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The purpose of this paper is to study the identification methods for multivariable nonlinear Box‐Jenkins systems with autoregressive moving average (ARMA) noises, based on the auxiliary model and the multi‐innovation identification theory.

A multi‐innovation generalized extended least squares (MI‐GELS) and a multi‐innovation generalized ex‐tended stochastic gradient (MI‐GESG) algorithms are developed for multivariable nonlinear Box‐Jenkins systems based on the auxiliary model. The basic idea is to construct an auxiliary model from the measured data and to replace the unknown terms in the information vector with their estimates (i.e. the outputs of the auxiliary model).

It is found that the proposed algorithms can give high accurate parameter estimation compared with existing stochastic gradient algorithm and recursive extended least squares algorithm.

In this paper, the AM‐MI‐GESG and AM‐MI‐GELS algorithms for MIMO Box‐Jenkins systems with nonlinear input are presented using the multi‐innovation identification theory and the proposed algorithms can improve the parameter estimation accuracy. The paper provides a simulation example.

In this paper, the international progress of Nationally Appropriate Mitigation Actions (NAMAs), Intended Nationally Determined Contributions (INDCs), and Nationally Determined Contributions (NDCs) under the United Nations Framework Convention on Climate Change are reviewed. The content of Taiwan’s NAMAs and INDCs are also investigated, especially with reference to actions for the electricity sector. To better understand the greenhouse gas (GHG) reduction contribution from the electricity sector, this paper aims to examine challenges and solutions for implementing a carbon trading mechanism in Taiwan’s monopolistic electricity market under the newly passed Greenhouse Gases Emissions Reduction and Management Act (GHG ERMA).

Carbon reduction strategies for the electricity sector are discussed by examining and explaining Taiwan’s official documents and the law of GHG ERMA.

This study finds that market mechanisms should be utilized to allocate appropriate costs and incentives for GHG reductions to transform Taiwan into a low-carbon society.

This study identifies strategies for the electricity sector to reduce GHG emissions, especially the operation of a carbon-trading scheme under a non-liberalized electricity market.

Over the past two decades, China’s stock market has experienced rapid growth. This period has seen the transplantation of many “OECD principles of corporate governance” into the Chinese corporate regulatory framework. These regulations are dominated by shareholder values. This paper aims to discover whether there is a causal relationship between the changes in China’s corporate governance and financial market growth.

This paper uses data from 1995-2014 to create a robust corporate index by looking at 52 variables and a financial index out of five financial market parameters. Subsequently, data are subject to a panel regression analysis, with the financial market index as the outcome variable, corporate governance index explanatory variable and a variety of economics, social and technological control variables.

This paper concludes that changes in corporate regulation have in fact had no statistically significant impact on China’s financial market growth, which must therefore be attributed to other factors.

The study is the first in the context of Chinese corporate governance impact studies to use Bayesian methodology to analyse a panel dataset. It uses OECD principles as the anchor to provide a clear picture of evolution of corporate governance for a 20-year period which is also longer than previous studies.

The purpose of this paper is to find two iterative methods to solve the general coupled matrix equations over the generalized centro‐symmetric and central antisymmetric matrices.

By extending the idea of conjugate gradient (CG) method, the authors present two iterative methods to solve the general coupled matrix equations over the generalized centro‐symmetric and central antisymmetric matrices.

When the general coupled matrix equations are consistent over the generalized centro‐symmetric and central anti‐symmetric matrices, the generalized centro‐symmetric and central anti‐symmetric solutions can be obtained within nite iterative steps. Also the least Frobenius norm generalized centrosymmetric and central anti‐symmetric solutions can be derived by choosing a special kind of initial matrices. Furthermore, the optimal approximation generalized centrosymmetric and central anti‐symmetric solutions to given generalized centro‐symmetric and central anti‐symmetric matrices can be obtained by finding the least Frobenius norm generalized centro‐symmetric and central anti‐symmetric solutions of new matrix equations. The authors employ some numerical examples to support the theoretical results of this paper. Finally, the application of the presented methods is highlighted for solving the projected generalized continuous‐time algebraic Lyapunov equations (GCALE).

By the algorithms, the solvability of the general coupled matrix equations over generalized centro‐symmetric and central anti‐symmetric matrices can be determined automatically. The convergence results of the iterative algorithms are also proposed. Several examples and an application are given to show the efficiency of the presented methods.

The purpose of this paper is to find the efficient iterative methods for solving the general matrix equation A1X+ XA2+A3XH+XHA4=B (including Lyapunov and Sylvester matrix equations as special cases) with the unknown complex (reflexive) matrix X.

By applying the principle of hierarchical identification and the Hermitian/skew‐Hermitian splitting of the coefficient matrix quadruplet A1; A2; A3; A4 the authors propose a shift‐splitting hierarchical identification (SSHI) method to solve the general linear matrix equation A1X+XA2+A3XH+XHA4=B. Also, the proposed algorithm is extended for finding the reflexive solution to this matrix equation.

The authors propose two iterative methods for finding the solution and reflexive solution of the general linear matrix equation, respectively. The proposed algorithms have a simple, neat and elegant structure. The convergence analysis of the methods is also discussed. Some numerical results are given which illustrate the power and effectiveness of the proposed algorithms.

