Hanoi city is currently dealing with rapidly increasing air pollution that result from variety of sources. The main cause of pollution is exhaust gas from traffic system with a very large number of private vehicles. In order to help the city's environment authorities monitor the level of air pollution, a wireless sensor network is currently under development to collect traffic pollution data measured by a number of gas sensors. This paper focuses on how to process pollution data and visualize level of pollution relying on available datasets collected from sensor network. The volume of data collected from each area of the city can be very large and dynamic due to the number of mobile sensors deployed in the same area at the same time and their measurement frequency. First, we present a method for processing raw data using calibration and data clustering techniques. Second, we describe how measurement datasets are visually represented on the city's online map on the basis of mathematical interpolation method that corresponding to characteristics of environmental data. And then we also use computer graphic technique to improve the visualization quality. Finally, this paper show the result of those methods with sample data collected from an urban district of Hanoi City on a website by which we do not only provide to viewer the actual level of pollution by position but also by time.

Background A global pandemic has been declared for coronavirus disease 2019 (COVID-19), which has serious impacts on human health and healthcare systems in the affected areas, including Vietnam. None of the previous studies have a framework to provide summary statistics of the virus variants and assess the severity associated with virus proteins and host cells in COVID-19 patients in Vietnam. Method In this paper, we comprehensively investigated SARS-CoV-2 variants and immune responses in COVID-19 patients. We provided summary statistics of target sequences of SARS-CoV-2 in Vietnam and other countries for data scientists to use in downstream analysis for therapeutic targets. For host cells, we proposed a predictive model of the severity of COVID-19 based on public datasets of hospitalization status in Vietnam, incorporating a polygenic risk score. This score uses immunogenic SNP biomarkers as indicators of COVID-19 severity. Result We identified that the Delta variant of SARS-CoV-2 is most prevalent in southern areas of Vietnam and it is different from other areas in the world using various data sources. Our predictive models of COVID-19 severity had high accuracy (Random Forest AUC = 0.81, Elastic Net AUC = 0.7, and SVM AUC = 0.69) and showed that the use of polygenic risk scores increased the models’ predictive capabilities. Conclusion We provided a comprehensive analysis for COVID-19 severity in Vietnam. This investigation is not only helpful for COVID-19 treatment in therapeutic target studies, but also could influence further research on the disease progression and personalized clinical outcomes.

No abstract

Abstract-Effective detectors with low-complexity are considered for the Alamouti's multiuser space-time block coded (STBC) systems. Viewing the noiseless received signals from Q users as a lattice with basis vectors being the columns of the total channel matrix H, we apply lattice reduction to transform the original basis into a nearly orthogonal one which improves the decision regions against noise. Then, linear detection using zero-forcing (ZF) and minimum-mean-square-error (MMSE) methods is performed on the transformed basis to detect transmitted signals from the Q users. These lattice-reduction-aided (LRA) linear detectors significantly improve BER of the linear detectors and, more importantly, allow us to achieve full diversity at high Eb/N 0 regions.