Effects of rough surface on sound propagation in shallow water

Underwater acoustic applications depend critically on the prediction of sound propagation, which can be significantly affected by a rough surface, especially in shallow water. This paper aims to investigate how randomly fluctuating surface influences transmission loss(TL) in shallow water. The one-dimension wind-wave spectrum, Monterey–Miami parabolic equation(MMPE) model, Monte Carlo method, and parallel computing technology are combined to investigate the effects of different sea states on sound propagation. It is shown that TL distribution properties are related to the wind speed,frequency, range, and sound speed profile. In a homogenous waveguide, with wind speed increasing, the TLs are greater and more dispersive. For a negative thermocline waveguide, when the source is above the thermocline and the receiver is below that, the effects of the rough surface are the same and more significant. When the source and receiver are both below the thermocline, the TL distributions are nearly the same for different wind speeds. The mechanism of the different TL distribution properties in the thermocline environment is explained by using ray theory. In conclusion, the statistical characteristics of TL are affected by the relative roughness of the surface, the interaction strength of the sound field with the surface, and the changes of propagating angle due to refraction.

A five-compartment model of age-specific transmissibility of SARS-CoV-2

Background:The novel coronavirus,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2,also called 2019-nCoV)causes different morbidity risks to individuals in different age groups.This study attempts to quantify the age-specific transmissibility using a mathematical model.Methods:An epidemiological model with five compartments(susceptible-exposed-symptomatic-asymptomatic-recovered/removed[SEIAR])was developed based on observed transmission features.Coronavirus disease 2019(COVID-19)cases were divided into four age groups:group 1,those≤14years old;group 2,those 15 to 44years old;group 3,those 45 to 64years old;and group 4,those≥65 years old.The model was initially based on cases(including imported cases and secondary cases)collected in Hunan Province from January 5 to February 19,2020.Another dataset,from Jilin Province,was used to test the model.Results:The age-specific SEIAR model fitted the data well in each age group(P<0.001).In Hunan Province,the highest transmissibility was from age group 4 to 3(median:β43=7.71×10-9;SAR43=3.86×10-8),followed by group 3 to 4(median:β34=3.07×10-9;SAR34=1.53×10-8),group 2 to 2(median:β22=1.24×10-9;SAR22=6.21×10-9),and group 3 to 1(median:β31=4.10×10-10;SAR31=2.08×10-9).The lowest transmissibility was from age group 3 to 3(median:β33=1.64×10-19;SAR33=8.19×10-19),followed by group 4 to 4(median:β44=3.66×10-17;SAR44=1.83×10-16),group 3 to 2(median:β32=1.21×10-16;SAR32=6.06×10-16),and group 1 to 4(median:β14=7.20×10-14;SAR14=3.60×10-13).In Jilin Province,the highest transmissibility occurred from age group 4 to 4(median:β43=4.27×10-8;SAR43=2.13×10-7),followed by group 3 to 4(median:β34=1.81×10-8;SAR34=9.03×10-8).Conclusions:SARS-CoV-2 exhibits high transmissibility between middle-aged(45 to 64 years old)and elderly(≥65 years old)people.Children(≤14 years old)have very low susceptibility to COVID-19.This study will improve our understanding of the transmission feature of SARS-CoV-2 in different age groups and suggest the most prevention measures should be applied to middle-aged and elderly people.