The flashover performance of insulators is the foundation of the calculation of precise lightning protection. The material and structure of an insulator affect its flashover performance but the research on the differences in the flashover performance of different insulators and the analysis of the reason for these differences have not been clear. In this paper, lightning impulse flashover tests of two types of insulators (composite and glass insulators) were carried out in the National Engineering Laboratory (Kunming) for Ultrahigh-voltage Engineering Technology. In these tests, the insulators were hung in a 110 kV double-circuit transmission tower and a standard lightning impulse (1.2/50 μs) was applied to the insulators. The discharge path and the 50% impulse flashover voltage of the insulators were recorded. At the same time, the electric fields in the vicinity of different insulators were calculated using the finite element method with COMSOL Multiphysics. The electric field distribution and the uneven coefficient were analyzed. Combined with the flashover test data and the electric field simulation, the relationship between the flashover performance (discharge path and 50% impulse flashover voltage) and the electric field (electric field distribution and uneven coefficient) is found. The simulation results are in accordance with the test data and the conclusions can provide useful references for the external insulation design of transmission lines and the optimization of insulators.

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

Lightning strike is the main reason for the transmission line tripping. Research on the insulator flashover characteristics under the lightning impulse and the accurate flashover criterions are the basis for correctly evaluating the lightning resistance performance of the transmission line. In this paper, an experimental study on the flashover characteristics of the insulators with different materials was carried out on the true transmission line tower. The insulators were hung on the umbrella-shape tower and the drum-shape tower in the test. Both the standard lightning impulse (1.2/50 μs) and the short-tail lightning impulse (1/10 μs) were selected. The flashover test of a single composite insulator was also carried out for comparatively analyzing the influence of the true tower on the insulator flashover performance. According to the test results on the true tower, the lightning flashover criterions of the insulators with different materials are given. The influencing factors of the insulator flashover characteristics, including the impulse waveform, the insulator material, and the tower type and the insulator suspension position, were further compared and analyzed. Meanwhile, the results of the single insulator test proved that the true tower has great influence on the insulator flashover characteristics and that the flashover test in the true tower can reflect actual situation better and obtain more accurate results. In addition, based on the simulation analysis results of the electric-field distribution around the insulators, the difference of 50% impulse flashover voltage (U50%) of the insulators in different conditions were partly explained.

The observation of the air gap discharge process is the basis of revealing the physical mechanism of gas discharge and establishing the accurate numerical simulation model. In order to obtain the variation characteristics at different development stages during the air gap discharge processes under positive and negative lightning impulses in this paper, repetitive discharge tests of 1 m rod-plate air gap under standard lightning impulse were carried out in the National Engineering Laboratory (Kunming) for Ultrahigh-voltage Engineering Technology. An improved discharge observation method, using electron-multiplying intensified charge-coupled device (EMICCD) camera, was adopted in the test. A series of discharge spatial-temporal distribution images with nanosecond exposure time and interval time were captured by changing the shooting time delay of the EMICCD in repetitive discharges. The complete discharge development process was reproduced by the image stitching. The development process and the characteristics of the air gap discharge under lightning impulse were analyzed qualitatively. The characteristics of the measured discharge current under the positive lightning impulse were discussed. The testing results and the analysis show that the shape and the development process of the ionization region of the streamer are quite different under different lightning impulses. There are spherical and fan-shaped streamer regions under the positive and negative lightning impulse, respectively. After the streamer runs through the entire gap, the leader develops from the rod electrode to the plate electrode (under the positive impulse) or from both the rod and the plate electrode to the middle area (under the negative impulse). Then the final jump occurs and the gap is broken down. If the streamer still cannot penetrate the entire gap when the applied impulse voltage exceeds the peak, the gap will not be broken down finally.

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.