Seismicity acceleration model and its application to several earthquake regions in China

With the theory of subcritical crack growth, we can deduce the fundamental equation of regional seismicity acceleration model. Applying this model to intraplate earthquake regions, we select three earthquake subplates: North China Subplate, Chuan Dian Block and Xinjiang Subplate, and divide the three subplates into seven researched regions by the difference of seismicity and tectonic conditions. With the modified equation given by Sornette and Sammis (1995), we analysis the seismicity of each region. To those strong earthquakes already occurred in these region, the model can give close fitting of magnitude and occurrence time, and the result in this article indicates that the seismicity acceleration model can also be used for describing the seismicity of intraplate. In the article, we give the magnitude and occurrence time of possible strong earthquakes in Shanxi, Ordos, Bole Tuokexun, Ayinke Wuqia earthquake regions. In the same subplate or block, the earthquake periods for each earthquake region are similar in time interval. The constant α in model can be used to describe the intensity of regional seismicity, and for the Chinese Mainland, α is 0.4 generally. To the seismicity in Taiwan and other regions with complex tectonic conditions, the model does not fit well at present.

Effects of Three Typical Resistivity Models on Pulsed Inductive Plasma Acceleration Modeling

The effects of three different typical resistivity models（Spitzer, Z＆L and M＆G） on the performance of pulsed inductive acceleration plasma are studied. Numerical results show that their influences decrease with the increase of the plasma temperature. The significant discriminations among them appear at the plasma temperature lower than 2.5 eV, and the maximum gap of the pulsed inductive plasma accelerated efficiency is approximately 2.5%.Moreover, the pulsed inductive plasma accelerated efficiency is absolutely related to the dynamic impedance parameters, such as voltage, inductance, capacitance and flow rate. However, the distribution of the efficiency as a function of plasma temperature with three resistivity models has nothing to do with the dynamic impedance parameter.

All-Atom Direct Folding Simulation for Proteins Using the Accelerated Molecular Dynamics in Implicit Solvent Model

We report the results of protein folding （219M, C34, N36, 2KES, 2KHK） by the method of accelerated molecular dynamics （aMD） at room temperature with the implicit solvent model. Starting from the linear structures, these proteins successfully fold to the native structure in a lO0-ns aMD simulation. In contrast, they are failed under the traditional MD simulation in the same simulation time. Then we find that the lowest root mean square deviations of helix structures from the native structures are 0.36 A, 0.63 A, 0.52 A, 1.1 A and 0.78 A. What is more, native contacts, cluster and free energy analyses show that the results of the aMD method are in accordance with the experiment very well. All analyses show that the aMD can accelerate the simulation process, thus we may apply it to the field of computer aided drug designs.

Concave Group Selection of Nonparameter Additive Accelerated Failure Time Model

In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property of the penalty estimator based on GMCP in the nonparameter AFT model.

MATHEMATICAL MODEL AND DIGITAL SIMULATION OF TURBOJET ACCELERATION

The acceleration performance of a turbojet is one of its important characteristics. In terms of control system, the engine is a controlled object. The acceleration performance not only depends on the engine, but also on the controller; therefore both the engine and the controller must be combined in a control system to make research for this performance. This paper presents a mathematical model of the turbojet acceleration control system and digital simulation method. Because the engine acceleration is a large variation transient process, the models of the engine and the controller are described by nonlinear equations. The methods of solving nonlinear equations and iterative techniques of calculating acceleration control system are discussed in this paper. The calculated results, as compared with test results, show that the simulation for this system is satisfactory.

