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In the development of physical theories, thought experiments play a crucial role. Research on this topic began in 1976 and has continued to the present. This study aims to provide a more complete picture of the progress of thought experiments over the past two decades. To achieve this, this study employs bibliometric mapping methods. A total of 679 published papers were analyzed, including articles (504), conference papers (92), and book chapters (83). This data was retrieved from the Scopus database. The study's findings reveal that research and publications on thought experiments are highly valued and have received significant attention over the past eight years. According to the findings, 90% of the top 20 source titles contributing to thought experiments are from journals in the first and second quartiles (Q1 and Q2). This quartile ranking shows the quality and significant influence of a journal. The geographical distribution indicates that the United States contributes the most to thought experiments research, with 213 documents, 2592 citations, and 47 links. We also identified several prospective keywords that could be the focus of future research, including artificial intelligence, physics education, fiction, God, theology, productive imagination, technology, speculative design, and critical design. Therefore, this study provides a thorough picture of thought experiment research trends and future directions of potential topics that can be the focus of future researchers.

Background: In previous decades, large-scale research has been carried out on bacterial meningitis. In every field, citation analysis is the most significant contribution. The study's objective was to identify and analyze the 100 articles on bacterial meningitis that received the most citations between 2000 and 2023, highlighting the most significant developments in the field. Objective: The objective of this study was to find out what makes a highly influential article by identifying and analyzing the characteristics of the 100 articles in the field of bacterial meningitis that receive the most citations. The goal of this study was to find and examine the 100 articles on bacterial meningitis that received the most citations. Methodology: We identified the top 100 most-cited papers in the field of bacterial meningitis from 55 journals using the Dimensions AI database. The results of each author's analysis of 100 articles were then compared. We gathered fundamental data such as the journal's title, country of publication, and study type. Descriptive counts or percentages were used to compare the various categories. Results: Between the year 2000 and the year 2023, articles were published. The total number of citations ranged from 115 to 1176, with 42 papers receiving more than 200 citations. In 2008, 14 articles were published, followed by 10 in 2000 and 2007. One thousand one hundred and seventy-six times were given to the most-cited paper, whereas 115 times were given to the least-cited article. "Clinical Features and Prognostic Factors in Adults with Bacterial Meningitis," by Diederik van de Beek, et al. (2004) was the article that received the most citations. 1176 people have cited this article. van de Beek Diederik of the Academic Medical Center in The Netherlands is the author who has written the most articles, was mentioned in 14 of the top 100 articles. Papers were primarily published in Pediatrics (n = 9) publication with 1861 citations. The Netherlands came in second with 18 publications, followed by the United States (n = 46). Conclusion: Our study uses bibliometrics and visualization analysis of the most important articles in this field to show the current state of research in the area of bacterial meningitis, provide a history of research trends, and offer a perspective for future bacterial predicts the growth of meningitis. [ABSTRACT FROM AUTHOR]

The academic community has witnessed a notable increase in paper publications, whereby the rapid pace at which modern society seeks information underscores the critical need for literature mapping. This study introduces an innovative automatic model for categorizing articles by subject matter using Machine Learning (ML) algorithms for classification and category labeling, alongside a proposed ranking method called SSS (Scientific Significance Score) and using Z-score to select the finest papers. This paper's use case concerns industry productivity. The key findings include the following: (1) The Decision Tree model demonstrated superior performance with an accuracy rate of 75% in classifying articles within the productivity and industry theme. (2) Through a ranking methodology based on citation count and publication date, it identified the finest papers. (3) Recent publications with higher citation counts achieved better scores. (4) The model's sensitivity to outliers underscores the importance of addressing database imbalances, necessitating caution during training by excluding biased categories. These findings not only advance the utilization of ML models for paper classification but also lay a foundation for further research into productivity within the industry, exploring themes such as artificial intelligence, efficiency, industry 4.0, innovation, and sustainability. [ABSTRACT FROM AUTHOR]

The notable increase in retraction papers has attracted considerable attention from diverse stakeholders. Various sources are now offering information related to research integrity, including concerns voiced on social media, disclosed lists of paper mills, and retraction notices accessible through journal websites. However, despite the availability of such resources, there remains a lack of a unified platform to consolidate this information, thereby hindering efficient searching and cross-referencing. Thus, it is imperative to develop a comprehensive platform for retracted papers and related concerns. This article aims to introduce "Amend," a platform designed to integrate information on research integrity from diverse sources. The Amend platform consolidates concerns and lists of problematic articles sourced from social media platforms (e.g., PubPeer, For Better Science), retraction notices from journal websites, and citation databases (e.g., Web of Science, CrossRef). Moreover, Amend includes investigation and punishment announcements released by administrative agencies (e.g., NSFC, MOE, MOST, CAS). Each related paper is marked and can be traced back to its information source via a provided link. Furthermore, the Amend database incorporates various attributes of retracted articles, including citation topics, funding details, open access status, and more. The reasons for retraction are identified and classified as either academic misconduct or honest errors, with detailed subcategories provided for further clarity. Within the Amend platform, a total of 32,515 retracted papers indexed in SCI, SSCI, and ESCI between 1980 and 2023 were identified. Of these, 26,620 (81.87%) were associated with academic misconduct. The retraction rate stands at 6.64 per 10,000 articles. Notably, the retraction rate for non-gold open access articles significantly differs from that for gold open access articles, with this disparity progressively widening over the years. Furthermore, the reasons for retractions have shifted from traditional individual behaviors like falsification, fabrication, plagiarism, and duplication to more organized large-scale fraudulent practices, including Paper Mills, Fake Peer-review, and Artificial Intelligence Generated Content (AIGC). The Amend platform may not fully capture all retracted and concerning papers, thereby impacting its comprehensiveness. Additionally, inaccuracies in retraction notices may lead to errors in tagged reasons. Amend provides an integrated platform for stakeholders to enhance monitoring, analysis, and research on academic misconduct issues. Ultimately, the Amend database can contribute to upholding scientific integrity. This study introduces a globally integrated platform for retracted and concerning papers, along with a preliminary analysis of the evolutionary trends in retracted papers. [ABSTRACT FROM AUTHOR]

