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Background and aims Covid-19 is a global pandemic that requires a global and integrated response of all national medical and healthcare systems. Covid-19 exposed the need for timely response and data sharing on fast spreading global pandemics. In this study, we investigate the scientific research response from the early stages of the pandemic, and we review key findings on how the early warning systems developed in previous epidemics responded to contain the virus. Methods We conducted data mining of scientific literature records from the Web of Science Core Collection, using the topics Covid-19, mortality, immunity, and vaccine. The individual records are analysed in isolation, and the analysis is compared with records on all Covid-19 research topics combined. The data records are analysed with commutable statistical methods, including R Studio's Bibliometrix package, and the Web of Science data mining tool. Results From historical analysis of scientific data records on viruses, pandemics and mortality, we identified that Chinese universities have not been leading on these topics historically. However, during the early stages of the Covid-19 pandemic, the Chinese universities are strongly dominating the research on these topics. Despite the current political and trade disputes, we found strong collaboration in Covid-19 research between the US and China. From the analysis on Covid-19 and immunity, we wanted to identify the relationship between different risk factors discussed in the news media. We identified a few clusters, containing references to exercise, inflammation, smoking, obesity and many additional factors. From the analysis on Covid-19 and vaccine, we discovered that although the USA is leading in volume of scientific research on Covid-19 vaccine, the leading 3 research institutions (Fudan, Melbourne, Oxford) are not based in the USA. Hence, it is difficult to predict which country would be first to produce a Covid-19 vaccine. Conclusions We analysed the conceptual structure maps with factorial ...

Cybersecurity risk assessment approaches have served us well over the last decade. They have provided a platform through which organisations and governments could better protect themselves against pertinent risks. As the complexity, pervasiveness and automation of technology systems increases however, particularly with the Internet of Things (IoT), there is a strong argument for the need for new approaches to assess risk and build trust. The challenge with simply extending existing assessment methodologies to these systems is that we could be blind to new risks arising in such ecosystems. These risks could be related to the high degrees of connectivity present, or the coupling of digital, cyber-physical and social systems. This article makes the case for new methodologies to assess risk in this context which consider the dynamics and uniqueness of IoT, but also the rigour of best practice in risk assessment.

We investigate the emerging meanings of ''value'' associated with the Internet of Things. Given the current political economy, we argue that the multiple meanings of ''value'' cannot be reduced to a single domain or discipline, but rather they are invariably articulated at the juxtaposition of three domains: social, economic, and technical. We analyse each of these domains and present domain challenges and cross-domain implications – drawing from an interdisciplinary literature review and gap analysis across sources from academia, business, and governments. We propose a functional model that aggregates these findings into a value-driven logic of the emerging global political economy enabled by digital technology in general and IoT in particular. These conceptual contributions highlight the critical need for an interdisciplinary understanding of the meaning of ''value'', so that IoT services and products will create and sustain such concurrent meanings during their entire lifecycle, from design to consumption and retirement or recycling.

Whether in the science lab or the music studio, we go in with a plan, we perform, and we make a record of that performance for distribution, consumption, and reuse. Both domains are increasingly data-intensive, with the adoption of new technology, and also socially intensive with democratised and growing citizen engagement. The music industry has embraced digital technology throughout the lifecycle from composition to consumption; scientific practice, and scholarly communication, are also undergoing transformation. Is the music industry more digital than science? We suggest that comparing and contrasting these two systems will provide insights of mutual benefit. Our investigation explores the notion of the Digital Music Object, analogous to the Research Object, for rich capture, sharing and reuse of both process and content.

Objectives: Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. Method: This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Results: Most countries' digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual's location databases, code scanning and individual's online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on social machines and connected devices. Solutions: The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a social machine with strong security and privacy protocols. We present solutions that would enable ...

Multiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

This report examines the policy challenges for the Internet of Things (IoT), and raises a broad range of issues that need to be considered if policy is to be effective and the potential economic value of IoT is harnessed. It builds on the Blackett review, The Internet of Things: making the most of the second digital revolution, adding detailed knowledge based on research from the PETRAS Cybersecurity of the Internet of Things Research Hub and input from Fellows of the Royal Academy of Engineering. The report targets government policymakers, regulators, standards bodies and national funding bodies, and will also be of interest to suppliers and adopters of IoT products and services.