Experimental observations and simulation results regarding a pure He atmospheric pressure plasma jet (APPJ) and He + N 2 APPJs interacting with a downstream dielectric substrate are presented in this paper. Experiments utilizing spatialtemporal imaging show that, in the case of the pure He APPJ, an annular plasmasubstrate interaction pattern is formed. With the introduction of N 2 , the plasma is more uniformly distributed on the substrate surface, appearing a solid interaction pattern. The experimental measurements indicate 0.5% N 2 mixture is the optimal condition to achieve the most intense discharge, while the plasma substrate contact area is slightly reduced by 6.1% in comparison to that of the pure He APPJ. A 2D selfconsistent fluid model is constructed to provide insights into the role of the addition of trace of N 2 on the discharge dynamics. The discharge morphologies predicated by the model is in principle consistent with the experimental observations. The simulation reveals that the conversion from the annular plasmasubstrate interaction pattern to the solid one is attributed to the synthetic effect of the addition of N 2 and the presentence of the substrate acting as the cathode to enhance the local electric field. In the solid interaction pattern, the Penning ionization makes a significant contribution to the surface discharge, especially in the afterglow region. The dominant positive ions (N + 2 and N + 4 ) and the reactive oxygen and nitrogen species including O and N gain remarkable increment in the flux intensity to the central surface, which merits great application potential.

A two-dimensional (2D) fluid model is presented to investigate the spatiotemporal generation and dynamic mechanics of dielectric barrier columnar discharges in atmospheric helium. The model was examined with discharge currents measured in experiments and images taken by an intensified charge couple device camera. Based on the model, a columnar discharge was simulated for several cycles after being ignited. The discharge could be regarded as an initial unstable stage for the first three and a half cycles, then a steady state for the following cycles. In the initial stage, the discharge evolves from a uniform pattern into a columnar one. The calculated equipotential lines, 2D radial electric field, and electron density distributions at the edge of uniform discharges show the radial electric field accounts for the shrinking discharge area and the formation of discharge columns in the end. The columnar glow discharges and the Townsend discharges beyond the columns could coexist in the initial stage, and a Townsend discharge might develop into a new glow column in the next half-cycle. The radial electric field surrounding a glow discharge column has an inhibiting effect on the ionization in the peripheral area.

Background Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. Objective The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. Methods Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. Conclusions Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.

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