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The first arrival time of seismic P- and S- waves affects the determination of the beginning of an earthquake by the Earthquake Early Warning System for High-speed Rail (HSREEWs). Accordingly, we propose an approach to picking P- and S-waves arrival time for HSREEWs. This approach can treat the continuous seismic signals in real-time without prior knowledge of whether an earthquake occurs. Firstly, we replace the fixed time window of the S transform (ST) with the dynamic window of the generalized S transform (GST) to make the necessary time of signal processing more flexible. Meanwhile, the GST extends the sequential signal to the time-frequency domain (TF), and combines the TF polarization to construct four three-component polarization filters. Accordingly, the P-wave and S-wave can be separated from the seismic waveform by those filters, which effectively avoids the phenomenon that the S-wave phase is difficult to determine because of the interference of S-wave overlapping in the coda region. Secondly, based on the instantaneous frequency of seismic signal, we construct a new STA/LTA (GSPA-STA/LTA) with the characteristic function (CF) of constrained TF spectrum to pick the first arrivals of the P- and S-waves, and provide the corresponding automatic picking algorithm. Finally, we analyze 504 seismic events larger than Ms40 near the Japanese Tohoku Shinkansen to comprehensively verify the effectiveness of our approach. Since the P-wave and S-wave are first separated, the subsequent process just has to identify the first arrival times of their respective components. The approach has a good practical value in maintaining the safe operation of high-speed rail under earthquake, which meets the industry demands for speed, scale and accuracy in HSREEWs. • The proposed generalized S-transform (GST) has better time resolution and better frequency resolution. • Four three-component polarization filters are constructed for separating the P- and S-waves in time-frequency domain. • A new STA/LTA with constrained time-frequency spectrum is designed for picking the arrival times. • The proposed approach has better performance on picking the arrival times of the P- and S-waves than revised STA/LTA, VAR-AIC, PphasePicker et al. [ABSTRACT FROM AUTHOR]

This paper aims to improve the problem of seismic signal enhancement and noise filtering by utilizing the generalized S-transform (GST) and nonlinear complex diffusion (NCD) methods. GST and its inverse transform (IGST) can decompose the original seismic signal into several sub-component signals in terms of the instantaneous frequencies to discern the distribution of various frequencies in the entire signal. The imaginary value generated by the NCD process, which can be used as the directional structure indicator to suppress the noise and enhance the effective signal. Therefore, we present a denoise method of seismic signal based on generalized S-transform and nonlinear complex diffusion (GSNCD). GSNCD, is a decomposition, denoise, and reconstruction method, with three key features: (1) A new GST transform is proposed under the GST framework, which uses the characteristic of the higher frequency resolution in high frequency and the higher time resolution in low frequency to decompose the signal into sub-component signals as different frequencies, and the sub-component signals are used to determine the structural tensor of seismic signal; (2) a fixed diffusion coefficient of complex diffusion is improved to adjust the adaptive iteration step size; (3) the sub-component seismic signals are calculated to avoid filtering deviation caused by fixed inclination angle. Finally, we reconstruct the processed sub-component signals into a complete signal. The simulations and real example fully verify the effectiveness of GSNCD in seismic signal denoising, with particular improvements in the attenuation at the data edge. In the ground motion signal test, the signal-to-noise ratio (SNR) after denoising by GSNCD increased from −2.50 dB to 13.83 dB. In two scenarios tests of exploration signals, the SNR of GSNCD is 12.09 dB and 11.19 dB, respectively, demonstrating better relative error and stability compared to other denoise methods such as Wavelet, empirical mode decomposition (EMD), adaptive NCD (ANCD), damped rank-reduction (DRR) and structural filtering method applied in the low-SNR seismic signal. • The proposed GST has better time resolution at low frequency than ST and GST. • The diffusion coefficient of nonlinear complex diffusion (NCD) is improved. • The inclination angle of each sub-component signal can be calculated through the matrix of structural tensor. • The proposed method has better performance on denoising than GST/Wavelet/EMD/ANCD/DRR/structural filtering method. [ABSTRACT FROM AUTHOR]

Aiming to solve the safety problems of high-speed railway operation and management, one new method is urgently needed to construct on the basis of the rough set theory and the uncertainty measurement theory. The method should carefully consider every factor of high-speed railway operation that realizes the measurement indexes of its safety operation. After analyzing the factors that influence high-speed railway safety operation in detail, a rough measurement model is finally constructed to describe the operation process. Based on the above considerations, this paper redistricts the safety influence factors of high-speed railway operation as 16 measurement indexes which include staff index, vehicle index, equipment index and environment. And the paper also provides another reasonable and effective theoretical method to solve the safety problems of multiple attribute measurement in high-speed railway operation. As while as analyzing the operation data of 10 pivotal railway lines in China, this paper respectively uses the rough set-based measurement model and value function model (one model for calculating the safety value) for calculating the operation safety value. The calculation result shows that the curve of safety value with the proposed method has smaller error and greater stability than the value function method’s, which verifies the feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

