Kinetics of Spinodal Phase Separation in Unstable Thin Liquid Films

We study universality in the kinetics of spinodal phase separation in unstable thin liquid films, via simulations of the thin film equation. It is shown that in addition to morphology and free energy,the number density of local maxima in the film profile can also be used to identify the early, intermediate and late stages of spinodal phase separation. A universal curve between the number density of local maxima and rescaled time describes the kinetics of early stage in d = 2, 3. The Lifshitz-Slyozov exponent of -1/3 describes the kinetics of the late stage in d = 2 even in the absence of coexisting equilibrium phases.Comment: 5 figure

Adaptive selection of sampling points for uncertainty quantification

We present a simple and robust strategy for the selection of sampling points in Uncertainty Quantification. The goal is to achieve the fastest possible convergence in the cumulative distribution function of a stochastic output of interest. We assume that the output of interest is the outcome of a computationally expensive nonlinear mapping of an input random variable, whose probability density function is known. We use a radial function basis to construct an accurate interpolant of the mapping. This strategy enables adding new sampling points one at a time, adaptively. This takes into full account the previous evaluations of the target nonlinear function. We present comparisons with a stochastic collocation method based on the Clenshaw-Curtis quadrature rule, and with an adaptive method based on hierarchical surplus, showing that the new method often results in a large computational saving.Comment: 22 pages, 15 figures; to appear in Int. J. Uncertainty Quantificatio

Coupling discharge and gas dynamics in streamer-less spark formation on the example of supercritical N2

A 2D cylindrically symmetric model is developed to study the streamer-less spark formation in a short gap on the timescale of ion motion. It incorporates the coupling between the electric discharge (described by reaction-drift-diffusion model on the timescale of ion motion and Poisson's equation) and the gas (described by Euler equations) through the heat generated by the discharge. The model is employed to study electrical breakdown in supercritical N2. We present the simulation results of gas heating by the electrical discharge and the effect of gas expansion on the electrical discharge

Coupling discharge and gas dynamics in streamer-less spark formation in supercritical N2

A two-dimensional cylindrically symmetric model is developed to study the streamer-less spark formation in a short gap on the timescale of ion motion. It incorporates the coupling between the electric discharge and the gas through the heat generated by the discharge and the consecutive gas expansion. The model is employed to study electrical breakdown in supercritical N 2 . We present the simulation results of gas heating by the electrical discharge and the effect of gas expansion on the electrical discharge

Epi Evident: Biosurveillance to Monitor, Compare, and Forecast Disease Case Counts

ObjectiveEpi Evident is a web based application built to empower public health analysts by providing a platform that improves monitoring, comparing, and forecasting case counts and period prevalence of notifiable diseases for any scale jurisdiction at regional, country, or global-level. This proof of concept application development addresses improving visualization, access, situational awareness, and prediction of disease behavior.IntroductionThe Epi Evident application was designed for clear and comprehensive visualization for monitoring, comparing, and forecasting notifiable diseases simultaneously across chosen countries. Epi Evident addresses the taxing analytical evaluation of how diseases behave differently across countries. This application provides a user-friendly platform with easily interpretable analytics which allows analysts to conduct biosurveillance with minimal user tasks. Developed at the Pacific Northwest National Laboratory (PNNL), Epi Evident utilizes time-series disease case count data from the Biosurveillance Ecosystem (BSVE) application Epi Archive (1). This diverse data source is filtered through the flexible Epi Evident workflow for forecast model building designed to integrate any entering combination of country and disease. The application aims to quickly inform analysts of anomalies in disease & location specific behavior and aid in evidence based decision making to help control or prevent disease outbreaks.MethodsA workflow was constructed to define the best disease forecast model for each location based on an adjustable method approach. The differences in disease behavior across countries was achieved through a React/Python application with a user-friendly output for monitoring and comparing different combinations.The forecast model building workflow consisted of three major steps to determine the best fit model for a given disease-country pair: data type, model type, and model comparison & selection. Testing various disease-country combinations allowed for direct evaluation of the workflow efficiency, flexibility, and criteria for determining the best fit model. Data type was characterized as either seasonal, cyclic, or sporadic. Depending on data type, a specific time series forecasting model was applied. In general, seasonal or cyclic data required either an Auto-Regression Integrated Moving Average (ARIMA) model or a Seasonal Auto-Regression Integrated Moving Average (SARIMA) model while sporadic datasets employed a Poisson model. Several model candidates for a single country and disease combination were then compared to determine which was the best fit model. ARIMA and SARIMA model selection criteria included their respective order significance, residual diagnostics, and lowest possible combination of Akaike Information Criterion and Root Mean Square Error (RMSE) values. Poisson model selection criteria involved Poisson or negative binomial distribution and event probability, lag dependency of immediate past events or seasonality, and lowest possible RMSE. To enhance the user’s monitoring and comparisons across multiple countries and diseases, each forecasted case counts supplied a corresponding period prevalence. This period prevalence was calculated by dividing the case counts by the population in the selected country and timeframe. Population records were obtained through the public World Health Organization database (2).ResultsA variety of visualization tools on Epi Evident allows convenient interpretation on behaviors of diseases spanning multiple countries simultaneously (Figure 1). Countries, diseases, and timeframe are selected and displayed within a matrix alongside with their corresponding forecasts for case counts and period prevalence. By providing this full representation, users can easily interpret and anticipate disease behavior while monitoring, comparing, and forecasting case counts and period prevalence across multiple countries. For future work, the Epi Evident workflow can be scaled to accommodate any disease-country combination with automated model selection to allow easier and more efficient biosurveillance.ConclusionsEpi Evident empowers analysts to visualize, monitor, compare, and forecast disease case counts and period prevalence across countries. Epi Evident exemplifies how filtering diverse data through a flexible workflow can be scalable to output distinctive models for any given country and disease combination. Thus, providing accurate forecasting and enhanced situational awareness throughout the globe. Implementing this application’s methodology helps enhance and expand biosurveillance efficacy for multiple diseases across multiple countries simultaneously.References1. Generous Nicholas, Fairchild Geoffrey, Khalsa Hari, Tasseff Byron, Arnold James. Epi Archive: An automated data collection of notifiable disease data. Online Journal of Public Health Informatics. 2017. 9(1):e372. http://apps.who.int/gho/data/view.main.POP2040?lang=en Accessed: 6/20/201