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Commercial airliners have played an important role in spreading the SARS-CoV-2 virus worldwide. This study used computational fluid dynamics (CFD) to simulate the transmission of SARS-CoV-2 on a flight from London to Hanoi and another from Singapore to Hangzhou. The dispersion of droplets of different sizes generated by coughing, talking, and breathing activities in a cabin by an infected person was simulated by means of the Lagrangian method. The SARS-CoV-2 virus contained in expiratory droplets traveled with the cabin air distribution and was inhaled by other passengers.Infection was determined by counting the number of viral copies inhaled by each passenger. According to the results, our method correctly predicted 84% of the infected/ uninfected cases on the first flight. The results also show that wearing masks and reducing conversation frequency between passengers could help to reduce the risk of exposure on the second flight.

The capability of image deraining is a highly desirable component of intelligent decision-making in autonomous driving and outdoor surveillance systems. Image deraining aims to restore the clean scene from the degraded image captured in a rainy day. Although numerous single image deraining algorithms have been recently proposed, these algorithms are mainly evaluated using certain type of synthetic images, assuming a specific rain model, plus a few real images. It remains unclear how these algorithms would perform on rainy images acquired "in the wild" and how we could gauge the progress in the field. This paper aims to bridge this gap. We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images of various rain types. This dataset highlights diverse rain models (rain streak, rain drop, rain and mist), as well as a rich variety of evaluation criteria (full-and no-reference objective, subjective, and task-specific). We further provide a comprehensive suite of criteria for deraining algorithm evaluation, including full-and no-reference metrics, subjective evaluation, and the novel task-driven evaluation. The proposed benchmark is accompanied with extensive experimental results that facilitate the assessment of the state-of-the-arts on a quantitative basis. Our evaluation and analysis indicate the gap between the achievable performance on synthetic rainy images and the practical demand on real-world images. We show that, despite many advances, image deraining is still a largely open problem. The paper is concluded by summarizing our general observations, identifying open research challenges and pointing out future directions. Our code and dataset is publicly available at http://uee.me/ddQsw. Keywords Image deraining • Image quality assessment • Deep convolution network • Benchmark analysis Communicated by Torsten Sattler.

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