Bibliometric insights into the evolution of uranium contamination reduction research topics: Focus on microbial reduction of uranium

Confronting the threat of environment uranium pollution, decades of research have yielded advanced and significant findings in uranium bioremediation, resulting in the accumulation of tremendous amount of high-quality literature. In this study, we analyzed over 10,000 uranium reduction-related papers published from 1990 to the present in the Web of Science based on bibliometrics, and revealed some critical information on knowledge structure, thematic evolution and additional attention. Methods including contribution comparison, co-occurrence and temporal evolution analysis are applied. The results of the distribution and impact analysis of authors, sources, and journals indicated that the United States is a leader in this field of research and China is on the rise. The top keywords remained stable, primarily focused on chemicals (uranium, iron, plutonium, nitrat, carbon), characters (divers, surfac, speciat), and microbiology (microbial commun, cytochrome, extracellular polymeric subst). Keywords related to new strains, reduction mechanisms and product characteristics demonstrated the strongest uptrend, while some keywords related to mechanism and performance were clearly emerging in the past 5 years. Furthermore, the evolution of the thematic progression can be categorized into three stages, commencing with the discovery of the enzymatic reduction of hexavalent uranium to tetravalent uranium, developing in the groundwater remediation process at uranium-contaminated sites, and delving into the research on microbial reduction mechanisms of uranium. For future research, enhancing the understanding of mechanisms, improving uranium removal performance, and exploring practical applications can be considered. This study provides unique insights into microbial uranium reduction research, providing valuable references for related studies in this field.

System's Crisis Resilience as a Societal Crisis: Knowledge Structure and Gaze of the Finnish Health Care System

The crisis resilience of vital social systems is currently the target of constant development efforts in Finland, as their drifting into crisis would weaken societies' functional abilities, safety, and security. This is also the case regarding the Finnish health care system. In an attempt to move beyond existing frameworks of crisis imagination, this article takes an unconventional stance by elucidating endogenous crisis dynamics present in the Finnish health care system. Delphi process was conducted for top experts in Finnish health care and crisis management. With a dissensus-seeking orientation, our aim was to fertilize disagreements among panelists to reveal key vulnerabilities in the health system. Despite our efforts to evoke dissensus, the panelists ended up generating a consensus that aims to protect the underlying assumptions of the health system's knowledge structure. Through inductive analysis of expert discourses, the data was analyzed through our research question "what constitutes a crisis-proof health system and a crisis-prone health system". What is framed as a strength of the system by our panelists, namely the ability to maintain legitimacy, improve efficiency, and guarantee continuity, can still have questionable implications that are left ungrasped. A system's theory approach illustrates how such effects can develop and escalate beyond the reach of social interventions, and thus be predisposed to cause objectionable yet concealed social crises. The discussion illustrates how these endogenous crisis dynamics could be seen to materialize in real-life cases.

Knowledge mapping of spastic cerebral palsy. A bibliometric analysis of global research (2000-2022)

BACKGROUND

Cerebral palsy (CP) is characterized by abnormal pronunciation, posture, and movement. Spastic CP accounts for more than 70% of all CP. To date, there has been no bibliometric analysis to summarize study on spastic CP. Here, we aim to conduct a bibliometric analysis of spastic CP to summarize this field's knowledge structure, research hotspots, and frontiers.

METHOD

Publications about spastic CP were searched utilizing the Web of Science Core Collection (WoSCC) database from 1 January 2000 to 30 November 2022, the WoSCC literature analysis wire, VOSviewer 1.6.18, CiteSpace 6.1.R4 and Online analysis platform for bibliometrics were used to conduct the analysis.

RESULTS

A total of 3988 publications, consisting of 3699 articles and 289 reviews, were included in our study. The United States emerged as the most productive country, while Kathleen Univ Leuven was the most productive institution. The leading author was Desloovere K. A total of 238 journals contributed to this field, with Developmental medicine and child neurology being the leading journal. Important keywords and keyword clusters included Spastic cerebral palsy, Reliability, and Gross motor function. Keywords identified through burst detection indicated that hotspots in this field were management, randomized controlled trials, and definition.

