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COVID-19 global pandemic is an unprecedented health crisis. Since the outbreak, many researchers around the world have produced an extensive collection of literatures. For the research community and the general public to digest, it is crucial to analyse the text and provide insights in a timely manner, which requires a considerable amount of computational power. Clouding computing has been widely adopted in academia and industry in recent years. In particular, hybrid cloud is gaining popularity since its two-fold benefits: utilising existing resource to save cost and using additional cloud service providers to gain assess to extra computing resources on demand. In this paper, we developed a system utilising the Aneka PaaS middleware with parallel processing and multi-cloud capability to accelerate the ETL and article categorising process using machine learning technology on a hybrid cloud. The result is then persisted for further referencing, searching and visualising. Our performance evaluation shows that the system can help with reducing processing time and achieving linear scalability. Beyond COVID-19, the application might be used directly in broader scholarly article indexing and analysing.

The wearables' market is rapidly evolving, with applications ranging from healthcare and activity monitoring to emerging domains such as drones and haptic helmets. Wearable-based contact tracing is gaining increased attention in the COVID-19 era for more efficient disease prevention. Therefore, it is of timely relevance to identify the leading existing wireless contact-tracing solutions and their suitability for wearables. Existing trade-offs of contact-tracing applications require a thorough analysis of technical capabilities, such as accuracy, energy consumption, availability, sources of errors when dealing with wireless channels, privacy challenges, and deterrents towards a large-scale adoption on the wearables market. Based on extensive literature research, we conclude that decentralized architectures generally offer a better place in a trade-off in terms of accuracy and user eagerness to adopt them, taking into account privacy considerations, compared to centralized approaches. Our paper provides a brief technical overview of the existing solutions deployed for contact tracing, defines main principles that affect the overall efficacy of digital contact tracing, and presents a discussion on the potential effect of wearables in tackling the spread of a highly contagious virus. acceptedVersion Peer reviewed

Summarization: Linkage disequilibrium (LD) is the non-random association between alleles at different loci. Assessing LD in thousands of genomes and/or millions of single-nucleotide poly-morphisms (SNPs) exhibits excessive time and memory requirements that can potentially hinder future large-scale genomic analyses. To this end, we introduce qLD (quickLD) (https//lgithub.com/StrayLamb2lqLD), a highly optimized open-source software that assesses LD based on Pearson's correlation coefficient. qLD exploits the fact that the computational kernel for calculating LD can be cast in terms of dense linear algebra operations. In addition, the software employs memory-aware techniques to lower memory requirements, and parallel GPU architectures to further shorten analysis times. qLD delivers up to 5x faster processing than the current state-of-the-art software implementation when run on the same CPU, and up to 29x when computation is offloaded to a GPU. Furthermore, the software is designed to quantity allele associations between arbitrarily distant loci in a time-and memory-efficient way, thereby facilitating the evaluation of long-range LD and the detection of co-evolved genes. We showcase qLD on the analysis of 22,554 complete SARS-CoV-2 genomes. Παρουσιάστηκε στο: 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering

With the advent of Coronavirus Disease 2019 (COVID-19) throughout the world, safe transportation becomes critical while maintaining reasonable social distancing that requires a strategy in the mobility of daily travelers. Crowded train carriages, stations, and platforms are highly susceptible to spreading the disease, especially when infected travelers intermix with healthy travelers. Travelers-profiling is one of the essential interventions that railway network professionals rely on managing the disease outbreak while providing safe commute to staff and the public. In this plethora, a Machine Learning (ML) driven intelligent approach is proposed to manage daily train travelers that are in the age-group 16-59 years and over 60 years (vulnerable age-group) with the recommendations of certain times and routes of traveling, designated train carriages, stations, platforms, and special services using the London Underground and Overground (LUO) Network. LUO dataset has been compared with various ML algorithms to classify different agegroup travelers where Support Vector Machine (SVM) mobility prediction classification achieves up to 86.43% and 81.96% in age-group 16-59 years and over 60 years.

