It has been confirmed that the coronavirus disease 2019 (COVID-19) can transmit through droplets created when an infected human coughs or sneezes. Accordingly, 1.83-m (6-feet) social distancing is advised to reduce the spread of the disease among humans. This is based on the assumption that no air circulation exists around people. However, it is not well investigated whether the ambient wind and relative humidity (RH) will cause SARS-CoV-2 laden droplets to transport farther in the air, and make the current social distancing policy insufficient. To provide evidence and insight into the "social distancing" guidelines, a validated computational fluidparticle dynamics (CFPD) model was employed to simulate the transient transport, condensation/evaporation, and deposition of SARS-CoV-2 laden droplets emitted by coughs, with different environmental wind velocities and RHs. Initial droplet diameters range from 2 to 2000 μm, and the wind velocities range from 0 to 16 km/h, representing different wind forces from calm air to moderate breeze. The comparison between a steady-state wind and a gust with a constant frequency has also been performed. Ambient RHs are 40% and 99.5%. The distances between the two virtual humans are 1.83 m and 3.05 m (6 feet and 10 feet). The facial covering effect on reducing the airborne transmission of the cough droplets has also been evaluated. Numerical results indicate that the ambient wind will enhance the complexity of the secondary flows with recirculation between the two virtual humans. Microdroplets follow the airflow streamlines well and deposit on both human bodies and head regions, even with the 3.05-m (10-feet) separation distance. The rest of the microdroplets can transport in the air farther than 3.05 m (10 feet) due to wind convection, causing a potential health risk to nearby people. High RH will increase the droplet sizes due to the hygroscopic growth effect, which increases the deposition fractions on both humans and the ground. With the complex environmental wind and RH conditions, the 6feet social distancing policy may not be sufficient to protect the inter-person aerosol transmission, since the suspending micro-droplets were influenced by convection effects and can transport from the human coughs/sneezes to the other human in less than 5 seconds. Due to the complex real-world environmental ventilation conditions, a social distance longer than 1.83 m (6 feet) needs to be considered. Wearing masks should also be recommended for both infected and healthy humans to reduce the airborne cough droplet numbers.

Nanofluids, i.e., well-dispersed (metallic) nanoparticles at low- volume fractions in liquids, may enhance the mixture's thermal conductivity, knf, over the base-fluid values. Thus, they are potentially useful for advanced cooling of micro-systems. Focusing mainly on dilute suspensions of well-dispersed spherical nanoparticles in water or ethylene glycol, recent experimental observations, associated measurement techniques, and new theories as well as useful correlations have been reviewed.It is evident that key questions still linger concerning the best nanoparticle-and-liquid pairing and conditioning, reliable measurements of achievable knf values, and easy-to-use, physically sound computer models which fully describe the particle dynamics and heat transfer of nanofluids. At present, experimental data and measurement methods are lacking consistency. In fact, debates on whether the anomalous enhancement is real or not endure, as well as discussions on what are repeatable correlations between knf and temperature, nanoparticle size/shape, and aggregation state. Clearly, benchmark experiments are needed, using the same nanofluids subject to different measurement methods. Such outcomes would validate new, minimally intrusive techniques and verify the reproducibility of experimental results. Dynamic knf models, assuming non-interacting metallic nano-spheres, postulate an enhancement above the classical Maxwell theory and thereby provide potentially additional physical insight. Clearly, it will be necessary to consider not only one possible mechanism but combine several mechanisms and compare predictive results to new benchmark experimental data sets.

The images that are captured in sand storms often suffer from low contrast and serious color cast that are caused by sand dust, and these issues will have significant negative effects on the performance of an outdoor computer vision system. To address these problems, a method based on halo-reduced dark channel prior (DCP) dehazing for sand dust image enhancement is proposed in this paper. It includes three components in sequence: color correction in the LAB color space based on gray world theory, dust removal using a halo-reduced DCP dehazing method, and contrast stretching in the LAB color space using a Gamma function improved contrast limited adaptive histogram equalization (CLAHE), in which a guided filter is used to improve the artifacts of the histogram equalization. Experiments on a large number of real sand dust images demonstrate that the proposed method can well remove the overall yellowing tone and dust haze effect and obtain normal visual colors and a detailed clear image. INDEX TERMSNormalized Gamma correction (NGC), DCP, CLAHE, color correction, LAB color space, illumination enhancement. ZHENGHAO SHI received the B.S. degree in material science and engineering from Dalian Jiaotong University, Dalian, China, in 1995, the M.S. degree in computer application technology from the

No abstract