CONCLUSION

Based on the analysis of bibliometric on spastic CP over the past 20 years, the trends and the knowledge graph of the countries, institutions, authors, references, and the keywords have been identified, providing accurate and expedited insights into critical information and potentially new directions in the study of spastic CP.

Comprehensive analysis of research related to rehabilitation and COVID-19, hotspots, mapping, thematic evolution, trending topics, and future directions

BACKGROUND

This study conducted a comprehensive analysis of research pertaining to the intersection of rehabilitation and COVID-19 (COV-REH). The main aim of this study is to analyze the thematic progression and hotspots, detect emerging topics, and suggest possible future research directions in the COV-REH.

METHODS

Appropriate keywords were selected based on the Medical Subject Headings (MeSH) PubMed database and the Scopus database were used to retrieve a total of 3746 original studies conducted in the English language. The data extraction was performed on June 30, 2023. VOSviewer and Bibliometrix utilize CVS and BibTex files to facilitate the performance analysis and generate visual maps. The performance indicators reported for the research components of the COV-REH were compiled using the Scopus Analytics tool.

RESULTS

From 2003 to 2023, 3470 authors from 160 organizations in 119 countries generated 3764 original research documents, with an annual growth of 53.73%. 1467 sources identified these scholarly works. Vitacca, M. (Italy), Harvard University (USA), and the USA published the most articles. This study included 54.1% of medical scholars. Telerehabilitation, exercise, quality of life, case reports, anxiety, and pulmonary rehabilitation were the primary themes of the COV-REH. One component of "telerehabilitation" is now the cardiac rehabilitation cluster. The trending topics in COV-REH are "symptoms," "protocol," and "community-based rehabilitation".

CONCLUSIONS

This study proposed several significant research directions based on the current thematic map and its evolution. Given that COV-REH investigations have been determined to be multidisciplinary, this study contributes conceptually to several fields and has wide-ranging implications for practitioners and policymakers.

Investigating online learners' knowledge structure patterns by concept maps: A clustering analysis approach

A deep understanding of the learning level of online learners is a critical factor in promoting the success of online learning. Using knowledge structures as a way to understand learning can help analyze online students' learning levels. The study used concept maps and clustering analysis to investigate online learners' knowledge structures in a flipped classroom's online learning environment. Concept maps (n = 359) constructed by 36 students during one semester (11 weeks) through the online learning platform were collected as analysis objects of learners' knowledge structures. Clustering analysis was used to identify online learners' knowledge structure patterns and learner types, and a non-parametric test was used to analyze the differences in learning achievement among learner types. The results showed that (1) there were three online learners' knowledge structure patterns of increasing complexity, namely, spoke, small-network, and large-network patterns. Moreover, online learners with novice status mostly had spoke patterns in the context of flipped classrooms' online learning. (2) Two types of online learners were found to have different distributions of knowledge structure patterns, and the complex knowledge structure type of learners exhibited better learning achievement. The study explored a new way for educators to analyze knowledge structures by data mining automatically. The findings provide evidence in the online learning context for the relationship between complex knowledge structures and better learning achievement while suggesting the existence of inadequate knowledge preparedness for flipped classroom learners without a special instructional design.

Medication safety education in nursing research: Text network analysis and topic modeling

OBJECTIVES

This study aimed to identify the knowledge structure of medication safety nursing education literature by developing schematic diagrams of the relationship between keywords from a macro perspective. This study also identifies the research topics and trends over time.

DESIGN

This quantitative content study used text network analysis to explore keywords and research topics using topic modeling within the medication safety nursing education literature.

DATA SOURCES

PubMed, EMBASE, and Cochrane databases were used to search for the medication safety nursing education literature published until December 2021.

METHODS

Keywords from 2085 articles were examined using text network analysis and topic modeling with NetMiner 4.4.3.

RESULTS

The keywords with the most frequency and the highest networking degree in centrality were "patient," "medication," "program," "nurse," and "care." The emerging keywords assessed by time periods were identified; the first phase ("heart failure," "insulin," "chemotherapy," and "infusion"), the second phase ("medication errors," "staff," and "information"), the third phase ("program," "management," and "data"). The results of topic modeling were as follows: safe medication administration, safe medication reconciliation process, medication education for patients, medication errors in nursing practice, and multidisciplinary teamwork for medication safety.

