In recent years, DC fault arc detection has been an electrical engineering research hotspot. At present, most proposed detection methods do not analyze the effects of fault arc electrical characteristics on both line current and supply voltage. Therefore, this study extensively analyzes variations of the line current and supply voltage because of DC arc faults based on the volt-ampere characteristics of DC arc faults. Then, a DC series arc fault detection method is proposed that comprehensively uses information on line current and supply voltage. An experimental platform for DC fault arc generation and detection was established using a DC-DC converter and a photovoltaic power supply as DC power supplies, and the proposed method was confirmed by experiments using this platform. Experimental results demonstrate that the proposed method can effectively distinguish arc faults and has the characteristics of clear physical meaning while maintaining a low amount of calculation. INDEX TERMS DC series arc, volt-ampere characteristic, electrical fault detection, current, voltage, chaotic characteristics, drop rate, change rate.

The influence of a series arc on line current is different with different loads, which makes it difficult to accurately extract arc fault characteristics suitable for all loads according to line current signal. To improve the accuracy of arc fault detection, a series arc fault detection method based on category recognition and an artificial neural network is proposed on the basis of analyzing the current characteristics of arc faults under different loads. According to the waveform of current and voltage, the load is divided into three types: Resistive category (Re), resistive-inductive category (RI), and rectifying circuit with a capacitive filter category (RCCF). Based on the wavelet transform, the characteristics of line current in the time domain and frequency domain when the series arc occurs under different types of loads are analyzed, and then the time and frequency indicators are taken as the inputs of the artificial neural network to establish three-layer neural networks corresponding to three types of loads to realize the detection of the series arc fault of lines under different categories of loads. To avoid the neural network falling into a local optimum, the initial weight and threshold of the neural network are optimized by a genetic algorithm, which further improves the accuracy of the neural network in arc identification. The experimental results show that the proposed arc detection method has the advantages of high recognition rate and a simple neural network model.

Numerous investigations of supershear earthquakes make a conclusion that a supershear earthquake produces a seismic shock wave on the ground that may increase the resulting destruction. We investigate a supershear rupture promoted by the free surface and find out that although the seismic energy of a supershear earthquake can be transmitted further with large amplitudes, the peak slip velocity on a fault near the free surface is smaller than that caused by a subshear rupture earthquake. Our results show that the free‐surface‐induced supershear rupture mitigates the amplitudes of ground motions near the fault plane compared with the subshear rupture. The Coulomb failure change derived from dynamic modeling further suggests that this free‐surface‐induced supershear reduces aftershock potential compared to a subshear rupture. Both ground motion at near‐fault and aftershock possibility show low risk for the free‐surface‐induced supershear rupture earthquake than subshear earthquake, contrary to the traditional concept.

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features and serological markers to estimate overall survival (OS) in non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection.Methods: The prognostic model was generated by using Lasso regression in training cohort. The incremental predictive value of the model to traditional TNM staging and clinical treatment for individualized survival was evaluated by concordance index (C-index), time-dependent ROC (tdROC), and decision curve analysis (DCA). A model risk score nomogram for OS was built by combining TNM staging and clinical treatment. Then we stratified patients into high and low risk subgroups according to the model risk score. Difference in survival between subgroups was analyzed using Kaplan–Meier survival analysis. Furthermore, correlations between the prognostic model and TNM staging or treatment were analysed.Results: The C-index values of the model for predicting OS were 0.769 and 0.676 in the training and validation cohorts, respectively, which were higher than that of TNM staging, and treatment, the tdROC curve and DCA also showed the model had good predictive accuracy and discriminatory power than TNM staging and treatment. And the nomogram shown some clinical net benefit. According to the model risk score, we divided the patients into low risk and high risk subgroups. The differences of OS rates were significant in the subgroups. Furthermore, the model was positive correlation with TNM staging and treatment. Conclusions: The proposed prognostic model showed favorable performance than traditional TNM staging and clinical treatment for estimating OS in NSCLC (HBV+) patients.