Smart City Construction and Management by Digital Twins and BIM Big Data in COVID-19 Scenario

With the rapid development of information technology and the spread of Corona Virus Disease 2019 (COVID-19), the government and urban managers are looking for ways to use technology to make the city smarter and safer. Intelligent transportation can play a very important role in the joint prevention. This work expects to explore the building information modeling (BIM) big data (BD) processing method of digital twins (DTs) of Smart City, thus speeding up the construction of Smart City and improve the accuracy of data processing. During construction, DTs build the same digital copy of the smart city. On this basis, BIM designs the building's keel and structure, optimizing various resources and configurations of the building. Regarding the fast data growth in smart cities, a complex data fusion and efficient learning algorithm, namely Multi-Graphics Processing Unit (GPU), is proposed to process the multi-dimensional and complex BD based on the compositive rough set model. The Bayesian network solves the multi-label classification. Each label is regarded as a Bayesian network node. Then, the structural learning approach is adopted to learn the label Bayesian network's structure from data. On the P53-old and the P53-new datasets, the running time of Multi-GPU decreases as the number of GPUs increases, approaching the ideal linear speedup ratio. With the continuous increase of K value, the deterministic information input into the tag BN will be reduced, thus reducing the classification accuracy. When K = 3, MLBN can provide the best data analysis performance. On genbase dataset, the accuracy of MLBN is 0.982 +/- 0.013. Through experiments, the BIM BD processing algorithm based on Bayesian Network Structural Learning (BNSL) helps decision-makers use complex data in smart cities efficiently.

A delayed modulation of solar ultraviolet radiation on the COVID-19 transmission reflects an incubation period

Laboratory experiments have revealed the meteorological sensitivity of the coronavirus disease 2019 (COVID-19) virus. However, no consensus has been reached about how outdoor meteorological conditions modulate the virus transmission as it is also constrained by non-meteorological conditions. Here, we identify the outbreak's evolution stage, constrained least by non-meteorological conditions, by searching the maximum correlation coefficient between the ultraviolet flux and the growth rate of cumulative confirmed cases at the country level. At this least-constrained stage, the cumulative cases count around 1300-3200, and the count's daily growth rate correlates with the ultraviolet flux and temperature significantly (correlation coefficients r = -0.54 +/- 0.09 and -0.39 +/- 0.10 at p<0.01$$ p, respectively), but not with precipitation, humidity, and wind. The ultraviolet correlation exhibits a delay of about 7 days, providing a meteorological measure of the incubation period. Our work reveals a seasonality of COVID-19 and a high risk of a pandemic resurgence in winter, implying a need for seasonal adaption in public policies.

Current status and bottlenecks of global pharmaceutical developments against COVID-19

The outbreaks of severe acute respiratory syndrome (SARS) in February 2003 in Guangdong, China, middle east respiratory syndrome (MERS) in September2012 in Kingdom of Saudi Arabia, and the current COVID-19 pandemics in December 2019 in Wuhan, China, are all caused by coronaviruses, and patients primarily died of acute respiratory distress syndrome (ARDS). Compared with more than 5 years of wreaking havoc from MERS-CoV and Ebor, China successfully contains the SARS-CoV within one year, which shows her advantages in political governance controlling such pandemics. Many coronaviruses have been separated and their molecular structures analyzed. However, there is no specific anti-coronavirus drug developed in the world since the outbreaks. The problems come from not only pharmaceutical technology per se that must treat both coronaviruses and their life-threatening ARDS, but also the small size of patients who could immune against the coronaviruses after infections resulting in pharmaceutical reluctance to invest in the area. Facing both the pharmaceutical and social-economic bottlenecks, here, we summarized the current development of anti-coronavirus drugs, and proposed the strategies of repurposing existing drugs and preparing their pharmacological combinations to fight the viruses including COVID-19 based on a well-understanding of how the coronaviruses enter the host and damage our respiratory system.