A parallel 2D code for modeling nanosecond surface dielectric barrier discharge (nSDBD), combining a discharge description, detailed kinetics and hydrodynamics, is developed and validated. A series of experiments and numerical modeling for a single pulse nSDBD in atmospheric pressure air at a voltage amplitude of 24 kV have been performed. The measured and calculated velocity of the discharge front, electrical current, 2D map of N 2) emission and hydrodynamic perturbations caused by the discharge on the time scale -0.2 5 μs are compared. The data are presented and analyzed for the negative and positive polarity of the streamers. A set of parametric calculations with different dielectric permittivities and different dielectric thicknesses is presented.

Background and purposeStroke assistance is facing changes and new challenges since COVID‐19 became pandemic. A variation on the patient influx might be one of the greater concerns, due to fewer people coming to emergency departments or coming too late. However, no data quantifying this have been published until now. The aim was to analyse the impact of the COVID‐19 epidemic outbreak on hospital stroke admissions and their characteristics in our region.MethodsThe data of every patient admitted to any hospital of our healthcare system with a diagnosis of ischaemic stroke between 30 December 2019 and 19 April 2020 were reviewed. Demographic and clinical data were recorded and compared between periods before and after the setting of the state of emergency secondary to the COVID‐19 outbreak.ResultsIn total, 354 patients with ischaemic stroke were admitted in our study period. There was a weekly average of 27.5 cases before the setting of the state of emergency against 12 afterwards (P < 0.001). This drop in stroke cases occurred progressively from week 11, persisting in time despite the decrease in confirmed cases of COVID‐19. No differences in the proportion of intravenous thrombolysis (21.1% vs. 21.5%, P = 0.935) or endovascular therapy (12.4% vs. 15.2%, P = 0.510) were found, nor in other demographic or clinical characteristics except for median onset‐to‐door time (102 vs. 183 min, P = 0.015).ConclusionsThis observational study offers the perspective of a whole region in one of the countries more heavily stricken by the SARS‐CoV‐2 epidemic and shows that the decrease of stroke events, since the beginning of the COVID‐19 outbreak, happened globally and without any specific patient distribution.

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This paper will demonstrate a machine learning application for predicting positive lead conversion events on the Edmunds.com website, an American destination for car shopping. A positive conversion event occurs when a user fills out and submits a lead form interstitial. We used machine learning to identify which users might want to fill out lead forms, and where in their sessions to present the interstitials. There are several factors that make these predictions difficult, such as (a) far more negative than positive responses (b) seasonality effects due to car sales events near holidays, which require the model to be easily tunable and (c) the need for computationally fast predictions for real-time decision-making in order to minimize any impact on the website’s usability. Rather than develop a single highly complex model, we used an ensemble of three simple models: Naive Bayes, Markov Chain, and Vowpal Wabbit. The ensemble generated significant lift over random predictions and demonstrated comparable accuracy to an external consulting company’s model.

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