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      The application of industry 4.0 technologies in pandemic management: Literature review and case study

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          Abstract

          The Covid-19 pandemic impact on people’s lives has been devastating. Around the world, people have been forced to stay home, resorting to the use of digital technologies in an effort to continue their life and work as best they can. Covid-19 has thus accelerated society’s digital transformation towards Industry 4.0 (the fourth industrial revolution). Using scientometric analysis, this study presents a systematic literature review of the themes within Industry 4.0. Thematic analysis reveals that the Internet of Things (IoT), Artificial Intelligence (AI), Cloud computing, Machine learning, Security, Big Data, Blockchain, Deep learning, Digitalization, and Cyber-physical system (CPS) to be the key technologies associated with Industry 4.0. Subsequently, a case study using Industry 4.0 technologies to manage the Covid-19 pandemic is discussed. In conclusion, Covid-19,is clearly shown to be an accelerant in the progression towards Industry 4.0. Moreover, the technologies of this digital transformation can be expected to be invoked in the management of future pandemics.

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          Deep learning.

          Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)
          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Is Open Access

            Software survey: VOSviewer, a computer program for bibliometric mapping

            Nees Jan van Eck, Ludo Waltman (2011)
            We present VOSviewer, a freely available computer program that we have developed for constructing and viewing bibliometric maps. Unlike most computer programs that are used for bibliometric mapping, VOSviewer pays special attention to the graphical representation of bibliometric maps. The functionality of VOSviewer is especially useful for displaying large bibliometric maps in an easy-to-interpret way. The paper consists of three parts. In the first part, an overview of VOSviewer’s functionality for displaying bibliometric maps is provided. In the second part, the technical implementation of specific parts of the program is discussed. Finally, in the third part, VOSviewer’s ability to handle large maps is demonstrated by using the program to construct and display a co-citation map of 5,000 major scientific journals.
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              CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature

              Chaomei Chen (2006)
              Article 0 comments Cited 1039 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Title: Healthcare Analytics
                Publisher: The Authors. Published by Elsevier Inc.
                ISSN (Print): 2772-4425
                ISSN (Electronic): 2772-4425
                Publication date PMC-release: 21 October 2021
                Publication date (Electronic): 21 October 2021
                Electronic Location Identifier: 100008
                Affiliations
                [a ]School of the Built Environment, University of Technology Sydney, Sydney 2007, Australia
                [b ]School of Project Management, The University of Sydney, Sydney 2006, Australia
                [c ]School of Architecture and Built Environment, Deakin University, Geelong VIC 3220, Australia
                Author notes
                [* ]Corresponding author.
                Article
                Publisher Item ID: S2772-4425(21)00007-1 Publisher ID: 100008
                DOI: 10.1016/j.health.2021.100008
                PMC ID: 8529533
                SO-VID: c3a64f93-8dc5-4fdb-beaf-336c384dbe8e
                Copyright © © 2021 The Authors. Published by Elsevier Inc.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 15 September 2021
                Date revision received : 5 October 2021
                Date accepted : 11 October 2021
                Categories
                Subject: Article

                Keywords: industry 4.0,internet of things (iot),blockchain,artificial intelligence (ai),covid-19,pandemic
                Data availability:
                Keywords: industry 4.0, internet of things (iot), blockchain, artificial intelligence (ai), covid-19, pandemic

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