Besides scientific impact, papers can also achieve a technological impact that remains less known in the scientific community. Cited papers in the patents are considered as the index to measure the technological impact. This study aimed to analyze the technological impact of Iranian publications using co-authorship and coword map, their evolution, the journals, and the subject areas of these publications. This applied research focuses on the quantitative study and visualization with a scientometric approach. The research population was all studies (4554 records) that were published during 2011-2020 in one of the Iranian institutions and had been cited by one of the international patents. The data collection tool was the SciVal database. CiteSpace and Excel spreadsheets were used to analyze the data. Of the 4,554 papers cited by the scholarly outputs that have been cited in patents e patents, most of them were published in collaboration with the USA (9%). Islamic Azad University and Tehran University of Medical Sciences (13% each) were the most active Iranian universities. The number of Iranian papers cited in patents had a downward trend from 686 in 2011 to 57 in 2020. RSC Advances journal was the first top journal to publish these papers. Of 27 subject areas, engineering (24.1%) was the first popular subject that patents cite in their publications. The cluster analysis of keywords identified 8 clusters, including "x-ray diffraction," "animal," "adult," "escherichia coli," "tissue engineering," "coronavirus infection," "neural network," and "methane." The technological impact of Iranian research has declined in recent years. It is suggested that research policymakers should consider scholarly outputs that have been cited in patents, which, in a way, shows the flow of knowledge to the industry and encourages researchers to produce such papers. [ABSTRACT FROM AUTHOR]

The present study was conducted to draw the co-authorship network of hot papers of science citation index in the Web of Science (WOS) database from 2020 to 2021. This investigation is a descriptive study using a scientometrics approach. This research was conducted using social network analysis indices to visualize the coauthorship networks of hot papers in the science citation index. The structure of the co-authorship network of researchers of hot papers in the field of science consists of 47,045 authors who have contributed to the publication of 3475 hot papers from 2020 to 2021, which indicates the high co-authorship of these authors. Moreover, it was found that among the co-authorship patterns of these researchers, the most significant number of articles during the studied years was related to the five-author collaborations. Moreover, the average Collaboration Coefficient (CC) of the authors of hot papers was higher than 0.80, indicating the authors' strong tendency to produce joint articles. The high collaboration of the authors of hot papers in the science citation index can be one of the reasons for increasing the level of visibility and the potential for using them. [ABSTRACT FROM AUTHOR]

This paper investigates the integration of blockchain technology into core systems within institutions of higher education, utilizing the National Institute of Standards and Technology's (NIST) Cybersecurity Framework as a guiding framework. It supplies definitions of key terminology including blockchain, consensus mechanisms, decentralized identity, and smart contracts, and examines the application of secure blockchain across various educational functions such as enrollment management, degree auditing, and award processing. Each facet of the NIST Framework is utilized to explore the integration of blockchain technology and address persistent security concerns. The paper contributes to the literature by defining blockchain technology applications and opportunities within the education sector.

Silicon (Si) is a chemical element that is not yet considered essential for plants. However, over the past few decades, an increasing number of scientifi c studies have focused on the role of Si in soil-plant interactions. At the same time, many countries are leading the development and use of silicon-based fertilizers. Si can be taken up by plants predominantly in a mobile form (H4SiO4) in the soil through both passive and active uptake mechanisms. Therefore, Scopus data from 2012 to 2022 were used to understand the implementation and current status of research on the benefi cial effects of silicon fertilizers on soil and plants and the role of soil forms of silicon in improving soil quality. A bibliometric study of articles published in the database on the role of Si in soil and silicon fertilizers was carried out. Various tools, such as Microsoft Offi ce Excel 2021, VOS Viewer and Mapchart.net were used in this study. The fi nal literature includes 440 articles, 82% of which are scientifi c. Over the past decade, the number of published articles has increased signifi cantly. For example, in the years 2021–2022, a total of 91 articles were published, which is six times higher than the number of articles published in 2012–2013 (15 articles). This signifi cant increase in publications highlights the growing interest in the role Si and Si fertilizer research. It was found around 32.04% of China, 13.2% of the USA and 9.8% of Brazil the reviewed publications focused on the role of Si and Si fertilizers studies. Among the authors who published the most articles on this topic during the selected period are Wang X. (with 12 articles), Liang Yu., Rizwan M. and Ali S. (each with 8 articles). [ABSTRACT FROM AUTHOR]

The convective hot-air drying technology can cause physicochemical, nutritional, and organoleptic losses in the mango (Mangifera indica). The present Systematic Review was carried out with the objective of comparing mango dehydration technologies to identify the effects on the physicochemical, nutritional, and organoleptic properties of the fruit. Through a review of published scientific and conference papers in the Scopus database, adjusted to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, a total of 134 documents dated between 2000 and December 6 of 2022 were obtained; 76 of these documents were finally included in the bibliographic and theoretical analysis. Selection parameters emphasizing the relationship between the articles and the research topic, evidenced by including at least one of three dehydration technologies and the fruit of interest with an experimental or theoretical approach to the dehydration subject; review articles and surveys were excluded. Correlation graphs of bibliographic variables were made using the data mining software VantagePoint (version 15.1), which was graphically restructured in Microsoft Excel with the support of statistical analysis. Of the resulting articles, it was found that the countries with authors who participated most in scientific production like India, Brazil, Colombia, the United States, and Thailand, were those related to mango production or importation. Furthermore, the freeze-drying technology allows operating at lower temperatures than convective hot-air drying, contributing to the preservation of ascorbic acid, among other compounds. The refractance window has the shortest operation time to obtain moisture values between 10 and 20%. The dehydrated samples using the refractance window are smooth, homogeneous, non-porous, and comparable to the color obtained with freeze-drying, which is acceptable for industrial applications. [ABSTRACT FROM AUTHOR]

Background: Natural language processing, such as ChatGPT, demonstrates growing potential across numerous research scenarios, also raising interest in its applications in public health and epidemiology. Here, we applied a bibliometric analysis for a systematic assessment of the current literature related to the applications of ChatGPT in epidemiology and public health. Methods: A bibliometric analysis was conducted on the Biblioshiny web-app, by collecting original articles indexed in the Scopus database between 2010 and 2023. Results: On a total of 3431 original medical articles, "Article" and "Conference paper", mostly constituting the total of retrieved documents, highlighting that the term "ChatGPT" becomes an interesting topic from 2023. The annual publications escalated from 39 in 2010 to 719 in 2023, with an average annual growth rate of 25.1%. In terms of country production over time, the USA led with the highest overall production from 2010 to 2023. Concerning citations, the most frequently cited countries were the USA, UK, and China. Interestingly, Harvard Medical School emerges as the leading contributor, accounting for 18% of all articles among the top ten affiliations. Conclusions: Our study provides an overall examination of the existing research interest in ChatGPT's applications for public health by outlining pivotal themes and uncovering emerging trends. [ABSTRACT FROM AUTHOR]

This study aims to explore the implementation of scorecards and dashboards in assessing internationalization activities in universities. The methodology of this concept paper is referencing secondary data comprising established academic databases from Scopus, Web of Science (WOS), and Google Scholar, focusing on aspects related to the benefits, monitoring, and effectiveness of scorecards and dashboards in evaluating an internationalization process or activity carried out by a university. The improvement of a university's reputation and ranking should be based on several key factors such as academic reputation, employer reputation, faculty/student ratio, citations per faculty, international faculty ratio, and international student ratio. These are the same criteria considered by institutions such as Quacquarelli Symonds (QS) and Times Higher Education (THE), which rank and rate universities and institutions of higher education around the world. This study found that implementing scorecards and dashboards in evaluating a university to improve its reputation and ranking is indeed effective. This is due to various factors, including the benefits, effectiveness, and accurate measurement offered by the scorecards and dashboards and their widespread use in universities. [ABSTRACT FROM AUTHOR]