Six months following the beginning of Covid-19 pandemic in China, data on the risk of SARS-CoV-2 infection among patients with autoimmune rheumatic diseases are now available. However, the rapid spread of the pandemic has not allowed proper design of prospective studies, thus evidence came mostly from case series and observational studies. The early enthusiasm on hydroxychloroquine (HCQ) anti-viral properties should not suggest that patients who are long-term treated with antimalarials, such as patients with systemic lupus erythematosus (SLE), are protected against SARS-CoV-2 infection. Indeed, a French report on 17 HCQ-treated SLE patients dampened the enthusiasm.1 A recent report from Covid-19 Global Rheumatology Alliance has described 80 SLE patients with Covid-19, mostly females under 65 years of age, 64% of whom were already taking HCQ before the infection: the rate of hospitalisation and the need for intensive care did not differ between patients who were and those who were not taking HCQ.2 A study group from Northern Italy – the Italian epicentre of the pandemic – reported an incidence of 2.5% of Covid-19 (higher compared to the general population of the same region) in 165 patients with SLE.3 Patients with SLE are possibly at risk of developing symptomatic or severe Covid-19, not only because of their disease or treatment but as a consequence of associated comorbidities known to worsen the outcome of SARS-COv-2 infection.4 5 What do we know so far? SLE patients should not withdraw their medication. Before drawing any other conclusion, large registry data are needed to clarify the incidence and the outcome of Covid-19 in patients with SLE. Learning Objectives Describe the current evidence for risk of SARS-CoV-2 infection among patients with autoimmune rheumatic diseases, notably SLE Explain why it is important to ensure robust evidence are available to clarify the outcome of Covid-19 in patients with SLE References Mathian A, Mahevas M, Rohmer J, et al. Clinical course of coronavirus disease 2019 (COVID-19) in a series of 17 patients with systemic lupus erythematosus under long-term treatment with hydroxychloroquine. Ann Rheum Dis 2020;79(6):837–39. Konig MF, Kim AH, Scheetz MH, et al. Baseline use of hydroxychloroquine in systemic lupus erythematosus does not preclude SARS-CoV-2 infection and severe COVID-19. Ann Rheum Dis 2020 doi: 10.1136/annrheumdis-2020-217690 [published Online First: 2020/05/10]. Bozzalla Cassione E, Zanframundo G, Biglia A, et al. COVID-19 infection in a northern-Italian cohort of systemic lupus erythematosus assessed by telemedicine. Ann Rheum Dis 2020 doi: 10.1136/annrheumdis-2020-217717 [published Online First: 2020/05/14]. Wallace B, Waher L, Correspondence regarding Research Letter to the Editor by Mathian A, et al. ‘Clinical course of coronavirus disease 2019 (COVID-19) in a series of 17 patients with systemic lupus under long-term treatment with hydroxychloroquine’. Ann Rheum Dis 2020. doi:10.1136/annrheumdis-2020-217794. Gianfrancesco M, Hyrich KL, Al-Adely S, et al. Characteristics associated with hospitalisation for COVID-19 in people with rheumatic disease: data from the COVID-19 Global Rheumatology Alliance physician-reported registry. Ann Rheum Dis 2020;79(7):859–66.

COVID-19 has caused more than 880.000 victims all over the world (September 2020); despite a large effort of the scientific community and of the governments, it is still a great problem, inducing most of the Nations to adopt restriction to mo-bility, social relations and economic activities. Since the beginning of the pandemic, COVID-19 appeared a rather mysterious virus, for which neither a vaccination nor specific medications exist. In this paper, COVID-19 is characterized by using the available data of total number of infected, healed and dead people to identify the contagion, the removed and the death rates. These values depend on various aspects related to the population characteristics, the general health conditions, the social and economical situations, as well as to other features not yet identified by the scientific community. The COVID-19 situation in Italy is herein explored, showing the great heterogeneity of the virus spread in different zones.

Link/channel scheduling is a process by which different channels within the allowed set of frequencies or different timeslots from a single channel are assigned to achieve minimum interference to the neighbouring nodes and higher efficiency. Existing channel scheduling approaches are often evaluated under the assumption of random mobility model. These approaches tend to average out interfering and non-interfering situations and the end results are not purely applicable to a consistent interference situation, such as the COVID-19 isolation centers. Therefore, in this paper, for a given interference level, we analyze the performance of the Interference and Priority aware Coexistence (IPC) algorithm. To this end, a controlled interference generation model is designed that can ensure consistent interference among a given number of Wireless Body Area Networks (WBANs). The performance is assessed in terms of delay, delivery, reuse, energy consumption, and throughput. Under controlled interference, the results show that the IPC algorithm guarantees better results as compared to existing link scheduling techniques.

This paper presents how the COVID-19 pandemic has changed the course of the mobile robotics market, showing the status of mobile robots in three stages: before, during and after the COVID-19 pandemic. By analyzing these stages, it is possible to estimate what awaits this market in the future. From the many applications of mobile robots found during the COVID- 19 pandemic, as will be shown later, it is clear that mobile robots will be an important part of the future influencing the accelerated growth of their market and development.

This article presents an investigation about the different applications of mobile robots in the fight against the Covid- 19 pandemic. It shows the different contributions of companies around the world that seek to adapt to the new needs in order to be able to mitigate the progress of the Covid-19 using mobile robots as a tool, focusing primarily in the area of health and service.