The ethics of shared Covid-19 risks: an epistemological framework for ethical health technology assessment of risk in vaccine supply chain infrastructures

Radanliev, Petar; Roure, David De; Ani, Uchenna; Carvalho, Graça Simões de

doi:10.1007/s12553-021-00565-3

Cited by 31 publications

(13 citation statements)

References 52 publications

(31 reference statements)

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“…The IoT industry belongs to the tertiary industry and also the high- and new-tech industry. From the perspective of industrial structure optimization and upgrading, the structure of this industry is gradually optimized from low to high [ 3 ]. For example, in terms of enterprise operating costs, if radiofrequency identification (RFID) technology is used, the entire enterprise can be automated.…”

Section: Introductionmentioning

confidence: 99%

The Influence of the Development of the Internet of Things Industry on the Optimization of the High- and New-Tech Industry Structure under the Wireless Mobile Network

Liu

Zhengchao

Jiang

et al. 2022

Computational Intelligence and Neuroscience

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At present, the pressure on China’s economic development is increasing day by day due to the profound changes in the internal and external environment. The global economic pattern is undergoing significant changes in terms of the external environment. Adjusting and optimizing the industrial structure will aid in achieving the goal of facilitating transformation through steady growth in the short term. Meanwhile, it will also accelerate sustainable economic development. In this study, the relevant theories of industrial structure optimization are described based on the impact of wireless mobile networks and the Internet of things (IoT) industry. Based on the gray correlation degree, the high- and new-tech industries under the development of the IoT industry are analyzed and the impact of optimization of the high- and new-tech industry structure is investigated. The results show that the development of the IoT industry has driven the development of the high- and new-tech industry. The gray correlation between the development of the IoT industry and the high- and new-tech industry obtained is 0.64, indicating a strong correlation. The average output share of the electronic computer and office equipment manufacturing industries is 47.09%. The average output ratio of the industrial structure optimization of the electronics and communication manufacturing industry is 42.55%. Moreover, the proportion of the output of medical manufacturing and medical equipment and instrument manufacturing industrial structure optimization is small, 15.63% and 10.54%, respectively. The results have significant value in the research on the impact of the development of the IoT industry on the high- and new-tech industry under the wireless mobile network and the effect of its industrial structure optimization.

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Section: Introductionmentioning

confidence: 99%

The Influence of the Development of the Internet of Things Industry on the Optimization of the High- and New-Tech Industry Structure under the Wireless Mobile Network

Liu

Zhengchao

Jiang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Although this research emphasizes the ethical challenges presented by common IoT system risks in the context of the COVID-19 pandemic, however, this work further requires investigations into the interdependencies among risk areas. In addition to this, Radanliev et al 25 present ethical awareness on cyber risk from IoT systems on shared COVID-19 supply chain infrastructure. Their research provides a detailed assessment of COVID-19 risk in healthcare technology and vaccine supply chain infrastructures.…”

Section: Research Background and Related Workmentioning

confidence: 99%

PESTLE+ risk analysis model to assess pandemic preparedness of digital ecosystems

Anwar

Gill

Fitzgibbon

et al. 2021

Security and Privacy

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COVID-19 pandemic has affected every country in many ways. Its substantial economic impacts are causing businesses to fade, pushing many nations into an economic downturn. This exposes organizations worldwide to unique risks which cannot be foreseen with conventional methods of risk analysis. This research is part of a broader action design research project conducted in collaboration with industry partner to answer an important research question: How to extend PESTLE risk analysis model to assess pandemic preparedness? In this context, the health factor is added to extend the traditional PESTLE risk analysis model. Furthermore, the interdependence between PESTLE factors has also been investigated, which has not been discussed before. The contribution of this research is the novel PESTLE+ risk analysis model that will help individuals and businesses to improve their understanding of the health crisis, such as the COVID-19, adjust accordingly and eventually endure the ongoing crisis, which is driving most businesses into liquidation.

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“…Their work focused on the identification of the role of AI in connected IoT devices to perform IoT risk assessment. Moreover, integration of IoT devices poses an ethical risk related to data security, privacy, reliability and management, data mining, and knowledge exchange [68].…”

Section: Introductionmentioning

confidence: 99%

Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

Dankwa

Yang

2021

Electronics

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The Internet of Things environment (e.g., smart phones, smart televisions, and smart watches) ensures that the end user experience is easy, by connecting lives on web services via the internet. Integrating Internet of Things devices poses ethical risks related to data security, privacy, reliability and management, data mining, and knowledge exchange. An adversarial machine learning attack is a good practice to adopt, to strengthen the security of text-based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), to withstand against malicious attacks from computer hackers, to protect Internet of Things devices and the end user’s privacy. The goal of this current study is to perform security vulnerability verification on adversarial text-based CAPTCHA, based on attacker–defender scenarios. Therefore, this study proposed computation-efficient deep learning with a mixed batch adversarial generation process model, which attempted to break the transferability attack, and mitigate the problem of catastrophic forgetting in the context of adversarial attack defense. After performing K-fold cross-validation, experimental results showed that the proposed defense model achieved mean accuracies in the range of 82–84% among three gradient-based adversarial attack datasets.

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The ethics of shared Covid-19 risks: an epistemological framework for ethical health technology assessment of risk in vaccine supply chain infrastructures

Cited by 31 publications

References 52 publications

The Influence of the Development of the Internet of Things Industry on the Optimization of the High- and New-Tech Industry Structure under the Wireless Mobile Network

The Influence of the Development of the Internet of Things Industry on the Optimization of the High- and New-Tech Industry Structure under the Wireless Mobile Network

PESTLE+ risk analysis model to assess pandemic preparedness of digital ecosystems

Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

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