Theoretical framework is one of the main parts of the research paper, so that researchers are expected to be able to determine the relevant theory that underlies their research. The purpose of conducting this systematic literature review (SLR) is to review of theoretical framework, compare, and describe various theoretical research frameworks in an investigative manner on the theme of self efficacy in collaborative science learning that underlies publications in Scopus indexed journals in the last ten years. In this regard, we used the phrase "self-efficacy in collaborative science learning" on the disbursement menu in the Scopus database and found 711 articles. There were 63 articles that met the criteria for analysis. The inclusion and exclusion model used is PRISMA. The newly discovered aspects consist of community of practice, professional learning community, and reflection, which are as a result of the development of a theory of change and a theory of instruction constructed from theoretical frameworks in the recent collaborative science learning practices literature. Therefore, the development of this theory can be considered as a theoretical basis for developing the self-efficacy of prospective science teachers in collaborative learning in the future. The purpose of conducting this systematic literature review (SLR) is to review, compare, and describe various theoretical research frameworks in an investigative manner on the theme of self-efficacy in collaborative science learning that underlies publications in Scopus indexed journals in the last ten years. In this regard, we used the phrase "self-efficacy in collaborative science learning" on the disbursement menu in the Scopus database and found 711 articles. There were 63 articles that met the criteria for analysis. The inclusion and exclusion model used is PRISMA. The newly discovered aspects consist of community of practice, professional learning community, and reflection, which are as a result of the development of a theory of change and a theory of instruction constructed from theoretical frameworks in the recent collaborative science learning practices literature. Therefore, the development of this theory can be considered as a theoretical basis for developing the self-efficacy of prospective science teachers in collaborative learning in the future.

The question of how agricultural knowledge and innovation systems (AKISs) can address the issue of sustainable management of natural resources (SMNR) is presented in this conference paper. This literature review, which collected published research from the Scopus electronic database, aimed to explore the value of AKISs in enhancing the sustainability of natural resources. Therefore, it examined and evaluated the roles of AKISs as either positive or negative overall. Moreover, it analyzed whether the use of AKISs supports the goal of creating a sustainable system that links agriculture with natural resources. Among its findings, this review presents the positive and negative outcomes of each element and potential future scenarios/suggestions if the current trends persist. [ABSTRACT FROM AUTHOR]

The concept of intrinsic capacity (IC) revolves around healthy aging and active aging. Since the Introduction of the concept by the World Health Organization in 2015, a series of studies have been conducted by scholars from multiple fields. However, no bibliometric analysis has systematically investigated this issue. We aim to identify the current landscape and frontier trends of scientific achievements on IC in older adults through bibliometric approaches. Methods: Quantitative analysis of publications relating to IC in older adults from 2015 to 2023 was interpreted and graphed through the Web of Science Core Collection database on December 5, 2023. A variety of quantitative variables was analyzed, including publication and citation counts, H-index, and journal citation reports. Co-authorship, citation, co-citation, and co-occurrence analyses were performed for countries/regions, institutions, authors, and keywords using the VOSviewer and CiteSpace. Results: A total of 952 original and review articles in English were identified. The European countries possessed an absolute advantage in this field. The most contributive institution was the University of São Paulo. The most productive author is Cesari Matteo from France, followed by Qaisar Rizwan from the United Arab Emirates. However, a relatively low level of research cooperation existed between institutions and authors. Important topics mainly include the connotations, theoretical framework models, evaluation, screening tools, and application scenarios of IC. Among the promising hotspots, "biological aging", "ICOPE", "Covid-19", "prevention", "inflammation", "caf22", "prevalence", and "randomized controlled trial" displayed relatively latest average appearing year. Conclusion: Global trends indicate a growing scientific output on IC in older adults, and developed countries are leading the way. There is still room for improvement in research team collaboration. The focus gradually shifts from theoretical research to empirical research. It is recommended to pay attention to the latest hot spots, such as "biological aging", "ICOPE implementation", "post-COVID-19 syndrome", and "biomarkers". [ABSTRACT FROM AUTHOR]

This review paper facilitates the examination of the comprehensive thought patterns within electric vehicles (EVs) technologies and elucidates the primary significance derived from recent research. Furthermore, it systematically identifies and explores key themes related to EVs through the incorporation of the keyword "electric vehicle" in the bibliometric analysis. The selection of the Scopus database for this research is grounded in its superior importance compared to other databases, emphasizing its utilization in the bibliometric analysis. The VOSviewer software served as the analytical tool employed to visually represent crucial data, including information about countries, authors, journals, and keywords. The analysis, conducted on November 19, 2022, encompassed a thorough examination of 1074 documents spanning from 1985 to 2023. While the analysis of the number of publications over the years revealed in 2020 were 190 publications, marking the highest point for research and work on electric vehicle studies. The most of the articles were Conference paper among all 1074 documents with 61.7 % while review papers were identified as lowest document type with only 1.3 % of all of selected documents. Bagheri, M is the top writer with 25 documents on the Scopus database regarding to the key words, while others have publications around 11 and 16 number of papers. Russian Federation is the top contributor to the research of EVs with 61 % of all documents while Egypt is contributed with 1 % among all selected areas on the Scopus database. Notably, the IOP Conference Series Materials Science and Engineering was hold as one of the primary sources, accounting 76 documents to the electric vehicle studies. The outcomes of this investigation reveal noteworthy advancements in the volume of publications and the growing interest in electric vehicles, particularly within the academic and manufacturing sectors. [ABSTRACT FROM AUTHOR]

Aim: A well-known method for quantitatively evaluating scholarly work is bibliometric analysis. Best-cited papers raise awareness of the influential publications and patterns in the literature on a specific subject. The aim was to conduct bibliometric analysis to determine most cited articles on vesiculobullous oral lesions. This is the first study on citation analysis with respect to vesiculobullous oral lesions. Materials and Methods: A retrospective data search was explored on December 2022 using the Scopus database. The articles were evaluated, and fundamental data for bibliometric analysis was reviewed. Standard details about the author, linked organizations, publishing year, and place of origin were noted. Statistical analysis was performed using Chi-square analysis. VOSviewer software was used to determine the bibliometric network analysis for co-occurrence among coauthors and commonly used keywords. Results: A total of 344 articles published from 1971 to 2022 were included in the study. A total of 6680 citations and 19.41 citations per article were observed. The journal Archives of Dermatology received the most citation. There was a significant association between the number of citations and the journal type (open access vs. non-open access) (P < 0.05). Four to five highly related clusters with the help of VOSviewer software were found during co-occurrence network analysis. Conclusions: The top 10 articles on vesiculobullous oral lesions that received the most citations were listed in detail in the present study. This will be a valuable resource for academics, clinicians, and researchers in the fields of dermatology, general pathology, oral pathology, and oral medicine. [ABSTRACT FROM AUTHOR]