So far, several methods have been presented and used for solving the matrix equations by using vec operator and Kronecker product, generalized inverse, generalized singular value decomposition (GSVD) and canonical correlation decomposition (CCD) of matrices. In several cases, it is difficult to find the solutions by using matrix decomposition and generalized inverse. Also vec operator and Kronecker product enlarge the size of the matrix greatly therefore the computations are very expensive in the process of finding solutions. To overcome these complications and drawbacks, by using the hierarchical identification principle and the Hermitian=skew‐Hermitian splitting of the coefficient matrix quadruplet (A1; A2; A3; A4), the authors propose SSHI methods for solving the general matrix equation.

The purpose of this paper is to compare GM(1,1) model, rolling GM(1,1) model and metabolism GM(1,1) model included in the GM(1,1) model cluster and use these models to forecast global oil consumption.

Simulated sequences will be generated randomly, and used to test the models included in the GM(1,1) model cluster; and these grey forecasting models are applied to forecast global oil consumption.

Effectiveness of these grey forecasting models is proved by random experiments, which explains the model adaptability. Global oil consumption is predicted, and it shows that global oil consumption will increase at a rather big growth rate in the next years.

The effectiveness of medium-term prediction of these grey forecasting models is analyzed by random experiments. These models are compared, and some basis for model selection is obtained.

The purpose of this paper is to investigate the effectiveness of GM(1,1) model on linear growth sequences (LGS) by random experiments and global primary energy consumption is predicted as by the GM(1,1) and the autoregressive integrated moving average (ARIMA) model, which is used as a reference.

LGS generated randomly are used for GM(1,1) modeling. The results of the massive repeated random experiments are analyzed to test the effectiveness of the GM(1,1) model and global primary energy consumption is predicted using the GM(1,1) model and the ARIMA model.

The use of the GM(1,1) model is effective when used for a LGS and the model is proven to be reliable by the experiments. Global primary energy consumption is predicted with the GM(1,1) model and the ARIMA model as a case study, and the results show that GM(1,1) is quite good. Global primary energy consumption will increase by 1.03 percent in 2016.

The contribution of this paper includes the following: first, the applicability of the GM (1,1) model is further discussed with random experiments and it is feasible for a LGS; second, random experiments provide good proof that four data are enough for GM(1,1) modeling, and GM(1,1) model is reliable; third, prediction by using GM(1,1) model with small data is even better than time-series analysis in the case study.

Seaports seek to find innovative technological solutions to deal with the ever-increasing growth of transportation and increasing the intensity of competition through the use of emerging technology such as digital twin technology to improve the quality of their logistics operations. Despite the success of digital twins in many industries, there is still a lack of their application in the field of seaports where ports play a central role as part of global transportation chains. Seaports sustainability comprises three main aspects: the social aspect that encompasses more job opportunities, the economic aspect that enhances foreign trade, and the environmental aspect that refers to the process of managing and operating ports in a way that saves the environment. This chapter discusses how to apply digital twins’ technology on the imported Twenty equivalent foot unit (TEUs) taking into consideration the population growth, and the capacity of the storage area of the container terminals in an attempt to explore the impact on Kuwait’s seaports sustainability. The study provides a framework for capacity management in an attempt to initiate the next generation of smart seaports cities and consequently impact society, economy, and well-being in Kuwait and Gulf region. The results of the study showed that there is a strong correlation between population growth and imported TEUs growth during the essential stage of the study. The correlation factor was 0.97, and this correlation will support the prediction until Kuwait vision 2040.

In this chapter, the authors critically examine the application of unmeasured latent method factors (ULMFs) in human resource and organizational behavior (HROB) research, focusing on addressing common method variance (CMV). The authors explore the development and usage of ULMF to mitigate CMV and highlight key debates concerning measurement error in the HROB literature. The authors also discuss the implications of biased effect sizes and how such bias can lead HR professionals to oversell interventions. The authors provide evidence supporting the effectiveness of ULMF when a specific assumption is held: a single latent method factor contributes to the data. However, the authors dispute this assumption, noting that CMV is likely multidimensional; that is, it is complex and difficult to fix with statistical methods alone. Importantly, the authors highlight the significance of maintaining a multidimensional view of CMV, challenging the simplification of a CMV as a single source. The authors close by offering recommendations for using ULMFs in practice as well as more research into more complex forms of CMV.

The paper aims to accurately measure the key motion parameters, such as velocity, azimuth and pitch angle, of the small flying object with a non-uniform curve trajectory. It proposes a measurement method and its calculation model of non-uniform curve trajectory using a photoelectric sensor array.

First, the basic composition of the measurement system and mechanism of photoelectric sensor array are described, respectively. Second, a non-uniform curve mathematical measurement model is constructed differently from the traditional linear trajectory, taking into account the influence of gravity and air resistance. Third, the measurement error of the system is analyzed through numerical simulation. Finally, the accuracy and feasibility of the approach are verified by live-ammunition experiments.

The results show that the systematic error of the hitting point coordinates can be reduced by 9% compared to the traditional linear measurement model. Consequently, this method can meet the higher measurement requirement for the key motion parameters of the small flying object under the non-uniform curve trajectory. Research limitations/implications (if applicable)- although the approach itself is generalizable, the method is unable to detect the motion parameters of multiple small flying objects.

Although the approach itself is generalizable, the method is unable to detect the motion parameters of the multiple small flying objects.

It is evident that the proposed non-uniform curve measurement model is more precise in quantifying the essential characteristics of the small flying object, particularly in consideration of the environmental conditions.

The precise measurement of the key motion parameters of the small flying object can facilitate the enhancement of the protective performance of protective materials.

A novel approach to measurement is proposed, which differs from the conventional uniform trajectory model. To this end, the space construction of the photoelectric sensor array is optimized. The number of the sensors is revised.