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This paper aims to develop an efficient algorithm combining straightforward response surface functions with Monte Carlo simulation to conduct seismic reliability analysis in a systematical way.

The representative slip surfaces are identified and based on to calibrate multiple response surface functions with acceptable accuracy. The calibrated response surfaces are used to determine the yield acceleration in Newmark sliding displacement analysis. Then, the displacement-based limit state function is adopted to conduct seismic reliability analysis.

The calibrated response surface functions have fairly good accuracy in predicting the yield acceleration in Newmark sliding displacement analysis. The seismic reliability is influenced by such factors as PGA, spatial variability and threshold value. The proposed methodology serves as an effective tool for geotechnical practitioners.

The multiple sources of a seismic slope response can be effectively determined using the multiple response surface functions, which are easily implemented within geotechnical engineering.

This paper aims to develop an effective framework which combines Bayesian optimized convolutional neural networks (BOCNN) with Monte Carlo simulation for slope reliability analysis.

The Bayesian optimization technique is firstly used to find the optimal structure of CNN based on the empirical CNN model established in a trial and error manner. The proposed methodology is illustrated through a two-layered soil slope and a cohesive slope with spatially variable soils at different scales of fluctuation.

The size of training data suite, T, has a significant influence on the performance of trained CNN. In general, a trained CNN with larger T tends to have higher coefficient of determination (R²) and smaller root mean square error (RMSE). The artificial neural networks (ANN) and response surface method (RSM) can provide comparable results to CNN models for the slope reliability where only two random variables are involved whereas a significant discrepancy between the slope failure probability (P_f) by RSM and that predicted by CNN has been observed for slope with spatially variable soils. The RSM cannot fully capture the complicated relationship between the factor of safety (FS) and spatially variable soils in an effective and efficient manner. The trained CNN at a smaller the scale of fluctuation (λ) exhibits a fairly good performance in predicting the P_f for spatially variable soils at higher λ with a maximum percentage error not more than 10%. The BOCNN has a larger R² and a smaller RMSE than empirical CNN and it can provide results fairly equivalent to a direct Monte Carlo Simulation and therefore serves a promising tool for slope reliability analysis within spatially variable soils.

A geotechnical engineer could use the proposed method to perform slope reliability analysis.

Slope reliability can be efficiently and accurately analyzed by the proposed framework.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

The paper aims to construct a method to simulate the relationship between the parameters of soil properties and the area of sliding mass of the true slip surface of a landslide.

The smoothed particle hydrodynamics (SPH) algorithm is used to calibrate a response surface function which is adopted to quantify the area of sliding mass of the true slip surface for each failure sample in Monte Carlo simulation. The proposed method is illustrated through a homogeneous and a heterogeneous cohesive soil slope.

The comparison of the results between the proposed method and the traditional method using the slip surface with minimum factor of safety (FS_min) to quantify the failure consequence has shown that the landslide risk tends to be attributed to a variety of risk sources, and that the use of a slip surface with FS_min to quantify the consequence of a landslide underestimates the landslide risk value. The difference of the risk value between the proposed method and the traditional method increases dramatically as the uncertainty of soil properties becomes significant.

A geotechnical engineer could use the proposed method to perform slope failure analysis.

The failure consequence of a landslide can be rationally predicted using the proposed method.

This study aims to understand the impact of market power and competition on earnings management, particularly discretionary accruals, in the Chinese and Taiwanese tourism industries. China and Taiwan differ not only in their political and social systems but also in their economic systems. The research aims to provide managers and investors with stock selection strategy in the decision-making process.

Accounting data consisted of 60 publicly traded travel companies in China and Taiwan from 2000 to 2014. Methodology included correlation matrix for the variables, univariate and multivariate regression and competition analysis.

Based on empirical results, the authors found a significant negative correlation between market power and discretionary accruals and market concentration (or lower market competition) and discretionary accruals in both the Chinese or Taiwanese markets. Although the Chinese travel companies enjoyed higher market power and market concentration, they engaged in less earnings manipulation than their Taiwanese counterparts as a result of the Chinese Government regulation.

Based on listed travel companies, generalization of the research results to entire tourism industry is limited. This study compares the travel companies’ practices of smoothing out earnings between China and Taiwan, thus helping managers and investors in making their financing, investment decisions.

This research contributes to the earnings management literature by examining a specific industry of tourism. This paper is original in two ways. The authors linked market power and market competition with earnings management simultaneously and then compared the Chinese and Taiwanese tourism industries in manipulating earnings.