The R package 'tosr' enables the construction of the Tree of Science (ToS), a metaphorical representation of scientific papers on a specific topic. The ToS's roots symbolize seminal works, the trunk stands for structural works, and the leaves depict the current literature. Traditionally, researchers have had to limit their ToS to data from a single database, such as Scopus or Web of Science (WoS). The 'tosr' package overcomes this limitation by allowing researchers to merge seed files from both Scopus and WoS, thereby facilitating a more comprehensive bibliometric analysis. This paper describes the development and application of the 'tosr' package, demonstrating its unique capabilities in creating a completer and more cohesive ToS and citation network for any scientific topic. By bridging the gap between these two major databases, 'tosr' offers researchers an unprecedented tool for scientometric research. [ABSTRACT FROM AUTHOR]

To generate a research overview of digital banking, we did a bibliometric analysis. In our bibliometric analysis, we identified 2475 papers about digital banking that were published in the previous 10 years (2013–2023) from 1492 different sources under the direction of 6777 academics. The bibliographic information was gathered using the Scopus database. The dataset was examined using the bibliometric R program Biblioshiny. The number of digital banking research publications published each year, the most relevant author, the most cited articles, thematic maps, and trending topics are all listed. A keyword analysis showed that "blockchain", "security", "digital banking", and "digital storage" appeared most often. The research results will be significant to scholars, researchers, and policymakers in banking, as the study showed the roadmap and pathways to scientifically understanding current and future research trends. [ABSTRACT FROM AUTHOR]

Estimates of the price or value of the individual journals within a full-text database may be useful to librarians engaged in serials reviews or other collection development projects, to scholars investigating the determinants of journal prices, and to publishers seeking to rationalize their pricing strategies. This paper evaluates six methods of apportioning the cost of a full-text database among the individual journals in the database--methods based on variables such as journal size, total citations, Journal Impact Factor (JIF) percentile, and single-journal list price. Each method is evaluated based on how well the resulting prices can be predicted by the determinants of journal prices identified in previous research. Although the six methods yield similar results, the single best option is to use price estimates that account for JIF percentile. If citation data are not available and cannot be estimated, the best alternative is to rely on the equal-value assumption--to split the total price equally among the wanted journals in the database.

This paper aims to comprehensively explore the pivotal role of simulation and modeling in the field of Artificial Intelligence (AI). It focuses on elucidating the diverse applications of simulation and modeling in training AI systems, optimizing algorithms, and enhancing decision-making processes. To achieve this objective, we conducted an extensive review of the literature from the Scopus database, employing a well-defined selection process. We utilized keywords such as "simulation," "modeling," "Artificial Intelligence," and related terms to identify relevant papers published within the last 10 years. The selection criteria included assessing the relevance, quality, contribution, and recent citations of the papers. After a rigorous screening process, we selected 40 papers with the highest overall scores for inclusion in our review. The selected papers encompass a wide range of domains where simulation and modeling play a vital role in advancing AI applications. These domains include manufacturing, healthcare, energy consumption prediction, public sector decision-making, education, environmental modeling, and more. Our review highlights how AI leverages simulation and modeling to improve predictive accuracy, optimize resource allocation, and enhance decision-making processes across diverse sectors. We also discuss the potential future directions in the integration of simulation and modeling with AI, emphasizing its significance in various fields. [ABSTRACT FROM AUTHOR]

Background The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis. Methods NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH iCite tool was used to determine citation counts and focus (eg, clinical, animal). iCite and the NIH's LitCOVID database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning–based model built to identify common topics/themes within the publications. Results and Conclusions We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning–based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase. [ABSTRACT FROM AUTHOR]

Large-scale assessments are rich sources of data that can inform a diverse range of research questions related to educational policy and practice. For this reason, datasets from large-scale assessments are available to enable secondary analysts to replicate and extend published reports of assessment results. These datasets include multiple imputed values for proficiency, known as "plausible values." Plausible values enable the analysis of achievement in large-scale assessment data with complete-case statistical methods such as t-tests implemented in readily-available statistical software. However, researchers are often challenged by the complex and unfamiliar nature of plausible values, large-scale assessments, and their datasets. Misunderstandings and misuses of plausible values may therefore arise. The aims of this paper are to explain what plausible values are, why plausible values are used in large-scale assessments, and how plausible values should be used in secondary analysis of the data. Also provided are answers to secondary researchers' frequently asked questions about the use of plausible values in analysis gathered by the authors during their experience advising secondary users of these databases.

Although the field of Artificial Intelligence in Education (AIEd) has a substantial history as a research domain, never before has the rapid evolution of AI applications in education sparked such prominent public discourse. Given the already rapidly growing AIEd literature base in higher education, now is the time to ensure that the field has a solid research and conceptual grounding. This review of reviews is the first comprehensive meta review to explore the scope and nature of AIEd in higher education (AIHEd) research, by synthesising secondary research (e.g., systematic reviews), indexed in the Web of Science, Scopus, ERIC, EBSCOHost, IEEE Xplore, ScienceDirect and ACM Digital Library, or captured through snowballing in OpenAlex, ResearchGate and Google Scholar. Reviews were included if they synthesised applications of AI solely in formal higher or continuing education, were published in English between 2018 and July 2023, were journal articles or full conference papers, and if they had a method section. 66 publications were included for data extraction and synthesis in EPPI Reviewer, which were predominantly systematic reviews (66.7%), published by authors from North America (27.3%), conducted in teams (89.4%) in mostly domestic-only collaborations (71.2%). Findings show that these reviews mostly focused on AIHEd generally (47.0%) or Profiling and Prediction (28.8%) as thematic foci, however key findings indicated a predominance of the use of Adaptive Systems and Personalisation in higher education. Research gaps identified suggest a need for greater ethical, methodological, and contextual considerations within future research, alongside interdisciplinary approaches to AIHEd application. Suggestions are provided to guide future primary and secondary research.

