Secure Distributed Mobile Volunteer Computing with Android

Volunteer Computing provision of seamless connectivity that enables convenient and rapid deployment of greener and cheaper computing infrastructure is extremely promising to complement next-generation distributed computing systems. Undoubtedly, without tactile Internet and secure VC ecosystems, harnessing its full potentials and making it an alternative viable and reliable computing infrastructure is next to impossible. Android-enabled smart devices, applications, and services are inevitable for Volunteer computing. Contrarily, the progressive developments of sophisticated Android malware may reduce its exponential growth. Besides, Android malwares are considered the most potential and persistent cyber threat to mobile VC systems. To secure Android-based mobile volunteer computing, the authors proposed MulDroid, an efficient and self-learning autonomous hybrid (Long-Short-Term Memory, Convolutional Neural Network, Deep Neural Network) multi-vector Android malware threat detection framework. The proposed mechanism is highly scalable with well-coordinated infrastructure and self-optimizing capabilities to proficiently tackle fast-growing dynamic variants of sophisticated malware threats and attacks with 99.01% detection accuracy. For a comprehensive evaluation, the authors employed current state-of-the-art malware datasets (Android Malware Dataset, Androzoo) with standard performance evaluation metrics. Moreover, MulDroid is compared with our constructed contemporary hybrid DL-driven architectures and benchmark algorithms. Our proposed mechanism outperforms in terms of detection accuracy with a trivial tradeoff speed efficiency. Additionally, a 10-fold cross-validation is performed to explicitly show unbiased results.

Fracturing Technology - Can We Finally Control Fracture Height?

For as long as we have been performing hydraulic fracturing, we have been trying to ensure that we stay out of undesirable horizons, potentially containing water and/or gas. The holy grail of hydraulic fracturing, an absolute control of created fracture height, has eluded the industry for more than 70 years. Of course, there have been many that have claimed solutions, but all the marketed approaches have at best merely created a delay to the inevitable growth and at worst been a snake-oil approach with little actual merit. Fundamentally, the applied techniques have attempted to delay or influence the underlying equations of net-pressure and stress variation; but having to ultimately honour them and by doing so then condemned themselves to limited success or outright failure. Fast forward to 2020, and a reassessment of the relative importance of height-growth constraint and what may have changed to help us achieve this. The development of unconventionals are focused on creating as much surface area as possible in micro/nano-Darcy environments, across almost any phase, but with typically poor line of sight to profit. However, the more valuable business of conventional oil and gas is working in thinner and thinner reservoirs with an often-deteriorating permeability, but with a significantly higher potential economic return. What unconventional has successfully delivered however, is a rapid deployment and acceleration in a range of completion technologies that were unavailable just a few years ago. We will demonstrate that these technologies potentially offer the capability of finally being able to control fracture height-growth. Consideration of a range of previously applied height-growth approaches will demonstrate how they attempted to fool or fudge height growth creation mechanisms. With this clarity, we can consider what advances in completion technology may offer in terms of delivering height growth control. We suggest that with the technology and approaches that are currently available today, that height-growth control is finally within reach. We will go on to describe a multi-well Pilot program, in deployment and execution in 2020/021 in Western Siberia; where billions of barrels remain to be recovered in thin oil-rim, low permeability sandstone reservoirs below gas or above water. A comprehensive assessment of the myriad of height-growth approaches that have been utilized over the last 70 years was performed, but in each case demonstrated the fallibility and limitations of each of these. However, rather than the interpretation that such control is not achievable, instead we will show a mathematically sound approach, along with field data and evidence that this is possible. The presentation will demonstrate that completion advances over the last 10 - 15 years make this approach a reality in the present day; and that broader field implementation is finally within reach.

Assessment of COVID-19 intervention strategies in the Nordic countries using genomic epidemiology

We explored how the duration, size and number of virus transmission clusters, defined as country-specific monophyletic groups in a SARS-CoV-2 phylogenetic tree, differed between the Nordic countries of Norway, Sweden, Denmark, Finland and Iceland. Our results suggest that although geographical connectivity, population density and openness influence the spread and the size of SARS-CoV-2 transmission clusters, the differing country-specific intervention strategies had the largest impact. We also found a significant positive association between the size and duration of transmission clusters in the Nordic countries, suggesting that the rapid deployment of contact tracing is a key response measure in reducing virus transmission.

