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In this study, response surface methodology (RSM) is applied to a three-point bending stiffness analysis of low-cost material (PLA) specimens printed using FDM technology to analyze the performance of different internal lattice structures (Octet and IsoTruss principally). The purpose of this study is to extend the definition from a discrete (lattice) model to an analytical one for its use in subsequent design phases, capable of optimizing the type of cell to be used and its defining parameters to find the best stiffness-to-weight ratio.

The representative function of their mechanical behavior is extrapolated through a two-variable polynomial model based on the cell size and the thickness of the beam elements characterizing it. The polynomial is obtained thanks to several tests performed according to the scheme of RSM. An analysis on the estimation errors due to discontinuities in the physical specimens is also conducted. Physical tests applied to the specimens showed some divergences from the virtual (ideal) behavior of the specimens.

The study allowed to validate the RSM models proposed to predict the behavior of the system as the size, thickness and type of cells vary. Changes in stiffness and weight of specimens follow linear and quadratic models, respectively. This generally allows to find optimal design points where the stiffness-to-weight ratio is at its highest.

Although the literature provides numerous references to studies characterizing and parameterizing lattice structures, the industrial/practical applications concerning lattice structures are often still detached from theoretical research and limited to achieving functioning models rather than optimal ones. The approach here described is also aimed at overcoming this limitation. The software used for the design is nTop. Subsequent three-point bending tests have validated the reliability of the model derived from the method’s application.

This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottlenecks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions.

For Chinese companies that cross-list in Chinese A share and Hong Kong (H share) markets, the H share price has been consistently lower than the A share price by an average of 85% in recent years. This is puzzling because most institutional differences between the two markets have been eliminated since 2007. The purpose of this study is to explain the puzzle of the price difference of A+H companies.

Using all A and H share Chinese firms in the period 2007–2013 and a simultaneous equations approach, this study identifies three new explanations for the recent price difference.

First, utilizing a unique earning quality measure that is directly related to non-persistent components of fair value accounting under International Financial Reporting Standards (IFRS), this study finds that the lower the earnings quality, the lower the H share price relative to the A share price, and hence the greater the price difference. Second, the higher the myopic investor ownership in A share firms, the larger the A share price relative to the H share price. Third, the short-selling mechanism introduced to the A share market since 2010 helps reduce the price difference.

First, this study identifies three new explanations for the puzzle of the AH price difference which remains substantial even after the institutional and accounting standards differences between the two markets were eliminated. Second, we examine the impact of the implementation of fair value accounting under IFRS in an emerging market on the pricing difference of cross-listed shares and reveal that it can induce an unintended negative consequence on the pricing difference of cross-listed shares. Third, this study contributes to the literature on short sales by providing its mitigating role in pricing differences across two different markets. Finally, this study makes improvements in research design, which utilizes a unique measure of earnings quality that is directly related to the implementation of IFRS and a simultaneous equations approach that minimizes endogeneity concern.

The author investigates realized comoments that overcome the drawback of conventional ones and derive the following findings. First, the author proves that (even generalized) geometric implied lower-order comoments yield neither geometric realized third comoment nor fourth moment. This is in contrast to previous studies that produce geometric realized third moment and arithmetic realized higher-order moments through lower-order implied moments. Second, arithmetic realized joint cumulants are obtained through complete Bell polynomials of lower-order joint cumulants. This study’s realized measures are unbiased estimators and they can, therefore, overcome the drawbacks of conventional realized measures.

This paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.

Six different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.

The authors found that the grey forecast is a reliable forecasting method for crude oil prices.

The contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.