Artificial intelligence (AI) has significantly transformed the medical field, especially in the diagnosis, treatment, and management of oncological diseases. It has had a profound impact on clinical decision-making and has enhanced the quality of life for various populations. This study aims to comprehensively assess the inherent relationship between AI and medicine and to uncover both its positive and negative implications. To achieve a comprehensive understanding, a thorough systematic review of articles was conducted, examining a total of 80 papers published between 2017 and 2023. These articles were carefully selected from well-known open-access databases, such as Scopus, IOPscience, IEEE Xplore, Google Scholar, ResearchGate, and ProQuest. A key finding from this review is that the majority of research on this topic has been published in scientific journals ranked in the first-quartile (Q1), underscoring the importance and high quality of research in this field. The United States, China, India, the United Kingdom, and Canada are the foremost countries in publishing on this topic. Most of the research is published in first-quartile (Q1) journals, representing 51% of the studies. Only 1% of articles appear in third-quartile (Q3) journals. IEEE Xplore is renowned as the primary database for accessing high-impact studies in this field. Future research should prioritize investigating the long-term impact of AI on patient clinical outcomes. International collaborative research could promote innovation and fairness in the implementation of artificial intelligence (AI) in oncology. [ABSTRACT FROM AUTHOR]

Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration time spans of the order of decades and, thus, cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. "Restoration" encompasses applications to pre-restoration and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. The aim of this scoping review is to identify, describe, and categorize current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of ecohydrological and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the entire Web of Science database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorized according to this review's research interests in seven distinct categories aggregating the papers' research themes and model outputs. Restoration site context was added by identifying 229 unique study site locations from the full database, which were catalogued and analysed against raster data for the Köppen–Geiger climate classification scheme. A majority of northern peatland sites were in temperate oceanic zones or humid continental zones that experienced snow. Over one in five models from the full database of papers were unnamed and likely intended for single use. Key themes emerging from topics covered by papers in the database included the following: modelling restoration development from a bog growth perspective, the prioritization of modelling greenhouse gas (GHG) emissions dynamics as a part of policymaking, the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems, and the increased prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Models are presented according to their application to peatlands or broader ecosystem and organized from most to least complex. This review provides valuable context for the application of ecohydrological models in determining strategies for peatland restoration and evaluating post-intervention development over time. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to present a bibliometric overview of the academic research on export performance (EP) in the business and management areas. Design/methodology/approach: A bibliometric overview of 1,463 papers from 1968 to 2021, including performance analysis, science mapping analysis and graphical mapping, was conducted using the Scopus database. SciMAT software was used for thematic analysis and conceptual evolution mapping of the EP domain, and VOSviewer software was used for graphical visualization. Findings: This study shows that EP research experienced spectacular growth, especially between 1998 and 2003, and the interest in this field continues to increase. Also, the USA and the UK appear to be the absolute leaders in EP research, with the best indicators of productivity and influence in all dimensions analyzed. The findings from the analysis through SciMAT indicate that "capabilities" and "R&D" are the main Motor themes that have contributed the most to the EP literature, whereas "global value chain" and "start-up" are emerging themes as new areas of interest. Research limitations/implications: This study develops a baseline for representing certain and exhaustive insights in the EP field and specifies trends over a period. Using a single database and excluding book chapters/conference papers are limitations of this study. Originality/value: EP is a research field that has gained wide acceptance in the academic community and international marketing literature. To the best of the authors' knowledge, no bibliometric overview has analyzed the EP literature. This study presents the first systematic quantitative analysis of academic research on EP in the business and management areas. [ABSTRACT FROM AUTHOR]

For the time being, many government or private organizations for recruitment of staff or educational institutions moving towards online based tests. The online examination system is a software application used for conducting examination using computer systems. It helps to the recruitment agency or any govt. or private organizations for conducting any job recruitment examinations transparently. Due to this system results are processed without delay and efficiently evaluated to assess the candidates abilities. But the biggest challenge for online examination system is data integrity, security and privacy. The current system is resolving the privacy issue by providing authentication credentials such as user name, password to the candidates. So that only authorized users with proper credentials can login to the system and attempt the exam. But the data confidentiality and integrity are biggest challenges for the system. As the data stored in system database is in plain text format, hence it may be modified or misused by the internal staff of the organization. This paper presents the frame work for secure storage and management of candidates data using encryption scheme, distributed databases in cloud database system. The proposed framework enhances the data confidentiality, integrity and avoids any cheating by internal staff or third party institutions. This paper conducts experimental work on proposed framework and analyses the results of the system. [ABSTRACT FROM AUTHOR]

Nowadays, literature review is a necessary task when trying to solve a given problem. However, an exhaustive literature review is very time-consuming in today's vast literature landscape. It can take weeks, even if looking only for abstracts or surveys. Moreover, choosing a method among others, and targeting searches within relevant problem and solution domains, are not easy tasks. These are especially true for young researchers or engineers starting to work in their field. Even if surveys that provide methods used to solve a specific problem already exist, an automatic way to do it for any use case is missing, especially for those who don't know the existing literature. Our proposed tool, SARBOLD-LLM, allows discovering and choosing among methods related to a given problem, providing additional information about their uses in the literature to derive decision-making insights, in only a few hours. The SARBOLD-LLM comprises three modules: (1: Scopus search) paper selection using a keyword selection scheme to query Scopus API; (2: Scoring and method extraction) relevancy and popularity scores calculation and solution method extraction in papers utilizing OpenAI API (GPT 3.5); (3: Analyzes) sensitivity analysis and post-analyzes which reveals trends, relevant papers and methods. Comparing the SARBOLD-LLM to manual ground truth using precision, recall, and F1-score metrics, the performance results of AI in the oncology case study are 0.68, 0.9, and 0.77, respectively. SARBOLD-LLM demonstrates successful outcomes across various domains, showcasing its robustness and effectiveness. The SARBOLD-LLM addresses engineers more than researchers, as it proposes methods and trends without adding pros and cons. It is a useful tool to select which methods to investigate first and comes as a complement to surveys. This can limit the global search and accumulation of knowledge for the end user. However, it can be used as a director or recommender for future implementation to solve a problem. Highlights: Automated support for literature choice and solution selection for any use case. A generalized keyword selection scheme for literature database queries. Trends in literature: detecting AI methods for a case study using Scopus and OpenAI. A better understanding of the tool by sensitivity analyzes for Scopus and OpenAI. Robust tool for different domains with promising OpenAI performance results. [ABSTRACT FROM AUTHOR]