Leakage Prediction Method for Contacting Mechanical Seals with Parallel Faces

Since the beginning of the 20th century, many researches on the sealing characteristic of mechanical seals were carried out broadly and in depth by various methods and some leakage models were built. But due to the lack of the way to characterize the main factors of influence on the leakage, most of the early researches were based on the assumptions that the seal faces topography and the frictional conditions were invariant. In the early built models, the effect of the surface topography change of the seal face on the leakage rate was neglected. Based on the fractal theory, the contact of end faces of the rotary and stationary rings was simplified to be the contact of a rough surface and an ideal rigid smooth surface, and the contact interface＇s cavity size-distribution function as well as the fractal characteristic of the cavity profile curve was discussed. By analyzing the influence of abrasion on the seal face topography and the leakage channel, the time-correlation leakage prediction model of mechanical seals based on the fractal theory was established and the method for predicting the leakage rate of mechanical seals with parallel plane was proposed. The values of the leakage rate predicted theoretically are similar to the measured values of the leakage rate in the model test and in situ test. The experimental results indicate that the leakage rate of mechanical seals is a transient value. The surface topography of the end faces of the seal tings and its change during the frictional wear of mechanical seals can be accurately characterized by the fractal parameters. Under the work conditions of changeless frictional mechanism, the fractal parameters measured or calculated based on the accelerated testing equation can be used to predict the leakage rate of mechanical seal in service. The proposed research provides the basis for determining the leakage state and predicting working life of mechanical seal.

On time-step in structural seismic response analysis under ground displacement/acceleration

There are two models in use today to analyze structural responses when subjected to earthquake ground motions, the Displacement Input Model （DIM） and the Acceleration Input Model （AIM）. The time steps used in direct integration methods for these models are analyzed to examine the suitability of DIM. Numerical results are presented and show that the time-step for DIM is about the same as for AIM, and achieves the same accuracy. This is contrary to previous research that reported that there are several sources of numerical errors associated with the direct application of earthquake displacement loading, and a very small time step is required to define the displacement record and to integrate the dynamic equilibrium equation. It is shown in this paper that DIM is as accurate and suitable as, if not more than, AIM for analyzing the response of a structure to uniformly distributed and spatially varying ground motions.

Preservation Property of NBUCA Life Distributions

In this paper, some results of the NBUCA class of life distribution are obtained.The preservation properties of NBUCA aging properties under anti-star-shaped transformation are investigated. The preservation of NBUCA aging properties under general accelerated life model are studied as well.

Application of Stochastic Optimization to Optimal Preventive Maintenance Problem

The contribution deals with the optimization of a sequential preventive maintenance schedule of a technical device. We are given an initial time-to-failure probability distribution, model of changes of this distribution after maintenance actions, as well as the costs of maintenance, of a device acquisition, and of the impact of failure. The maintenance timing and, eventually, its extent, are the matter of optimization. The objective of the contribution is two-fold: first, to formulate a proper (random) objective function evaluating the lifetime of the maintained device relatively to maintenance costs;second, to propose a numerical method searching for a maintenance policy optimizing selected characteristics of this objective function. The method is based on the MCMC random search combined with simulated annealing. It is also shown that such a method is rather universal for different problem specifications. The approach will be illustrated on an artificial example dealing with accelerated lifetime after each maintenance action.

A Generalized Accelerated Failure Time Model to Predict Restoration Time from Power Outages

Major disasters such as wildfire, tornado, hurricane, tropical storm, and flooding cause disruptions in infrastructure systems such as power and water supply, wastewater management, telecommunication, and transportation facilities. Disruptions in electricity infrastructure have negative impacts on sectors throughout a region, including education, medical services,financial services, and recreation. In this study, we introduced a novel approach to investigate the factors that can be associated with longer restoration time of power service after a hurricane. Considering restoration time as the dependent variable and using a comprehensive set of county-level data, we estimated a generalized accelerated failure time(GAFT) model that accounts for spatial dependence among observations for time to event data. The model fit improved by 12% after considering the effects of spatial correlation in time to event data. Using the GAFT model and Hurricane Irma's impact on Florida as a case study, we examined:(1) differences in electric power outages and restoration rates among different types of power companies—investor-owned power companies, rural and municipal cooperatives;(2) the relationship between the duration of power outage and power system variables;and(3) the relationship between the duration of power outage and socioeconomic attributes. The findings of this study indicate that counties with a higher percentage of customers served by investor-owned electric companies and lower median household income faced power outage for a longer time. This study identified the key factors to predict restoration time of hurricane-induced power outages, allowing disaster management agencies to adopt strategies required for restoration process.