Preliminary layout of the main technological equipment of a self-propelled machine with a rod mechanism for installing and fixing the end tower for a mobile ropeway

Mobile ropeways for carrying out transport operations, formed with the help of terminal transport units connected by a single cable system on the basis of self-propelled wheeled or tracked chassis of increased carrying capacity and cross-country ability, are a promising type of lifting and transport equipment that ensures the rapid deployment of the necessary technological means. The article discusses the issues of preliminary arrangement of the rod mechanism for installation and fixation in the working position of the end tower using a folding rod consisting of two articulated links for a constructive variant of the outrigger placement of the tower on a rotary platform. The design and principle of operation of the rod mechanism is considered. A mathematical model has been developed that provides the required normative vertical dimension of a self-propelled vehicle for the purpose of its safe independent movement to the deployment site on general-purpose highways. The analysis of the influence of normative dimensional requirements, the structural dimensions of the bearing frame of the chassis and the height of the end tower on the main structural dimensions of the articulated folding rod in the transport position is carried out.

JaponicusDB: Rapid deployment of a model organism database for an emerging model species

The fission yeast Schizosaccharomyces japonicus has recently emerged as a powerful system for studying the evolution of essential cellular processes, drawing on similarities as well as key differences between S. japonicus and the related, well-established model Schizosaccharomyces pombe. We have deployed the open-source, modular code and tools originally developed for PomBase, the S. pombe model organism database (MOD), to create JaponicusDB (www.japonicusdb.org), a new MOD dedicated to S. japonicus. By providing a central resource with ready access to a growing body of experimental data, ontology-based curation, seamless browsing and querying, and the ability to integrate new data with existing knowledge, JaponicusDB supports fission yeast biologists to a far greater extent than any other source of S. japonicus data. JaponicusDB thus enables S. japonicus researchers to realise the full potential of studying a newly emerging model species, and illustrates the widely applicable power and utility of harnessing reusable PomBase code to build a comprehensive, community-maintainable repository of species-relevant knowledge.

EpidemiXs: Harnessing digital technology in the fight against COVID-19 and the associated infodemic

BACKGROUND: EpidemiXs is an innovative ecosystem of digital tools centralizing official and validated information on COVID-19 for healthcare workers and the general public in a single hub. OBJECTIVE: The vision of EpidemiXs is to foster collaboration between researchers, institutions and individuals to promote “open data” in order to enrich the scientific community and further accelerate science in the fight against COVID-19. METHODS: Through its set of solutions, EpidemiXs Info, EpidemiXs TV and EpidemiXs Studies, this innovative ecosystem contributes to advancing collaborations, data collection and analysis, and helps find funders. RESULTS: EpidemiXs was launched in March 2020 in Spain with 30 healthcare institutions and rapidly reached close to 1 million users and 2 million views. EpidemiXs gained international recognition when it was awarded the Barcelona Health Hub Awards (BHHAwards) 2020 of the category “Best Startup Initiative to help tackle COVID-19”. CONCLUSION: EpidemiXs has proven the efficiency of the rapid deployment of digital tools in times of COVID-19.

FlockAI: A Testing Suite for ML-Driven Drone Applications

Machine Learning (ML) is now becoming a key driver empowering the next generation of drone technology and extending its reach to applications never envisioned before. Examples include precision agriculture, crowd detection, and even aerial supply transportation. Testing drone projects before actual deployment is usually performed via robotic simulators. However, extending testing to include the assessment of on-board ML algorithms is a daunting task. ML practitioners are now required to dedicate vast amounts of time for the development and configuration of the benchmarking infrastructure through a mixture of use-cases coded over the simulator to evaluate various key performance indicators. These indicators extend well beyond the accuracy of the ML algorithm and must capture drone-relevant data including flight performance, resource utilization, communication overhead and energy consumption. As most ML practitioners are not accustomed with all these demanding requirements, the evaluation of ML-driven drone applications can lead to sub-optimal, costly, and error-prone deployments. In this article we introduce FlockAI, an open and modular by design framework supporting ML practitioners with the rapid deployment and repeatable testing of ML-driven drone applications over the Webots simulator. To show the wide applicability of rapid testing with FlockAI, we introduce a proof-of-concept use-case encompassing different scenarios, ML algorithms and KPIs for pinpointing crowded areas in an urban environment.