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The penetrating power of X-rays coupled with the high flux of 3rd generation synchrotron sources makes X-ray tomography to excel among fast imaging methods. To exploit this asset of synchrotron sources is the motivation for setting up an ultra-fast tomography endstation at the TOMCAT beamline. The state of the art instruments at synchrotron sources offer routinely a temporal resolution of tens of seconds in tomography. For a number of applications, for example biomedical studies, the relevant time scales (breathing, heartbeat) are rather in the range of 0.5-2 seconds. To overcome motion artifacts when imaging such systems a new ultra-fast tomographic data acquisition scheme is being developed at the TOMCAT beamline. We can acquire a full set of projections at sub-second timescale in monochromatic or white-beam configuration. We present a feasibility study with the ultimate aim to achieve sub-second temporal resolution in 3D without significant deterioration of the spatial resolution. For the first time, the 3D dynamics of the very early stages of a quickly aging liquid foam can be visualised with high quality and sufficiently large field of view. Abstract. The penetrating power of X-rays coupled with the high flux of 3rd generation synchrotron sources makes X-ray tomography to excel among fast imaging methods. To exploit this asset of synchrotron sources is the motivation for setting up an ultra-fast tomography endstation at the TOMCAT beamline. The state of the art instruments at synchrotron sources offer routinely a temporal resolution of tens of seconds in tomography. For a number of applications, for example biomedical studies, the relevant time scales (breathing, heartbeat) are rather in the range of 0.5-2 seconds. To overcome motion artifacts when imaging such systems a new ultra-fast tomographic data acquisition scheme is being developed at the TOMCAT beamline. We can acquire a full set of projections at sub-second timescale in monochromatic or white-beam configuration. We present a feasibility study with the ultimate aim to achieve sub-second temporal resolution in 3D without significant deterioration of the spatial resolution. For the first time, the 3D dynamics of the very early stages of a quickly aging liquid foam can be visualised with high quality and sufficiently large field of view.

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 much greater speed in future responses. These solutions are enabled by the social aspect of human-computer interactions (social machines) and the increased connectivity of humans and devices (Internet of Things). Conclusion Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

Highlights • We analysed scientific research records on Covid-19 with computable statistical methods. • We present visualisations of interrelationships between scientific research data records on Covid-19. • We compare the coverage of research on Covid 19 mortality, immunity, and vaccine development by organisations and countries. • Despite political disagreements, countries (US and China) are still collaborating in Covid-19 research.

We explore the potential and practical challenges in the use of artificial intelligence (AI) in cyber risk analytics, for improving organisational resilience and understanding cyber risk. The research is focused on identifying the role of AI in connected devices such as Internet of Things (IoT) devices. Through literature review, we identify wide ranging and creative methodologies for cyber analytics and explore the risks of deliberately influencing or disrupting behaviours to socio-technical systems. This resulted in the modelling of the connections and interdependencies between a system's edge components to both external and internal services and systems. We focus on proposals for models, infrastructures and frameworks of IoT systems found in both business reports and technical papers. We analyse this juxtaposition of related systems and technologies, in academic and industry papers published in the past 10 years. Then, we report the results of a qualitative empirical study that correlates the academic literature with key technological advances in connected devices. The work is based on grouping future and present techniques and presenting the results through a new conceptual framework. With the application of social science's grounded theory, the framework details a new process for a prototype of AI-enabled dynamic cyber risk analytics at the edge.

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