Objective Bayesian analysis for the accelerated degradation model usingWiener process with measurement errors

The Wiener process as a degradation model plays an important role in the degradation analysis.In this paper, we propose an objective Bayesian analysis for an acceleration degradation Wienermodel which is subjected to measurement errors. The Jeffreys prior and reference priors underdifferent group orderings are first derived, the propriety of the posteriors is then validated. It isshown that two of the reference priors can yield proper posteriors while the others cannot. A simulation study is carried out to investigate the frequentist performance of the approach comparedto the maximum likelihood method. Finally, the approach is applied to analyse a real data.

Approaches for a 3D assessment of pavement evenness data based on 3D vehicle models

Pavements are 3D in their shape. They can be captured in three dimensions by modern road mapping equipment which allows for the assessment of pavement evenness in a more holistic way as opposed to current practice which divides into longitudinal and transversal evenness. It makes sense to use 3D vehicle models to simulate the effects of 3D surface data on certain functional criteria like pavement loading, cargo loading and driving comfort. In order to evaluate the three criteria mentioned two vehicle models have been created： a passenger car used to assess driving comfort and a truck-semitrailer submodel used to assess pavement and cargo loading. The vehicle models and their application to 3D surface data are presented. The results are well in line with existing single-track （planar） models. Their advantage over existing 1D/2D models is demonstrated by the example of driving comfort evaluation. Existing ＂geometric＂ limit values for the assessment of longi- tudinal evenness in terms of the power spectral density could be used to establish corre- sponding limit values for the dynamic response, i.e. driving comfort, pavement loading and cargo loading. The limit values are well in line with existing limit values based on planar vehicle models. They can be used as guidelines for the proposal of future limit values. The investigations show that the use of 3D vehicle models is an appropriate and meaningful way of assessing 3D evenness data gathered by modern road mapping systems.

Regression Analysis of Interval-Censored Data with Informative Observation Times Under the Accelerated Failure Time Model

This paper discusses regression analysis of interval-censored failure time data arising from the accelerated failure time model in the presence of informative censoring.For the problem,a sieve maximum likelihood estimation approach is proposed and in the method,the copula model is employed to describe the relationship between the failure time of interest and the censoring or observation process.Also I-spline functions are used to approximate the unknown functions in the model,and a simulation study is carried out to assess the finite sample performance of the proposed approach and suggests that it works well in practical situations.In addition,an illustrative example is provided.

Multistate Models for the Recovery Process in the Covid-19 Context:An Empirical Study of Chinese Enterprises

The Covid-19 pandemic has severely affected enterprises worldwide.It is thus of practical significance to study the process of enterprise recovery from Covid-19.However,the research on the effects of relevant determinants of business recovery is limited.This article presents a multistate modeling framework that considers the determinants,recovery time,and transition likelihood of Chinese enterprises by the state of those enterprises as a result of the pandemic(recovery state),with the help of an accelerated failure time model.Empirical data from 750 enterprises were used to evaluate the recovery process.The results indicate that the main problems facing non-manufacturing industries are supply shortages and order cancellations.With the increase of supplies and orders,the probability of transition between different recovery states gradually increases,and the recovery time of enterprises becomes shorter.For manufacturing industries,the factors that hinder recovery are more complex.The main problems are employee panic and order cancellations in the initial stage,employee shortages in the middle stage,and raw material shortages in the full recovery stage.This study can provide a reference for enterprise recovery in the current pandemic context and help policymakers and business managers take necessary measures to accelerate recovery.

