treatment such as manufacturing nanomaterials food production and medicine.

Knowledge sharing and innovation in business organization: A literature review

BACKGROUND: This study is a review of the literature related to the linkage of knowledge sharing and innovation in organizations. This is among the very few studies related to analyzing the extant literature about the relationship between knowledge sharing and innovation to reveal the knowledge gaps and recommend future research directions. OBJECTIVE: The goal of this review of literature is to highlight the most essential concepts that may affect knowledge sharing and innovation, as well as to provide a foundation for future research on knowledge sharing and innovation through bibliometric analysis. The study identifies perhaps the most important popular keywords directly related to knowledge sharing and innovation. METHODS: This study systematically reviews papers on knowledge sharing and innovation extracted from the Web of Science. RESULTS: The study identified the five clusters, each comprising a set of closely related concepts of knowledge sharing and innovation. The first cluster was comprised of the concepts directly revealing the relationship between knowledge sharing and innovation. This stream of research consists of a well developed of research. The other clusters such as knowledge management, innovation performance, mediation processes underlying the relationship between knowledge sharing and innovation also revealed the relationship between knowledge sharing and innovation. IMPLICATIONS: This research has a number of theoretical and practical implications. Theoretically, this study identifies knowledge gaps and new avenues for future research. This study also helps managers and decision makers to identify and examine trends in promoting the linkage between knowledge sharing and innovation.

Mapping Neuroscience in the Field of Education through a Bibliometric Analysis

This study aimed to explore the core knowledge topics and future research trends in neuroscience in the field of education (NIE). In this study, we have explored the diffusion of neuroscience and different neuroscience methods (e.g., electroencephalography, functional magnetic resonance imaging, eye tracking) through and within education fields. A total of 549 existing scholarly articles and 25,886 references on neuroscience in the field of education (NIE) from the Web of Science Core Collection databases were examined during the following two periods: 1995-2013 and 2014-2022. The science mapping software Vosviewer and Bibliometrix were employed for data analysis and visualization of relevant literature. Furthermore, performance analysis, collaboration network analysis, co-citation network analysis, and strategic diagram analysis were conducted to systematically sort out the core knowledge in NIE. The results showed that children and cognitive neuroscience, students and medical education, emotion and empathy, and education and brain are the core intellectual themes of current research in NIE. Curriculum reform and children's skill development have remained central research issues in NIE, and several topics on pediatric research are emerging. The core intellectual themes of NIE revealed in this study can help scholars to better understand NIE, save research time, and explore a new research question. To the best of our knowledge, this study is one of the earliest documents to outline the NIE core intellectual themes and identify the research opportunities emerging in the field.

A comparison study of topic modeling based literature analysis by using full texts and abstracts of scientific articles: a case of COVID-19 research

Purpose

How to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning algorithm to uncover latent thematic structures from large collections of documents, is a widespread approach in literature analysis, especially with the rapid growth of academic literature. In this paper, a comparison of topic modeling based literature analysis has been done using full texts and abstracts of articles.

Design/methodology/approach

The authors conduct a comparison study of topic modeling on full-text paper and corresponding abstract to assess the influence of the different types of documents been used as input for topic modeling. In particular, the authors use the large volumes of COVID-19 research literature as a case study for topic modeling based literature analysis. The authors illustrate the research topics, research trends and topic similarity of COVID-19 research by using Latent Dirichlet allocation (LDA) and topic visualization method.

Findings

The authors found 14 research topics for COVID-19 research. The authors also found that the topic similarity between using full-text paper and corresponding abstract is higher when more documents are analyzed.

Originality/value

First, this study contributes to the literature analysis approach. The comparison study can help us understand the influence of the different types of documents on the results of topic modeling analysis. Second, the authors present an overview of COVID-19 research by summarizing 14 research topics for it. This automated literature analysis can help specialists in the health and medical domain or other people to quickly grasp the structured morphology of the current studies for COVID-19.