CONCLUSION

These findings will help nursing researchers and educators to understand the trends and insights for medication safety education and educate future nurses to provide safer nursing care.

Analysis of the structure of scientific knowledge on wine tourism: A bibliometric analysis

This research offers a bibliometric analysis of 588 publications on wine tourism published between 1998 and 2021, highlighting the years of publication, the publication format, the fields of research, the authors, the institutions, the main journals and the country of origin of both the authors and the scientific production analyzed. To our knowledge, no bibliometric study has focused on the study of wine tourism, so this study aims to fill this research gap, serving as a reference guide for both academics and professionals in the wine sector who want to know in depth how the study of this type of tourism has been approached. The results of the research show that the academic study of wine tourism dates back to the end of the 1990s, with the New World countries standing out in its study, especially Australia as the country with the highest scientific production and the largest number of academics focused on the study of this type of tourism. The study allows us to highlight the value of academic articles as the main means of disseminating research results, these being focused on tourism, business, economic and cross-cutting research. The results reveal relevant conclusions for academics, winemakers and tourism managers on the knowledge structure of wine tourism activity.

Clinical reasoning pattern used in oral health problem solving - A case study in Indonesian undergraduate dental students

BACKGROUND

Health professionals are known to use various combinations of knowledge and skills, such as critical thinking, clinical reasoning, clinical judgment, problem-solving, and decision-making, in conducting clinical practice. Clinical reasoning development is influenced by knowledge and experience, the more knowledge and experience, the more sophisticated clinical reasoning will be. However, clinical reasoning research in dentistry shows varying results .

AIMS

This study aims to observe the clinical reasoning pattern of undergraduate dental students when solving oral health problems, and their accordance with their knowledge acquisition.

MATERIAL AND METHODS

This qualitative study employed the think-aloud method and the result was assessed through verbal protocol analyses. Five respondents from final year dental undergraduate students were agreed to participate. A unique hypothetical clinical scenario was used as a trigger. The audio data were transcribed, interpreted, and categorized as a clinical reasoning pattern; and the concept maps created were assessed by a Structure of Learning Outcomes (SOLO) taxonomy as knowledge acquisition.

RESULTS

Observations on clinical reasoning patterns and the level of knowledge acquisition in five undergraduate dental students showed varying results. They applied clinical reasoning patterns according to their knowledge acquisition during didactical phase. Learners with inadequate knowledge relied on guessing, meanwhile learners with adequate knowledge applied more sophisticated reasoning pattern when solving problems.

CONCLUSIONS

Various problem-solving strategies were encountered in this study, which corresponded to the level of knowledge acquisition. Dental institutions must set minimum standards regarding the acquisition of conceptual knowledge accompanied by improvement of clinical reasoning skills, as well as refinement of knowledge and procedural skills.

Uncover the reasons for performance differences between measurement functions (Provably)

Recently, an exciting experimental conclusion in Li et al. (Knowl Inf Syst 62(2):611-637, 1) about measures of uncertainty for knowledge bases has attracted great research interest for many scholars. However, these efforts lack solid theoretical interpretations for the experimental conclusion. The main limitation of their research is that the final experimental conclusions are only derived from experiments on three datasets, which makes it still unknown whether the conclusion is universal. In our work, we first review the mathematical theories, definitions, and tools for measuring the uncertainty of knowledge bases. Then, we provide a series of rigorous theoretical proofs to reveal the reasons for the superiority of using the knowledge amount of knowledge structure to measure the uncertainty of the knowledge bases. Combining with experiment results, we verify that knowledge amount has much better performance for measuring uncertainty of knowledge bases. Hence, we prove an empirical conclusion established through experiments from a mathematical point of view. In addition, we find that for some knowledge bases that cannot be classified by entity attributes, such as ProBase (a probabilistic taxonomy), our conclusion is still applicable. Therefore, our conclusions have a certain degree of universality and interpretability and provide a theoretical basis for measuring the uncertainty of many different types of knowledge bases, and the findings of this study have a number of important implications for future practice.