Operational steganalysis contends with a major problem referred to as the cover-source mismatch (CSM), which is essentially a difference in distribution caused by different parameters and settings over training and test data. Despite it being of fundamental importance in an operational context, the CSM problem is often overlooked in the literature. With the goal to increase the visibility of this problem and attract the interest of the community, the present paper proposes a systematic review of the literature. It summarizes gathered knowledge and major open questions over the last 20 years of active research on CSM: terminology, methods of measurement, known causes, and mitigation strategies. Over 100 papers exploring, mitigating, assessing, or discussing steganalysis under train-test mismatch were collected by sampling scholar databases, and tracing references, cited and generated. For image steganalysis, the literature provided enough evidence to quantify the impact of causes, and the effectiveness of mitigation strategies. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) as a field has recently evolved as a transformative force in recruitment. Numerous empirical, conceptual, and exploratory studies have been conducted that indicate the novel ways organizations identify, select, and attract top talent. Nevertheless, no network analysis or attempt to map the literature scientifically in the domain has been done in the past. Hence, the paper intends to showcase the trends in Artificial Intelligence and Recruitment research through knowledge and conceptual structures. An analysis using bibliometric tools in artificial intelligence and recruitment was conducted. A sample of 1450 documents was extracted from the Scopus database. This was based on a search strategy determined by the author. An application that is equipped with a bibliometric package was installed. This software enabled the analysis of the dataset, and various themes, patterns, and trends were identified based on the authors, countries, and citations. Results show that the artificial intelligence and recruitment domain need direction. There is also a pressing need for interdisciplinary research in this area. The article provides some crucial insights on areas that need further inquiry. The conceptual and social network structure depicts an upward trend in terms of this area of research. There is a growing demand for Talent Acquisition practitioners and Recruiters with Artificial Intelligence skill sets. The current paper only had the Scopus Database as its backdrop. Future researchers could use multiple databases, such as the Web of Science, and conduct a comparative study. A systematic literature review would widen the scope and help identify some uncharted niche territories of recruitment and artificial intelligence. The novelty of the paper lies in the unexplored intersection of artificial intelligence and recruitment, as no bibliometric studies have been conducted on this subject before. [ABSTRACT FROM AUTHOR]

Financial literacy is considered an essential attribute for individuals and businesses to make optimal decisions. Considering the importance of financial literacy and the dearth of rigorous summaries in the existing literature on this topic, this study aims to investigate the factors affecting financial literacy. In doing so, we conducted a systematic review by selecting 53 papers from the Scopus database published between 1981 and 2024. Our investigation revealed that financial literacy is a multidimensional concept, and its determinants can be summarized into seven dimensions, namely demographic, socio-economic, psychological, financial, societal, Islamic, and technological factors. While demographic and socio-economic factors are widely used, psychological, financial, societal, and Islamic factors have received less attention from researchers. Nevertheless, the integration of technology into financial markets has recently drawn interest in the technological facet of financial literacy. Additionally, we analyzed the most influential papers and co-authorship networks in financial literacy research, providing a network analysis of existing studies. We further suggest that religious and technological factors, specifically Islamic financial literacy and digital financial literacy, may also influence financial literacy and deserve further investigation. [ABSTRACT FROM AUTHOR]

The superiority of BWM over other weighting methods for obtaining the weight values of the attributes is that it achieves high-confidence results with a reasonable number of pairwise comparisons. Although the best-worst method (BWM) is a well-known multi-criteria decision-making (MCDM) method that has been successfully utilized in almost all scientific areas to solve challenging real-life problems, no research has comprehensively examined the state-of-the-art in this regard. The present study depicts a detailed overview of publications concerned with BWM during the period 2015–2022. Based on the information obtained from the Scopus database, this work presents a big picture of current research on BWM. In other words, this paper analyzes the existing literature about BWM and identifies thematic contexts, application areas, emerging trends, and remaining research gaps to shed light on future research agendas aligning with those gaps. Further, the most recent BWM research is analyzed in the top ten scientific areas, from engineering to materials science. “Engineering”, “computer science”, and “business, management, and accounting” are the hottest fields of BWM research. China is the most active country regarding “engineering” and “computer science”, whereas India is the leader in “business, management, and accounting”. The study also reveals that there are still many research gaps in BWM research. The big picture taken in this study will not only showcase the current situation of BWM research but will also positively impact the direction and quality of new research. [ABSTRACT FROM AUTHOR]

This paper aims to help students better grasp the required professional knowledge and core concepts. This paper presents a design method for a multi-layer knowledge base based on XML. According to learners' identity characteristics and learning behaviour, using the mathematical statistics method, the feature expression for the learning system is constructed. Multivariable linear regression theory establishes convergence constraints for accurate and deep mining. The average detection results of the collected samples are used for high-quality deep mining of user portraits in the learning system. This project intends to study the method of solving accurate confidence intervals for user portrait data in the education system. Excel and Access are used to complete the data collection of the teaching object and the construction of the database. A multi-mode interactive editing and processing method of user portrait information for education systems is studied in cloud computing. Finally, a learning system based on mathematical loading mode is proposed, and an object-oriented learning recommendation system is designed. The developed teaching software can enable students to get more teaching guidance when they acquire the required knowledge to improve students' learning effect effectively. [ABSTRACT FROM AUTHOR]

The field of artificial intelligence is starting to permeate all aspects of society and management is no exception. From digital transformation, marketing and industry 4.0 to privacy and ethics, the significant growth of the number of papers being published each year makes it difficult to assess the state of research, the current topics that academia is focusing on and how these topics are evolving over the years. This paper aims to highlight the main topics of this complex and central theme and showcase the evolution of the field through a structured bibliometric analysis of all business-relevant articles and conference paper published in this interval. Leveraging the SCOPUS database, a number of 4763 papers have been identified and analysed, revealing a number of new insights into how the study of artificial intelligence is evolving. Although we are looking at a global perspective, in subsidiary we also observe and compare how Romania is faring against the other global players. [ABSTRACT FROM AUTHOR]

Background: Co-design with consumers and healthcare professionals is widely used in applied health research. While this approach appears to be ethically the right thing to do, a rigorous evaluation of its process and impact is frequently missing. Evaluation of research co-design is important to identify areas of improvement in the methods and processes, as well as to determine whether research co-design leads to better outcomes. We aimed to build on current literature to develop a framework to assist researchers with the evaluation of co-design processes and impacts. Methods: A multifaceted, iterative approach, including three steps, was undertaken to develop a Co-design Evaluation Framework: 1) A systematic overview of reviews; 2) Stakeholder panel meetings to discuss and debate findings from the overview of reviews and 3) Consensus meeting with stakeholder panel. The systematic overview of reviews included relevant papers published between 2000 and 2022. OVID (Medline, Embase, PsycINFO), EBSCOhost (Cinahl) and the Cochrane Database of Systematic reviews were searched for papers that reported co-design evaluation or outcomes in health research. Extracted data was inductively analysed and evaluation themes were identified. Review findings were presented to a stakeholder panel, including consumers, healthcare professionals and researchers, to interpret and critique. A consensus meeting, including a nominal group technique, was applied to agree upon the Co-design Evaluation Framework. Results: A total of 51 reviews were included in the systematic overview of reviews. Fifteen evaluation themes were identified and grouped into the following seven clusters: People (within co-design group), group processes, research processes, co-design context, people (outside co-design group), system and sustainment. If evaluation methods were mentioned, they mainly included qualitative data, informal consumer feedback and researchers' reflections. The Co-Design Evaluation Framework used a tree metaphor to represent the processes and people in the co-design group (below-ground), underpinning system- and people-level outcomes beyond the co-design group (above-ground). To evaluate research co-design, researchers may wish to consider any or all components in the tree. Conclusions: The Co-Design Evaluation Framework has been collaboratively developed with various stakeholders to be used prospectively (planning for evaluation), concurrently (making adjustments during the co-design process) and retrospectively (reviewing past co-design efforts to inform future activities). [ABSTRACT FROM AUTHOR]

