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Introduction: The present study examines the fatigue reliability of subway tunnel invert structures subjected to train-induced loads, a critical factor in ensuring the safety and longevity of these vital infrastructures.Methods: By integrating higher-order moment theory, fatigue equations, and cumulative damage theory, this research analyzes the impact of train loads on invert structures, taking into account factors such as tension stress, compression stress, train axle weight, speed, and lining thickness.Results: The findings indicate that the reinforcement of the invert structure is required under tension stress, while it is not necessary for compression stress. Notably, the train’s axle weight and speed exert a significant influence on the fatigue reliability index, with speed displaying a marginally greater effect than axle weight. Conversely, the lining thickness demonstrates a negligible impact on the reliability index. As time progresses, the fatigue reliability index diminishes, resulting in an elevated probability of failure.Discussion: In light of these findings, it is imperative to conduct regular inspections and maintain the tensile state of the invert structure, which warrants the most attention. To safeguard the safety and longevity of subway tunnel invert structures, it is essential to concentrate on the aspects related to tensile stress and closely monitor train loads, specifically axle weight and speed.

This paper introduces some improved methods of imputation using higher order moment of an auxiliary variable while imputing missing values. The performance of the proposed imputation methods are investigated relative to the methods proposed by Mohamed, Sedory, and Singh (2016) and Bhushan and Pandey (2016, 2018). A comparative study has been carried out and it has been shown that the proposed methods perform better in comparision to methods proposed by Mohamed, Sedory, and Singh (2016) and Bhushan and Pandey (2016, 2018). The theoretical findings are supported by numerical study on two real populations and a simulation study using hypothetical population. [ABSTRACT FROM AUTHOR]

There are three distinguishing features in the financial time series, such as stock prices, are as follows: (1) Non-normality, (2) serial correlation, and (3) leverage effect. All three points need to be taken into account to model the financial time series. However, multivariate financial time series modeling involves a large number of stocks, with many parameters to be estimated. Therefore, there are few examples of multivariate financial time series modeling that explicitly deal with higher-order moments. Furthermore, there is no multivariate financial time series model that takes all three characteristics above into account. In this study, we propose the generalized orthogonal (GO)-Glosten, Jagannathan, and Runkle GARCH (GJR) model which extends the GO-generalized autoregressive conditional heteroscedasticity (GARCH) model and incorporates the three features of the financial time series. We confirm the effectiveness of the proposed model by comparing the performance of risk-based portfolios with higher-order moments. The results show that the performance with our proposed model is superior to that with baseline methods, and indicate that estimation methods are important in risk-based portfolios with higher moments.

This article is mainly devoted to pile defect identification and classification. The main objective of the work is to improve the pile features. Accordingly, a novel feature based on higher order statistical moments is proposed in this paper. The stress wave reflected signals are possessed by wavelet packet transform method. Three higher order statistical parameters--variance, skewness, and kurtosis are calculated in each wavelet packet band of the decomposed signals. The sliding window method is proposed to extract characteristics in every time interval. In particular, the Principal Component Analysis (PCA) is used to reduce dimension of the merged feature and eliminate the relevance among them. Then the features are fed into Support Vector Machine (SVM). Compared with three other existing features and the 42-dimensional feature, the multi-higher order moment feature achieves the highest classification accuracy which reaches 98%. The simulation results demonstrate that the proposed feature can be used as a suitable tool for pile defect detection. It is simple and effective. [ABSTRACT FROM AUTHOR]

Turbulent statistical properties in the outer region are presented for high Reynolds number turbulent pipe flow using LDV (Laser Doppler Velocimetry) measurement. Employing the indicator function for turbulence intensity, the region which has logarithmic behavior is not only observed in second moment but also higher even moments. It is found that power spectrum density distributions in these regions have approximately -1 law profile which mainly consists of LSM (Large Scale Motion) and VLSM (Very Large Scale Motion). However, the spectral dependence on the wall distance is different from based on Attached-Eddy model. We observed that the region where PDFs (Probability Density Function) are approximately invariant against the wall normal distance is enlarged with increasing Reynolds number. And, it is found that this invariant PDF region overlaps with logarithmic profiles of the even order moments. We clarified the relation between logarithmic behaviors in the even order moments and invariance property of PDF against the wall distance. However, in the finite Reynolds number experiment, it is reported that invariance property of PDF is approximately satisfied. And so, we derived the relation between the even moment profiles and invariance of PDF, and verified the relation using experimental results. Finally, we expected that, in the infinite Reynolds number condition, the invariance property of PDF and the logarithmic relations in the even moments are realized.

A unified approach based on truncated moment generating function (TMGF) is employed to derive the explicit expressions for channel capacity of log-normal channel under different adaptive transmission policy. The analysis is carried out assuming channel state information known to both transmitter and receiver. The efficacy of the approach is based on the fact it yields computationally convenient and efficient results in log-normal fading environment. Based on TMGF, a simple closed form expression for higher order moments of channel capacity for log-normal channels is obtained when channel state information is only known to receiver. Numerical computation in relation to simulation results for varying value of $$\sigma _{ dB}$$ over permissible range are carried out to validate the accuracy of derived expressions. [ABSTRACT FROM AUTHOR]

Abstract: An experimental study was carried out to focus the effect of introduction seepage on different turbulent characteristics for an open channel flow. Test results with both suction and injection is presented and discussed to understand the extent of influence of seepage in the depth-wise direction. Particular attention was given on mean velocity, turbulence intensity, Reynolds shear stress, shear stress correlation, higher order moments and quadrant analysis. Two different Reynolds numbers (Re =31000 and 47500) were used for each test condition. Reynolds numbers were chosen in order to keep flow condition as sub-critical (i.e. Froude numbers less than unity). The results from the analysis show that the effect of seepage on different turbulent characteristics is not restricted to the near-bed region but can be seen for most of the flow depth. Effect of injection on different turbulent characteristics is more evident with the lower flow rate, however the effect of suction seems to be independent of the flow rates. Results from the analysis of turbulent bursting events show clearly visible effect of seepage well beyond the near-bed region. [Copyright &y& Elsevier]

Abstract: A new higher order closure model for the stable boundary layer is presented and compared with Large Eddy Simulation data. The model includes numerical solutions for the mean values, second and third order moments equations. A satisfactory agreement is found between the calculated vertical profiles of the turbulent quantities with those provided by the LES. Furthermore the new model results are compared with profiles obtained with a lower order closure model in order to verify the effective importance of including third order dynamical equations in the model. [Copyright &y& Elsevier]

There are three distinguishing features in the financial time series, such as stock prices, are as follows: (1) Non-normality, (2) serial correlation, and (3) leverage effect. All three points need to be taken into account to model the financial time series. However, multivariate financial time series modeling involves a large number of stocks, with many parameters to be estimated. Therefore, there are few examples of multivariate financial time series modeling that explicitly deal with higher-order moments. Furthermore, there is no multivariate financial time series model that takes all three characteristics above into account. In this study, we propose the generalized orthogonal (GO)-Glosten, Jagannathan, and Runkle GARCH (GJR) model which extends the GO-generalized autoregressive conditional heteroscedasticity (GARCH) model and incorporates the three features of the financial time series. We confirm the effectiveness of the proposed model by comparing the performance of risk-based portfolios with higher-order moments. The results show that the performance with our proposed model is superior to that with baseline methods, and indicate that estimation methods are important in risk-based portfolios with higher moments. [ABSTRACT FROM AUTHOR]