Pruning-aware Sparse Regularization for Network Pruning

Structural neural network pruning aims to remove the redundant channels in the deep convolutional neural networks(CNNs)by pruning the filters of less importance to the final output accuracy.To reduce the degradation of performance after pruning,many methods utilize the loss with sparse regularization to produce structured sparsity.In this paper,we analyze these sparsity-training-based methods and find that the regularization of unpruned channels is unnecessary.Moreover,it restricts the network′s capacity,which leads to under-fitting.To solve this problem,we propose a novel pruning method,named Mask Sparsity,with pruning-aware sparse regularization.Mask Sparsity imposes the fine-grained sparse regularization on the specific filters selected by a pruning mask,rather than all the filters of the model.Before the fine-grained sparse regularization of Mask Sparity,we can use many methods to get the pruning mask,such as running the global sparse regularization.Mask Sparsity achieves a 63.03%float point operations(FLOPs)reduction on Res Net-110 by removing 60.34%of the parameters,with no top-1 accuracy loss on CIFAR-10.On ILSVRC-2012,Mask Sparsity reduces more than 51.07%FLOPs on Res Net-50,with only a loss of 0.76%in the top-1 accuracy.The code of this paper is released at https://github.com/CASIA-IVA-Lab/Mask Sparsity.We have also integrated the code into a self-developed Py Torch pruning toolkit,named Easy Pruner,at https://gitee.com/casia_iva_engineer/easypruner.

Research of Step-down Stress Accelerated Degradation Data Assessment Method of a Certain Type of Missile Tank

The performance degradation rates of the missile tank are generally time-varying functions uneasily evaluated by general classical evaluation methods. This paper develops a segmented nonlinear accelerated degradation model （SNADM） based on the equivalent method of accumulative damage theory, which tackles the problem that product life is difficult to be determined with degradation rate being a function of the variable of time. A segmented expression of the function of population accumulative degradation is derived. And combined with nonlinear function, an accelerated degradation function, i.e., SNADM is obtained. The parameters of the SNADM are identified by numerical iteration, and the statistical function of degradation track is extrapolated. The reliability function is determined through the type of random process of the degradation distribution. Then an evaluation of product storage life is undertaken by combining the statistical function of degradation track, reliability function and threshold. An example of a missile tank undergoes a step-down stress accelerated degradation test （SDSADT）, in which the results with the SNADM and the classical method are evaluated and compared. The technology introduced is validated with the resultant coincidence of both evaluated and field storage lives.

PokerNet:Expanding Features Cheaply via Depthwise Convolutions

Pointwise convolution is usually utilized to expand or squeeze features in modern lightweight deep models.However,it takes up most of the overall computational cost(usually more than 90%).This paper proposes a novel Poker module to expand features by taking advantage of cheap depthwise convolution.As a result,the Poker module can greatly reduce the computational cost,and meanwhile generate a large number of effective features to guarantee the performance.The proposed module is standardized and can be employed wherever the feature expansion is needed.By varying the stride and the number of channels,different kinds of bottlenecks are designed to plug the proposed Poker module into the network.Thus,a lightweight model can be easily assembled.Experiments conducted on benchmarks reveal the effectiveness of our proposed Poker module.And our Poker Net models can reduce the computational cost by 7.1%-15.6%.Poker Net models achieve comparable or even higher recognition accuracy than previous state-of-the-art(SOTA)models on the Image Net ILSVRC2012 classification dataset.Code is available at https://github.com/diaomin/pokernet.

Estimating Survival Treatment Effects with Covariate Adjustment Using Propensity Score

Propensity score is widely used to estimate treatment effects in observational studies.The covariate adjustment using propensity score is the most straightforward method in the literature of causal inference.In this article,we estimate the survival treatment effect with covariate adjustment using propensity score in the semiparametric accelerated failure time model.We establish the asymptotic properties of the proposed estimator by simultaneous estimating equations.We conduct simulation studies to evaluate the finite sample performance of the proposed method.A real data set from the German Breast Cancer Study Group is analyzed to illustrate the proposed method.