Mapping the field of bioenergy with carbon capture and storage (BECCS): scientific cooperation and co-citation analyses

Bioenergy with carbon capture and storage (BECCS), as the most scalable negative emission technology, can limit global warming to 1.5 ℃ under climate change scenarios. With increasing research on BECCS, concerns have been raised about its deployment and impacts. In view of the limited research on the possible structure and collaboration in the field of BECCS, this study sought to determine the scientific cooperation and knowledge structure using bibliometric approaches based on a science mapping analysis. Co-authorship and co-citation networks were developed from CiteSpace to explore the individual, institutional, and national collaborations, and detect the knowledge structure in the field of BECCS. Six key research groups with connections were found with the research group centered on NIALL MAC DOWELL and PETE SMITH being more focused on BECCS. Cluster analysis results show that the knowledge structure of BECCS has gradually formed. The research field has been continuously developed and relatively independent. The findings provide researchers with an in-depth understanding of the current state of BECCS research and its knowledge structure.

Visualizing the knowledge structure and evolution of bioinformatics

Background

Bioinformatics has gained much attention as a fast growing interdisciplinary field. Several attempts have been conducted to explore the field of bioinformatics by bibliometric analysis, however, such works did not elucidate the role of visualization in analysis, nor focus on the relationship between sub-topics of bioinformatics.

Results

First, the hotspot of bioinformatics has moderately shifted from traditional molecular biology to omics research, and the computational method has also shifted from mathematical model to data mining and machine learning. Second, DNA-related topics are bridge topics in bioinformatics research. These topics gradually connect various sub-topics that are relatively independent at first. Third, only a small part of topics we have obtained involves a number of computational methods, and the other topics focus more on biological aspects. Fourth, the proportion of computing-related topics hit a trough in the 1980s. During this period, the use of traditional calculation methods such as mathematical model declined in a large proportion while the new calculation methods such as machine learning have not been applied in a large scale. This proportion began to increase gradually after the 1990s. Fifth, although the proportion of computing-related topics is only slightly higher than the original, the connection between other topics and computing-related topics has become closer, which means the support of computational methods is becoming increasingly important for the research of bioinformatics.

Conclusions

The results of our analysis imply that research on bioinformatics is becoming more diversified and the ranking of computational methods in bioinformatics research is also gradually improving.

Bibliometric analysis of microbial sulfonamide degradation: Development, hotspots and trend directions

Microbial sulfonamide degradation (MSD) is an efficient and safe treatment in both natural and engineered ecosystems. In order to systematically understand the research status and frontier trends of MSD, this study employed CiteSpace to conduct a bibliometric analysis of data from the Web of Science (WoS) and the China National Knowledge Infrastructure (CNKI) published from 2000 to 2021. During this time, China, Germany, Spain, the United States and Australia played leading roles by producing numerous high impact publications, while the Chinese Academy of Sciences was the leading research institution in this interdisciplinary research category. The Chemosphere was the top journal in terms of the number of citations. MSD research has gradually progressed from basic laboratory-based experiments to more complex environmental microbial communities and finally to deeper research on molecular mechanisms and engineering applications. Although multi-omics and synthetic community are the key techniques in the frontier research, they are also the current challenges in this field. A summary of published articles shows that Proteobacteria, Gammaproteobacteria, Burkholderiales and Alcaligenaceae are the most frequently observed MSD phylum, class, order and family, respectively, while Bacillus, Pseudomonas and Achromobacter are the top three MSD genera. To our knowledge, this study is the first to investigate the development and current challenges of MSD research, put forward future perspective, and form a relatively complete list of sulfonamide-degrading microorganisms for reference.

An exploration of the knowledge structure in studies on old people physical activities in Journal of Exercise Rehabilitation: by semantic network analysis

Physical activity, a key component of maintaining health, is becoming an essential alternative in reducing medical expenses for the old people. This research was intended to analyze 51 research papers published in the Journal of Exercise Rehabilitation (JER) through semantic network analysis. The subjects of the study were the keywords that the authors of each paper used in journal search sites from 2013 to 2019. The present researcher analyzed the frequency, density, and centrality of the keywords of the indicators through semantic network analysis and then visualized them on the basis of findings using UCINET6 and the NetDraw program. Also, the researcher classified the hidden clusters by CONCOR (Convergence of iterated Correlations) analysis, which is a kind of cluster analysis. As a result, it was found that the keyword with the highest frequency was “exercise,” followed by “cognition, “physicalactivity,” “old-women,” “Korean,” “fall,” and “training.” It was also found that most of the high-frequency keywords, such as “exercise,” “cognition,” “old-women,” “program” and “depression” had high centrality. These keywords were classified into four clusters: (a) mental health research, (b) physical health research, (c) social behavior research, and (d) leisure efficacy research. This suggests that the old people-related research papers published in the JER have derived effective methods of maintaining physical and mental health using scientific exercise programs, and especially address the effects of exercise intervention for old women.

