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This paper represents a bibliometric analysis of the Scopus database publications on resilience and urban planning. The study recalled 1,923 documents from 1997 to 2021 using keywords related to the research topic and analyzed this using Microsoft Excel 2019, VOSviewer and Harzing's Publish or Perish software. The findings show that the expansion rate of works in resilience and urban planning has steadily increased every year since 1997. This study's most frequently used terms are the main keywords, which are climate change, sustainability, urban resilience and spatial planning. Landscape and Urban Planning is recommended as the main target journal for publication of the results of this research analysis. Research on COVID-19 or pandemic resilience, public space and urban mobility may eventually supersede prior dominant themes. The findings suggest that researchers from less contributed countries should explore this topic more to provide nuance to this field. © 2022 by MIP.
ABSTRACT
Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results.
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This study aims to provide an overview of research related to ambulatory care centers by mapping its outputs. The methodology used in this research is a bibliometrics study based on the Scopus database from 2001 to 2020. The analysis was conducted using Harzing Publish and Perish and VOSviewer to generate visualization data. A total of 145 papers from the scientific database were analyzed. The results show that there is still a lack of research being conducted regarding ambulatory care centers, particularly on their potential for information dissemination and the prevention of COVID-19 cases. The analysis provides a new perspective on the concept of ambulatory care centers in healthcare management. Finally, the analysis identifies potential baseline research that can be established before initiating ambulatory care center research for future studies © 2022, Malaysian Journal of Public Health Medicine.All Rights Reserved.
ABSTRACT
Real-time data processing and distributed messaging are problems that have been worked on for a long time. As the amount of spatial data being produced has increased, coupled with increasingly complex software solutions being developed, there is a need for platforms that address these needs. In this paper, we present a distributed and light streaming system for combating pandemics and give a case study on spatial analysis of the COVID-19 geo-tagged Twitter dataset. In this system, three of the major components are the translation of tweets matching with user-defined bounding boxes, name entity recognition in tweets, and skyline queries. Apache Pulsar addresses all these components in this paper. With the proposed system, end-users have the capability of getting COVID-19 related information within foreign regions, filtering/searching location, organization, person, and miscellaneous based tweets, and performing skyline based queries. The evaluation of the proposed system is done based on certain characteristics and performance metrics. The study differs greatly from other studies in terms of using distributed computing and big data technologies on spatial data to combat COVID-19. It is concluded that Pulsar is designed to handle large amounts of long-term on disk persistence.
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The outbreak of the COVID-19 pandemic has resulted in various studies to find out various solutions, from economic, cultural, social and political perspectives. Changes in lifestyle to survive in the pandemic era have led, scientific communities attempt to seek information and knowledge. This study aims to investigate related to knowledge sharing by utilizing ICT during the Covid-19 pandemic by utilizing a systematic literature review. The search for scientific literature was carried out by using Publish or Perish and PRISMA2000 through the keywords 'knowledge sharing', 'ICT' and 'COVID-19' and found 32 journal articles during the period of 2020 until the first half of 2022. The study found that there has been an increasing trend in research on knowledge sharing based on ICT during the period in question. ICT is very helpful for the community in sharing knowledge and the more people can take advantage of ICT, the faster people will acquire knowledge. © 2022 IEEE.
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Recent interest in multiple sclerosis research warrants literature analysis to evaluate the current state of the discipline and new research domains. This bibliometric review summarised the research trends and analysed research areas in multiple sclerosis over the last decade. The documents containing the term 'multiple sclerosis' in the article title were retrieved from the Scopus database. We used Harzing's Publish or Perish and VOSviewer for citation analysis and data visualisation, respectively. We found a total of 18,003 articles published in journals in the English language between 2012 and 2021. The emerging keywords identified utilising the enhanced strategic diagram were 'covid-19', 'teriflunomide', 'clinical trial', 'microglia', 'b cells', 'myelin', 'brain', 'white matter', 'functional connectivity', 'pain', 'employment', 'health-related quality of life', 'meta-analysis' and 'comorbidity'. This study demonstrates the tremendous growth of multiple sclerosis literature worldwide, which is expected to grow more than double during the next decade especially in the identified emerging topics.
Subject(s)
COVID-19 , Multiple Sclerosis , Humans , Quality of Life , Sclerosis , Myelin SheathABSTRACT
Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to capture the high variability of personal exposure to air pollution during daily life with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of these novel technologies for scientific and policy purposes due to their lack of reliability. The aim of this work is the development of three types of portable air quality devices that monitor particulate matter, differential pressure and outdoor emissions (CO, CO2, O3 and VOCs) with high reliability using low-cost sensors and communicating measurements to the cloud in real time. Reliability is strengthened in all three places: at the sensor level, the device/edge level and at the cloud, cashing data until network connectivity is restored. In order to evaluate their efficiency, two case studies were deployed: (a) in a modern industrial setting and (b) in an IT office space in Greece and the findings are reported.