Visualizing Research Trends on Culture Neuroscience (2008–2021): A Bibliometric Analysis

Recently, cultural neuroscience has gained attention as a new, important, and interdisciplinary topic in the field of neuroscience. It helps us understand the interaction of cultural and biological factors over the course of life. This study aims to provide a comprehensive overview of the field to readers and potential researchers engaged in cultural neuroscience research. A bibliometric analysis was performed on 113 articles in the field of cultural neuroscience from 2008 to 2021 using data from the core collection of Web of Science. Network visualization software VOSviewer and ITGInsight were used for performance analysis and science mapping. Specifically, the performance analysis included countries, institutions, authors, papers, and journals, while science mapping analyzed the collaboration network, keyword network, bibliographic coupling network, and time series evolution. The results showed that the United States was the most productive country, Northwestern University was the most influential research institution, Chiao Jy was the most influential scholar, and “Social Cognitive and Affective Neuroscience” made the greatest contribution to publishing in the field of cultural neuroscience. Furthermore, collaboration is expected to be the development trend in the future. The key research topics in the field of cultural neuroscience included neuroimaging and psychiatric diseases, theoretical methods, interdisciplinary research, cultural differences (collectivism and individualism), and brain functions. Finally, future research will focus on cultural neuroscience, culture, and self, while adolescence will be the emerging research frontier.

Neglected and Underutilised Crops: A Systematic Review of Their Potential as Food and Herbal Medicinal Crops in South Africa

The African continent harbours many native species with nutraceutical and pharmaceutical potential. This study reviewed underutilised crops in South Africa to determine their potential as food and herbal medicinal crops. Over 5,000 species have been identified and earmarked for their medical attributes in formal and informal setups. Researchers, plant breeders and policymakers have mostly ignored the development potential of these crops. Consequently, their value chains are poorly developed. In South Africa, there is a wide range of neglected and underutilised crops, which were historically popular and used by communities; however, over the years, they have lost their status within farming systems and been relegated to the status of neglected and underutilised. Recently, driven by the need to transition to more sustainable and resilient food systems, there has been renewed interest in their potential as food and herbal medicinal crops to establish new value chains that include vulnerable groups. They are now gaining global attention, and their conservation and sustainable utilisation are now being prioritized. The review confirmed that several of these crops possess nutraceutical and pharmaceutical properties, highlighting their potential for development as food and herbal medicines. However, current production levels are too low to meet the requirements for industrial development; research and development should focus on all aspects of their value chain, from crop improvement to utilisation. A transdisciplinary approach involving a wide range of actors is needed to develop the identified neglected and underutilised crops' potential as food and herbal medicinal crops and support the development of new and inclusive value chains.

An Interactive Scholarly Collaborative Network Based on Academic Relationships and Research Collaborations

In this era of digital transformation, when the amount of scholarly literature is rapidly growing, hundreds of papers are published online daily with regard to different fields, especially in relation to academic subjects. Therefore, it difficult to find an expert/author to collaborate with from a specific research area. This is thought to be one of the most challenging activities in academia, and few people have considered authors’ multi-factors as an enhanced method to find potential collaborators or to identify the expert among them; consequently, this research aims to propose a novel model to improve the process of recommending authors. This is based on the authors’ similarity measurements by extracting their explicit and implicit topics of interest from their academic literature. The proposed model mainly consists of three factors: author-selected keywords, the extraction of a topic’s distribution from their publications, and their publication-based statistics. Furthermore, an enhanced approach for identifying expert authors by extracting evidence of expertise has been proposed based on the topic-modeling principle. Subsequently, an interactive network has been constructed that represents the predicted authors’ collaborative relationship, including the top-k potential collaborators for each individual. Three experiments have been conducted on the collected data; they demonstrated that the most influential factor for accurately recommending a collaborator was the topic’s distribution, which had an accuracy rate of 88.4%. Future work could involve building a heterogeneous co-collaboration network that includes both the authors with their affiliations and computing their similarities. In addition, the recommendations would be improved if potential and real collaborations were combined in a single network.

Trends in global research on industrial parks: A bibliometric analysis from 1996-2019

Industrial parks have been used to promote the economic development of countries. However, its rapid growth has generated environmental problems related to the depletion of natural resources and pollution. Consequently, the network analysis and the bibliometric analysis applied in this research generated qualitative and quantitative information from a systemic perspective on the thematic and community evolution of research on industrial parks (IP) performed to improve its negative environmental impact and reach sustainability. This study used the Web of Science (WoS) database from 1996 – 2019. The main trends and critical research points were identified in four periods of 6-year each. Social network analysis (SNA) was used to identify the intellectual structure main and the academic collaboration networks established among countries/territories, institutions, and authors. The most productive country in articles is currently China (882), however, when we consider the frequency of articles per million inhabitants, it ranks seventh. The WoS database grouped 63.6 ​% of the articles published in the subjects of “Environmental Sciences & Ecology”, “Engineering”, and “Science & Technology - Other Topics”. Industrial Ecology (IE), Industrial Symbiosis (IS), and Circular Economy (CE) were the author keywords with the highest frequency, indicating that IP research has focused from these perspectives to promote the exchange of byproducts and to evaluate the performance and environmental impact of industrial areas through the use of methodologies such as carbon footprints, emergy analysis, and life cycle analysis (LCA). Finally, some themes were identified and proposed for future research based on analyzing research trends and hot spots from the literature review on industrial parks.