Visualization and analysis of mapping knowledge domain of oxidation studies of sulfide ores

The oxidation of sulfide ores is a common phenomenon. To better understand the current development and status of oxidation studies of sulfide ores (OSSO), a bibliometric analysis of OSSO was conducted by mapping the spatiotemporal distribution of the knowledge domain and the research focus using VOSviewer and Citespace tools. The data were derived from the Web of Science (WOS) core collection database from 2000 to 2018. Study emphases covered publication outputs, countries/regions, organizations, top journals, research focus and keyword co-occurrence network, and theme development. The results include the following findings: (1) The line of the 3-year moving average of publications (3-year MAP), h-index (3-year MAH), and authors (3-year MAA) increased from 2001 to 2018. Conversely, the h-index continuously declined. (2) Asia had the most publications, with 1052, followed by Europe, with 923, and America, with 767. China, the USA, and Australia are the most active countries. (3) The top 10 organizations with the most publications are five Chinese organizations and one organization from each of the following countries: Russia, Australia, France, and India. (4) Hydrometallurgy, Minerals Engineering, Ore Geology Reviews, Geochimica Et Cosmochimica Acta, and Economic Geology are among the top 10 journals that researchers are most concerned about. (5) Cooperation among different organization or different countries is the most effective way to produce the most influential papers. (6) The OSSO is still focused on the process of oxidation by using different methods and techniques. In future work, it is necessary to progress new methods to understand the process of self-heating and prevent spontaneous combustion disaster of sulfide ore which result from OSSO.

Visualizing the knowledge structure and evolution of big data research in healthcare informatics

BACKGROUND

In recent years, the literature associated with healthcare big data has grown rapidly, but few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of the healthcare big data field.

METHODS

To explore the foundational knowledge and research hotspots of big data research in the field of healthcare informatics, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the trend of each paper's co-author number, the distribution of core institutions and countries, the core literature distribution, the related information of prolific authors and innovation paths in the field, a keyword co-occurrence analysis, and research hotspots and trends for the future.

RESULTS

By conducting a literature content analysis and structure analysis, we found the following: (a) In the early stage, researchers from the United States, the People's Republic of China, the United Kingdom, and Germany made the most contributions to the literature associated with healthcare big data research and the innovation path in this field. (b) The innovation path in healthcare big data consists of three stages: the disease early detection, diagnosis, treatment, and prognosis phase, the life and health promotion phase, and the nursing phase. (c) Research hotspots are mainly concentrated in three dimensions: the disease dimension (e.g., epidemiology, breast cancer, obesity, and diabetes), the technical dimension (e.g., data mining and machine learning), and the health service dimension (e.g., customized service and elderly nursing).

CONCLUSION

This study will provide scholars in the healthcare informatics community with panoramic knowledge of healthcare big data research, as well as research hotspots and future research directions.

Quantitative mapping of scientific research-The case of electrical conducting polymer nanocomposite

This study aims to understand knowledge structure both quantitatively and visually by integrating keyword analysis and social network analysis of scientific papers. The methodology proposed in this study is capable of creating a three-dimensional "Research focus parallelship network" and a "Keyword Co-occurrence Network", together with a two-dimensional knowledge map. The network and knowledge map can be depicted differently by choosing different information for the network actor, i.e. country, institute, paper and keyword, to reflect knowledge structures from macro, to meso, to micro-levels. A total of 223 highly cited papers published by 142 institutes and 26 countries are analyzed in this study. China and the US are the two countries located at the core of knowledge structure and China is ranked no. 1. This quantitative exploration provides a way to unveil important or emerging components in scientific development and also to visualize knowledge; thus an objective evaluation of scientific research is possible for quantitative technology management.