Joon Y. Park is one of the pioneers in developing nonlinear cointegrating regression. Since his initial work with Phillips (Park & Phillips, 2001) in the area, the past two decades have witnessed a surge of interest in modeling nonlinear nonstationarity in macroeconomic and financial time series, including parametric, nonparametric and semiparametric specifications of such models. These developments have provided a framework of econometric estimation and inference for a wide class of nonlinear, nonstationary relationships. In honor of Joon Y. Park, this chapter contributes to this area by exploring nonparametric estimation of functional-coefficient cointegrating regression models where the structural equation errors are serially dependent and the regressor is endogenous. The self-normalized local kernel and local linear estimators are shown to be asymptotic normal and to be pivotal upon an estimation of co-variances. Our new results improve those of Cai et al. (2009) and open up inference by conventional nonparametric method to a wide class of potentially nonlinear cointegrated relations.

The purpose of this paper is to review the application of digital holography in studies of the corrosion of metallic materials.

Digital holography is used for in situ observation of the dynamic processes at the electrode | electrolyte interface and on the electrode surface during the corrosion dissolution of metallic materials.

Digital holography is an effect method to in situ observe the corrosion processes, and it can provide a direct experimental foundation for studying the corrosion mechanism.

Even though there are several challenges, digital holography will play a significant role in studying corrosion processes.

The conventional wisdom suggests that the lack of prior host country-specific experience and a higher institutional distance deter multinational enterprises (MNEs) from entering a foreign country. However, past studies report that Chinese MNEs show an unconventional risk-taking behavior choosing foreign locations, where they have no prior experience or there is an increased institutional distance. Drawing on the institutional theory, the purpose of this paper is to argue that Chinese Government official visits to the host country may act as a risk-reduction device, thus providing an explanation for such an unconventional behavior.

The authors develop two hypotheses regarding how Chinese Government official visits moderate the impact of host country-specific experience and institutional distance on the location choice of Chinese MNEs. The authors test the hypotheses using a sample of investment location decisions by Chinese MNEs in Latin America.

The authors find that government official visits mitigate the lack of firm’s prior host country experience. However, only high-level government visits reduce institutional distance.

The authors contribute to the international business literature by analyzing how home country government diplomatic activities may pave the way of host country institutional environment for foreign MNEs from that home country. In addition, the authors provide an additional explanation for the unconventional risk-taking behavior of Chinese MNEs. Finally, the authors also contribute to a better understanding of the decision-making process of emerging-market MNEs entering other emerging economies.

This paper aims to investigate the inhibition effect of sodium silicate (SS), sodium alginate (SA) and sodium tungstate (ST) on the corrosion behavior of AZ91D magnesium alloy in 3.5 per cent NaCl solution through polarization curve test at room temperature, electrochemical impedance spectroscopy and weight loss measurement.

The influence of SA concentration on the inhibition efficiency of the corrosion inhibitor was mainly analyzed. The corrosion morphology and inhibition mechanism of the samples were also analyzed with scanning electron microscopy and energy-dispersive spectroscopy.

The results show that with the increase of SA concentration, the corrosion inhibition first increases and then decreases. When SA concentration is 0.03 mol/L, the inhibition efficiency is the highest, reaching 98 per cent. The adsorption film formed by SA and other deposition films produce a synergistic effect on the improvement of corrosion resistance of AZ91D magnesium alloy.

The adsorption film formed by SA with other deposition films produces a synergistic effect on the improvement of corrosion resistance of AZ91D magnesium alloy. With the increase of SA concentration, the corrosion inhibition first increases and then decreases. When the concentrations of SA, SS and ST are 0.03 mol/L, 0.015 mol/L and 0.02 mol/L, respectively, the inhibition efficiency of the inhibitor is the highest, reaching 98 per cent.

The current chapter is a tentative step toward investigating the allocation of advertising budget between the internet platform and the entity platform according to the long-term and short-term achievement of advertising investment. We provide a decision-making framework on how to allocate the advertising budget to the two platforms for the best results. The integrated effect of advertising investment consists of two parts. Goodwill and customer scale reflect the long-term achievement, and sale profit represents the short-term achievement. We selected some representative feasible investment plans as decision-making units (DMUs), and calculated the values of sale profit, goodwill, and customer scale as three outputs. To determine the best advertising investment plan, we use data envelopment analysis (DEA) model to seek efficient plans, and then determine the best one from those efficient plans through preference investigation and super-efficiency technique.