Global trends and characteristics of nano- and micro-bubbles research in environmental engineering over the past two decades: A scientometric analysis

The present study has two primary goals, the first goal is to investigate a bibliometric analysis and assess the trends to evaluate the global scientific production of microbubbles and nanobubbles from 2000 to 2020. The aim is to elucidate the cornucopia of benefits the two technologies (micro and nanobubbles) can offer in environmental sciences and environmental amelioration such as wastewater treatment, seed germination, separation processes, etc. The second goal is to explicate the reason behind every chart and trend through environmental engineering perspectives, which can confer value to each analysis. The data was acquired from the Web of Science and was delineated by VOS viewer software and GraphPad Prism. Considering 1034 publications in the area of micro-and nanobubbles, this study was conducted on four major aspects, including publication growth trend, countries contribution assessment, categories, journals and productivity, and keywords co-occurrence network analysis. This article revealed a notable growth in microbubbles and nanobubbles-related publications and a general growth trend in published articles in a 20-year period. China had the most significant collaboration with other countries, followed by the USA and Japan. The most dominant categories for microbubbles were environmental sciences and environmental engineering comprising 22.5% of the total publications, while multidisciplinary subjects such as nanotechnology and nanosciences (8%) were among the dominant categories for nanobubbles. Keyword's analysis results showed that microbubbles had reached the apex since their discovery. Consequently, they are being used mostly in water/wastewater treatment or environmental improvement. On the other hand, nanobubbles are still in their infancy, and their pervasive use is yet to be fully materialized. Most of the publications are still striving to understand the nature of nanobubbles and their stability; however, a critical analysis showed that during the past two years, the trend of using nanobubbles as a cost-effective and environmentally friendly approach has already begun.

Application of Drone Technologies in Surface Water Resources Monitoring and Assessment: A Systematic Review of Progress, Challenges, and Opportunities in the Global South

Accurate and timely information on surface water quality and quantity is critical for various applications, including irrigation agriculture. In-field water quality and quantity data from unmanned aerial vehicle systems (UAVs) could be useful in closing spatial data gaps through the generation of near-real-time, fine resolution, spatially explicit information required for water resources accounting. This study assessed the progress, opportunities, and challenges in mapping and modelling water quality and quantity using data from UAVs. To achieve this research objective, a systematic review was adopted. The results show modest progress in the utility of UAVs, especially in the global south. This could be attributed, in part, to high costs, a lack of relevant skills, and the regulations associated with drone procurement and operational costs. The progress is further compounded by a general lack of research focusing on UAV application in water resources monitoring and assessment. More importantly, the lack of robust and reliable water quantity and quality data needed to parameterise models remains challenging. However, there are opportunities to advance scientific inquiry for water quality and quantity accounting by integrating UAV data and machine learning.

Lessons From the UK's Lockdown: Discourse on Behavioural Science in Times of COVID-19

In recent years behavioural science has quickly become embedded in national level governance. As the contributions of behavioural science to the UK's COVID-19 response policies in early 2020 became apparent, a debate emerged in the British media about its involvement. This served as a unique opportunity to capture public discourse and representation of behavioural science in a fast-track, high-stake context. We aimed at identifying elements which foster and detract from trust and credibility in emergent scientific contributions to policy making. With this in mind, in Study 1 we use corpus linguistics and network analysis to map the narrative around the key behavioural science actors and concepts which were discussed in the 647 news articles extracted from the 15 most read British newspapers over the 12-week period surrounding the first hard UK lockdown of 2020. We report and discuss (1) the salience of key concepts and actors as the debate unfolded, (2) quantified changes in the polarity of the sentiment expressed toward them and their policy application contexts, and (3) patterns of co-occurrence via network analyses. To establish public discourse surrounding identified themes, in Study 2 we investigate how salience and sentiment of key themes and relations to policy were discussed in original Twitter chatter (N = 2,187). In Study 3, we complement these findings with a qualitative analysis of the subset of news articles which contained the most extreme sentiments (N = 111), providing an in-depth perspective of sentiments and discourse developed around keywords, as either promoting or undermining their credibility in, and trust toward behaviourally informed policy. We discuss our findings in light of the integration of behavioural science in national policy making under emergency constraints.