• An early warning process for the rainstorm disaster is designed. • An HSR operation program with different rainstorm degrees is given out. • A data perception model of rainstorm is proposed with correction coefficients. In order to solve the safety operation problems of High-speed rail (HSR) in different areas and different sections under the rainstorm condition, an early warning process for the rainstorm disaster is designed. Furthermore, in order to control the operation risk, a HSR operation program with different rainstorm degrees is given out based on the analysis of rainstorm warning mechanism and rainstorm warning threshold in this paper. In addition, considering the reality that natural conditions vary greatly and the rainfall is very uneven, a data perception model of rainstorm (DPM) is proposed with correction coefficients for solving the calculation problem of precipitation for rainstorm warning. The DPM mainly adopts Paulhus's empirical equation and uses the linear function to improve it for calculating the precipitation, which is able to calculate the hourly precipitation in different regional environments, and also effectively evaluate the rainstorm warning level of high-speed rail in this period. It can calculate and monitor the process by big data and MATLAB. The result of case analysis shows that the DPM has good practical value for solving the safety operation problem of HSR in different areas under rainstorm environment. [ABSTRACT FROM AUTHOR]

This paper identifies salient beliefs that influence e-bike couriers' traffic violation behavior based on the theory of planned behavior (TPB). Two surveys were conducted in Nanjing, China, in 2018. The first survey extracted the key psychological beliefs, which were used to design a questionnaire. The second survey assessed TPB components and reported e-bike couriers' traffic violation behavior. A structural equation model was adopted to analyze the data. The results revealed that attitudes, descriptive norms, and perceived behavioral control explained 55.7% of the variance in intention to perform traffic violation behavior, and intentions together with perceived behavior control accounted for 28.5% of the variance in self-reported violation riding behavior. All of the belief composites had strong direct impacts on their respective TPB constructs. Salient beliefs were applied to develop effective intervention strategies. Age, education level, whether one possessed a driver's license, and past traffic violation behaviors had significant effects on belief composites and behavior. The quantitative analysis results obtained in the study can provide theoretical support for designing more effective interventions for reducing the traffic violation rate of e-bike couriers. [ABSTRACT FROM AUTHOR]

• Urbanization increases MTC ownership ratio. • As urbanization increases, MTC ownership and income become U-shaped. • Urbanization changes the MTC ownership and income inequality U-shaped relationship. Recent empirical research found that the motorcycle-to-car (MTC) ownership ratio and economic performance have an inverse U-shaped relationship, with the MTC ownership ratio increasing at lower economic levels but decreasing after a critical threshold. This study examined the effects of income per population, relative improvements in road mobility over road accessibility (MOA) and income inequality on the MTC ownership ratio under different levels of urbanization. A panel of 53 nations from 1963 to 2013 was analyzed using fixed-effects panel linear regression. The inverse U-shaped relationship between the MTC ownership ratio and income changes to a U-shaped relationship as urbanization rises. The growing rate of the MTC ownership ratio and the turning point for the inverse U-shaped relationship between the ratio and income inequality both rise along with urbanization. The negative effect of MOA on the ownership ratio of MTC is lessened by urbanization. [ABSTRACT FROM AUTHOR]

Azoreductase, a flavin mononucleotide-containing oxidoreductase fromEscherichia coli, can catalyze the reduction of azo dyes to form aromatic amine compounds. Few spectroscopic studies have explored the binding mode of azo dyes or the role of the arginine at site 59 in Azoreductase. In this article, protein engineering strategy has been used to construct one mutant in which the arginine residue at site 59 was mutated to glycine. Fluorescence spectroscopic data showed that the addition of Methyl Red and Methyl Orange resulted in the fluorescence quenching of the cofactor flavin mononucleotide bound to Azoreductase. The association constant was fitted using the standard binding equation instead of the Stern-Volmer equation. The results showed that the mutation from the arginine to glycine at site 59 weakened the association constant from 2.21 × 105 L.mol−1to 4.55 × 104 L.mol−1at 25°C. A similar phenomenon was also observed when Methyl Orange was used as a substrate. In each case, the association constant tended to decrease as the temperature increased from 25°C to 37°C. Thermodynamic parameter analysis revealed that the interaction type changed from a van der Waals interaction (between Azoreductase and the dyes) to a hydrogen bonding interaction (between the mutant and the dyes). Moleculcar docking was also performed in this work to provide some support for the binding mode and binding stability between Azoreductase/mutant and azo dyes. [ABSTRACT FROM PUBLISHER]