Optical remote sensing can effectively capture 2-dimensional (2D) forest information, such as woodland area and percentage forest cover. However, accurately estimating forest vertical-structure relevant parameters such as height using optical images remains challenging, which leads to low accuracy of estimating forest stocks like biomass and carbon stocks. Thus, accurately obtaining vertical structure information of forests has become a significant bottleneck in the application of optical remote sensing to forestry. Microwave remote sensing such as synthetic aperture radar (SAR) and polarimetric SAR provides the capability to penetrate forest canopies with the L-band signal, and is particularly adept at capturing the vertical structure information of forests, which is an alternative ideal remote-sensing data source to overcome the aforementioned limitation. This paper utilizes the Citexs data analysis platform, along with the CNKI and PubMed databases, to investigate the advancements of applying L-band SAR technology to forest canopy penetration and structure-parameter estimation, and provides a comprehensive review based on 58 relevant articles from 1978 to 2024 in the PubMed database. The metrics, including annual publication numbers, countries/regions from which the publications come, institutions, and first authors, with the visualization of results, were utilized to identify development trends. The paper summarizes the state of the art and effectiveness of L-band SAR in addressing the estimation of forest height, moisture, and forest stocks, and also examines the penetration depth of the L-band in forests and highlights key influencing factors. This review identifies existing limitations and suggests research directions in the future and the potential of using L-band SAR technology for forest parameter estimation. [ABSTRACT FROM AUTHOR]

In recent years, various graph computing architectures have been proposed to process graph data that represent complex dependencies between different objects in the world. The designs of the processing element (PE) in traditional graph computing accelerators are often optimized for specific graph algorithms or tasks, which limits their flexibility in processing different types of graph algorithms, or the parallel configuration that can be supported by their PE arrays is inefficient. To achieve both flexibility and efficiency, this paper proposes Grapher, a reconfigurable graph computing accelerator based on an optimized PE array, efficiently supporting multiple graph algorithms, enhancing parallel computation, and improving adaptability and system performance through dynamic hardware resource configuration. To verify the performance of Grapher, this paper selected six datasets from the Stanford Network Analysis Project (SNAP) database for testing. Compared with the existing typical graph frameworks Ligra, Gemini, and GraphBIG, the processing time for the six datasets using the BFS, CC, and PR algorithms was reduced by up to 39.31%, 35.43%, and 27.67%, respectively. The energy efficiency has also been improved by 1.8× compared to Hitgraph and 4.7× compared to ThunderGP. [ABSTRACT FROM AUTHOR]

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%. [ABSTRACT FROM AUTHOR]

Background: Globally, reduction of patient waiting time has been identified as one of the major characteristics of a functional health system. In South Africa, 83% of the general population visiting primary healthcare (PHC) facilities must contend with long waiting times, overcrowding, staff shortages, poor quality of care, an ineffective appointment booking system, and a lack of medication. These experiences may, in turn, affect how patients view service quality. Methods: This scoping review was guided by Arksey and O'Malley methodological framework. The primary literature search of peer-reviewed and review articles was achieved through PubMed/MEDLINE, Google Scholar, Science Direct, and World Health Organization (WHO) library databases, using waiting times, outpatient departments, factors, interventions, and primary healthcare facilities as keywords. Two independent reviewers screened abstracts and full articles, using the set inclusion and exclusion criteria. We used NVIVO® version 10 software to facilitate thematic analysis of the results from included studies. Results: From the initial 250 records screened, nine studies were eligible for inclusion in this scoping review. Seven papers identified the factors contributing to waiting time, and five papers mentioned effective interventions implemented to reduce waiting times within PHC facilities. Our analysis produced three (patient factors, staff factors, and administrative systems) and two (manual-based waiting time reduction systems and electronic-based waiting time reduction systems) main themes pertaining to factors contributing to long waiting times and interventions to reduce waiting times, respectively. Conclusion: Our results revealed that the patients, staff, and administrative systems all contribute to long waiting times within the PHC facilities. Patient waiting times recorded a wider and more evenly spread patient arrival pattern after the identified interventions in our study were implemented. There is a need to constantly strategize on measures such as implementing the use of an electronic appointment scheduling system and database, improving staff training on efficient patient flow management, and regularly assessing and optimizing administrative processes. By continuously monitoring and adapting these strategies, PHC facility managers can create a more efficient and patient-centered healthcare experience. [ABSTRACT FROM AUTHOR]

This paper analyses Sol Plaatje University's (SPU) progress on increased research activities. The paper employs bibliometric analysis review method to demonstrate the university's transition from being a predominantly teaching-focused to a more researchoriented institution. A novel, data-driven methodology is also adopted in this paper, to identify and examine SPU's research niche through publications. This paper's data were collected from the Scopus and Web of Science databases. The paper's findings reveal that there was an overall significant increase in research outputs, observable on yearly basis for a decade (2014 to 2023). The yearly increase in research output is recorded from diverse research fields, including machine learning, cryptography, environmental research, and public health. Findings further reveal that SPU has built its international research collaborations within the African and European continents. This paper's findings contribute to literature on higher education development by offering insights into how newly established universities can transition from a teachingcentric focus to becoming research-active. This paper revealed the importance of strategic planning, interdisciplinary research, and international collaboration in the development of a vibrant research environment. [ABSTRACT FROM AUTHOR]

This paper aims to structure and semantically describe the information within the industrial chain by constructing an Industry Chain Knowledge Graph (ICKG), enabling more efficient and intelligent information management and analysis. In more detail, this paper constructs a multi-domain industrial chain dataset and proposes a method that combines the top-down establishment of a semantic expression framework with the bottom-up establishment of a data layer to build an ICKG. In the data layer, a deep learning algorithm based on BERT-BiLSTM-CRF is used to extract industry chain entities from relevant literature and reports. The results indicate that the model can effectively identify industry chain entities. These entities and relationships populate a Neo4j graph database, creating a large-scale ICKG for visual display and aiding cross-domain applications. [ABSTRACT FROM AUTHOR]