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BACKGROUND: Preprints are publicly available manuscripts posted to various servers that have not been peer reviewed. Although preprints have existed since 1961, they have gained increased popularity during the COVID-19 pandemic due to the need for immediate, relevant information. OBJECTIVE: The aim of this study is to evaluate the publication rate and impact of preprints included in the Centers for Disease Control and Prevention (CDC) COVID-19 Science Update and assess the performance of the COVID-19 Science Update team in selecting impactful preprints. METHODS: All preprints in the first 100 editions (April 1, 2020, to July 30, 2021) of the Science Update were included in the study. Preprints that were not published were categorized as "unpublished preprints." Preprints that were subsequently published exist in 2 versions (in a peer-reviewed journal and on the original preprint server), which were analyzed separately and referred to as "peer-reviewed preprint" and "original preprint," respectively. Time to publish was the time interval between the date on which a preprint was first posted and the date on which it was first available as a peer-reviewed article. Impact was quantified by Altmetric Attention Score and citation count for all available manuscripts on August 6, 2021. Preprints were analyzed by publication status, publication rate, preprint server, and time to publication. RESULTS: Of the 275 preprints included in the CDC COVID-19 Science Update during the study period, most came from three servers: medRxiv (n=201, 73.1%), bioRxiv (n=41, 14.9%), and SSRN (n=25, 9.1%), with 8 (2.9%) coming from other sources. Additionally, 152 (55.3%) were eventually published. The median time to publish was 2.3 (IQR 1.4-3.7). When preprints posted in the last 2.3 months were excluded (to account for the time to publish), the publication rate was 67.8%. Moreover, 76 journals published at least one preprint from the CDC COVID-19 Science Update, and 18 journals published at least three. The median Altmetric Attention Score for unpublished preprints (n=123, 44.7%) was 146 (IQR 22-552) with a median citation count of 2 (IQR 0-8); for original preprints (n=152, 55.2%), these values were 212 (IQR 22-1164) and 14 (IQR 2-40), respectively; for peer-review preprints, these values were 265 (IQR 29-1896) and 19 (IQR 3-101), respectively. CONCLUSIONS: Prior studies of COVID-19 preprints found publication rates between 5.4% and 21.1%. Preprints included in the CDC COVID-19 Science Update were published at a higher rate than overall COVID-19 preprints, and those that were ultimately published were published within months and received higher attention scores than unpublished preprints. These findings indicate that the Science Update process for selecting preprints had a high fidelity in terms of their likelihood to be published and their impact. The incorporation of high-quality preprints into the CDC COVID-19 Science Update improves this activity's capacity to inform meaningful public health decision-making.
Subject(s)
COVID-19 , Centers for Disease Control and Prevention, U.S. , Humans , Pandemics , United States/epidemiologyABSTRACT
The major event of the emergence of the infectious disease COVID-19 has changed the perspective and behavior of investors in the stock market. This study aims to analyze the determinants of the research roadmap cluster in the field of capital market performance. Analysis was conducted on manuscripts published in Scopus-indexed publications from 2020 to the end of 2021. Using the search engine software Publish or Perish (PoP), 400 manuscripts were obtained. Furthermore, manuscripts with high credibility were selected through double-blind review and journal categories. The results of the extraction of 240 article titles and their s using VOSviewer revealed that the determinants of the research roadmap in the stock market during the COVID-19 outbreak were divided into four clusters, namely: 1) an increase in cryptocurrencies such as bitcoin due to the spillover of oil and gold prices;2) international stock market response and behavior;3) major stock market performance results such as stock returns and equity;and 4) changes in stock market dynamics in the form of volatility and investor attention.
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During the COVID-19 pandemic, virtual laboratories are instrumental in learning physics concepts. A virtual laboratory is one media that can be used as a solution to carry out concept discovery activities through simulation using computer software. This article was created to explain the trend of publications related to virtual laboratories during the COVID-19 pandemic for 2020-2021 through bibliometric analysis of the literature. The papers analyzed were obtained using the Publish or Perish (PoP) software on the Google Scholar database. The following publication trend expresses in the form of graphic visualization, which is analyzed using VOSviewer software. Overall, articles with the keyword virtual laboratory published in the late 2020-2021 period focus on its relationship with Covid 19 and evaluation. This focus of research shows that during the past year, the focus of research is how to evaluate learning through virtual laboratories. © 2021 Institute of Physics Publishing. All rights reserved.
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The Covid-19 pandemic has brought significant changes in education and the tendency of face-to-face learning to be online. Therefore, several studies have developed e-modules to support learning, one of which is physics learning. This research is the result of a review based on a bibliometric analysis of the physics e-module. The data was obtained based on articles published in journals and proceedings on Google Scholar in 2020-2021, namely when the Covid-19 pandemic happened. These were selected via Publish or Perish (PoP) software. The data has been sorted until found 74 articles from 142 articles. The data were processed in depth using VOSviewer software. The results showed how the network, overlay, and density were between pieces. It also showed other information regarding the distribution of the authors and their affiliations. Based on this analysis, it was suggested that there is a need to strengthen collaboration between writers and research focused on students' abilities and skills. © 2021 Institute of Physics Publishing. All rights reserved.
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BACKGROUND: Papers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion. OBJECTIVE: We present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications. METHODS: We publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher's query. RESULTS: PLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research. CONCLUSIONS: The PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.
Subject(s)
COVID-19 , Image Interpretation, Computer-Assisted , Information Storage and Retrieval , SARS-CoV-2 , Humans , Natural Language Processing , Software , User-Computer InterfaceABSTRACT
State policies mandating public or community use of face masks or covers in mitigating the spread of coronavirus disease 2019 (COVID-19) are hotly contested. This study provides evidence from a natural experiment on the effects of state government mandates for face mask use in public issued by fifteen states plus Washington, D.C., between April 8 and May 15, 2020. The research design is an event study examining changes in the daily county-level COVID-19 growth rates between March 31 and May 22, 2020. Mandating face mask use in public is associated with a decline in the daily COVID-19 growth rate by 0.9, 1.1, 1.4, 1.7, and 2.0 percentage points in 1-5, 6-10, 11-15, 16-20, and 21 or more days after state face mask orders were signed, respectively. Estimates suggest that as a result of the implementation of these mandates, more than 200,000 COVID-19 cases were averted by May 22, 2020. The findings suggest that requiring face mask use in public could help in mitigating the spread of COVID-19.