Simulation of Aerosol Evolution within Background Pollution for Nucleated Vehicle Exhaust via TEMOM

This work is intended to study the effect of background particles on vehicle emissions in representative realistic atmospheric environments. The coupling of Reynolds-Averaged Navier–Stokes equation (RANS) and Taylor-series Expansion Method Of Moments (TEMOM) is performed to track the emissions of the vehicle and simulating the evolution of the matters. The transport equation of mass, momentum, heat, and the first three orders of moments are taken into account with the effect of binary homogeneous nucleation, Brownian coagulation, condensation, and thermophoresis. The parameterization model is utilized for nucleation. The measured data for Beijing’s particle size distribution under both polluted and nonpolluted conditions are utilized as background particles. The relationship between the macroscopic measurement results and the microscopic dynamic process is analyzed by comparing the variation trend of several physical quantities in the process of aerosol evolution. It is found with an increase of background particle concentration, the nucleation is inhibited, which is consistent with the existing studies.

Characterizing bulk rigidity of rigid red blood cell populations in sickle-cell disease patients

In this work, we utilized a parameterization model of ektacytometry to quantify the bulk rigidity of the rigid red blood cell (RBC) population in sickle cell disease (SCD) patients. Current ektacytometry techniques implement laser diffraction viscometry to estimate the RBC deformability in a whole blood sample. However, the diffraction measurement is an average of all cells present in the measured sample. By coupling an existing parameterization model of ektacytometry to an artificially rigid RBC model, we formulated an innovative system for estimating the average rigidity of the rigid RBC population in SCD blood. We demonstrated that this method could more accurately determine the bulk stiffness of the rigid RBC populations. This information could potentially help develop the ektacytometry technique as a tool for assessing disease severity in SCD patients, offering novel insights into the disease pathology and treatment.

Understanding the Bias between the Number of Confirmed Cases and Actual Number of Infections in the COVID-19 Pandemic

The number of positive cases confirmed in the viral tests is a probe of the actual number of infections of COVID-19. The bias between these two quantities is a key element underlying the determination of some important parameters of this disease and the policy-making during the pandemic. To study the dependence of this bias on measured variables, we introduce a parameterization model that motivates a method of organizing the daily data of the numbers of the total tests, confirmed cases, hospitalizations and fatalities. After comparing with the historical data of the USA in the past few months, we find a simple formula relating these four variables. As a few applications, we show, among other things, how this formula can be used to project the number of actual infections, to provide guidance on how the test volume should be adjusted, and to derive an upper bound on the overall infection fatality rate of COVID-19 (< 0.64%, 95% C.L.) and a theoretical estimate of its value.

Parameterization of Wind Evolution using Lidar

Wind evolution refers to the change of the turbulence structure of the eddies over time while the eddies are advected by the main flow over space. With the development of the lidar-assisted wind turbine control, modelling of the wind evolution becomes an interesting topic, because the control system should only react to the changes in the wind field which can be predicted accurately over the distance to avoid harmful and unnecessary control action. This paper aims to achieve a parameterization model for the wind evolution model to predict the wind evolution model parameters according to the wind field conditions. For this purpose, a two-parameter wind evolution model suggested in literature was applied to model the wind evolution and the wind evolution was estimated using lidar data. A statistical analysis was done to reveal the characteristics of wind evolution model parameters. Gaussian process regression was applied to achieve the parameterization model. The results have proven the applicability of Gaussian process regression model to predict the wind evolution model parameters with sufficient accuracy.