Background: Epilepsy is one of the most common serious chronic neurological disorders, which can have a serious negative impact on individuals, families and society, and even death. With the increasing application of machine learning techniques in medicine in recent years, the integration of machine learning with epilepsy has received close attention, and machine learning has the potential to provide reliable and optimal performance for clinical diagnosis, prediction, and precision medicine in epilepsy through the use of various types of mathematical algorithms, and promises to make better parallel advances. However, no bibliometric assessment has been conducted to evaluate the scientific progress in this area. Therefore, this study aims to visually analyze the trend of the current state of research related to the application of machine learning in epilepsy through bibliometrics and visualization. Methods: Relevant articles and reviews were searched for 2004-2023 using Web of Science Core Collection database, and bibliometric analyses and visualizations were performed in VOSviewer, CiteSpace, and Bibliometrix (R-Tool of R-Studio). Results: A total of 1,284 papers related to machine learning in epilepsy were retrieved from the Wo SCC database. The number of papers shows an increasing trend year by year. These papers were mainly from 1,957 organizations in 87 countries/regions, with the majority from the United States and China. The journal with the highest number of published papers is EPILEPSIA. Acharya, U. Rajendra (Ngee Ann Polytechnic, Singapore) is the authoritative author in the field and his paper "Deep Convolutional Neural Networks for Automated Detection and Diagnosis of Epileptic Seizures Using EEG Signals" was the most cited. Literature and keyword analysis shows that seizure prediction, epilepsy management and epilepsy neuroimaging are current research hotspots and developments. Conclusions: This study is the first to use bibliometric methods to visualize and analyze research in areas related to the application of machine learning in epilepsy, revealing research trends and frontiers in the field. This information will provide a useful reference for epilepsy researchers focusing on machine learning. [ABSTRACT FROM AUTHOR]

The current study on the synthesis problems of four-bar mechanism trajectories primarily relies on establishing a numerical atlas based on trajectory characteristics and employing neural networks to synthesize mechanism parameters. However, this approach has several shortcomings, including a vast database, inefficient retrieval, and challenges in maintaining accuracy. This paper presents a method for synthesizing a trajectory-generation mechanism that combines the extreme gradient boosting (XGBoost) algorithm with a genetic algorithm (GA). The purpose is to synthesize, based on a particular trajectory, the dimensions and installation position parameters of a four-bar mechanism. The paper classifies the trajectories according to their shape features and geometric center placements, thereby improving the accuracy of the XGBoost model for synthesizing mechanisms. The XGBoost algorithm is employed to synthesize the basic dimensional parameters for the mechanism, with the relative slopes of trajectories as input features. The synthesized basic dimensional parameters are turned into parameters for the actual mechanism by researching the scaling, translation, and rotation relationships between mechanisms and the trajectories they generate. The accuracy of the generated trajectories from the synthesized mechanism can be improved by applying GA to optimize the mechanism parameters. Five comparative examples are provided in this research for the different scenarios of given trajectory curves and trajectory points. The effectiveness and accuracy of the proposed approach in this study are validated in comparison to existing research methods by comparing errors between the generated trajectories and the given trajectories. [ABSTRACT FROM AUTHOR]

This paper presents priority support in the Internet of Things to support the reliable and timely transmission of messages during emergencies. The Message Queuing Telemetry Transport protocol is a widely used IoT messaging protocol. However, it does not support the timely and fast delivery of emergency messages. In this regard, this paper proposes to classify the messages into three different queues. The RabbitMQ broker manages virtual queues based on the message type, such as First Come First Served, Critical, and Urgent. In addition, the proposed approach stores the messages in the MySQL database for further analysis. To confirm its efficacy, we compare the Urgent and Critical queues with the current First Come First Served technique in an experimental implementation. Wireshark packet analyzer is used to record packets while messages are being transmitted between clients and the broker to examine end-to-end latency, jitter, response time, and total time. The results show that the proposed approach performs better for high-priority emergency messages. [ABSTRACT FROM AUTHOR]

This study aims to map out social manufacturing research and the distribution of social manufacturing research, which can be used as a reference for social manufacturing research. Various studies on social manufacturing have been carried out to explore various insights on social manufacturing in different domains. This study was conducted in 2021, and explored the social manufacturing literature using bibliometric analysis methods. The approach used in the bibliometric analysis is the citation analysis to see one article cited by another article, and the co-citation analysis approach to find two or more articles cited by one article. In this study, the data source used is from the Scopus database, with social manufacturing keyword. The results show the types of analysis that have been processed include co-authorship (authors, organizations, countries), citations (authors), and co-citations (sources, cited authors). This research still has limitations, because it use the Scopus database only, so for further research, it could be added with other sources, such as Web of Science, PubMed, Crossref, etc. In addition, there are still many visualization results using VOSviewer software that could be explored further, such as the type of analysis citation-document, citation-organization, bibliographic coupling, which has not been discussed in this study. [ABSTRACT FROM AUTHOR]

A quantitative analysis of scientific articles published in the Journal of Agrometeorology (JAM) between 2008 and 2022 was conducted using SCOPUS database and a variety of scientometric indicators. Various metrics were utilized to examine aspects including yearly research output, highly referenced sources, author rankings, contributions and profiles, cooperation trends, highly contributing nations, most cited papers, commonly searched keywords and worldwide collaboration mapping. This study employs biblioshiny for analysis and only looks at data that is available in Scopus database. With an h-index (17), a g-index (21) and 3238 total citations across the study period, the journal demonstrated considerable influence. With the greatest number of research publications (n=46) and the greatest number of citations (236), Pandey V stands out among other authors. In terms of the number of papers and citations, India emerged as the leading nation, with the Punjab Agricultural University in the lead with 744 publications. Four clusters were found by co-citation network analysis, with Allen RG being the most quoted author among them. The study also highlighted the fact that Indian authors worked together the most. This analysis is important for assessing the influence of the JAM and offers insightful information about noteworthy research trends and developments in the scientific community. [ABSTRACT FROM AUTHOR]

Background: Stem cell-related studies have been increasingly conducted to facilitate the regeneration of degenerative discs. However, analyses of high-impact articles focused on this topic are rare. This study aimed to determine and summarize the most-cited studies examining stem cells in the context of intervertebral disc degeneration (IDD). Methods: We searched the Web of Science (WoS) database for stem cell-related articles in IDD, and the 50 highest-cited papers were summarized. A correlation analysis was conducted to determine the relationship among WoS citations, Altmetric Attention Score (AAS), and Dimensions. Results: The number of citations of the top 50 manuscripts ranged from 92 to 370. The top three countries were the United States (14), China (10), and Japan (9). Spine (12) was the most prevalent journal, and this was followed by Biomaterials (6). Bone marrow-derived stem cells were the most common subject (38), and they were followed by nucleus pulposus-derived stem cells (4) and annulus fibrosus-derived stem cells (4). Humans were the most studied species (31), and the next most studied were rabbits (9) and rats (7). There was a very high correlation between WoS and Dimension citations (p < 0.001, r = 0.937). Conclusions: For the first time, the highest impact articles examining stem cells in the context of IDD were assessed together. The current study provides a deepened understanding of historical studies focused on stem cells in IDD and is beneficial for future studies in this field. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model's robust capability to address the unique challenges presented by the language's structural intricacies. [ABSTRACT FROM AUTHOR]