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To provide an explicit representation for wide‐sense stationary stochastic fields which can be used in stochastic finite element modelling to describe random material properties.

This method represents wide‐sense stationary stochastic fields in terms of multiple Fourier series and a vector of mutually uncorrelated random variables, which are obtained by minimizing the mean‐squared error of a characteristic equation and solving a standard algebraic eigenvalue problem. The result can be treated as a semi‐analytic solution of the Karhunen‐Loève expansion.

According to the Karhunen‐Loève theorem, a second‐order stochastic field can be decomposed into a random part and a deterministic part. Owing to the harmonic essence of wide‐sense stationary stochastic fields, the decomposition can be effectively obtained with the assistance of multiple Fourier series.

The proposed explicit representation of wide‐sense stationary stochastic fields is accurate, efficient and independent of the real shape of the random structure in consideration. Therefore, it can be readily applied in a variety of stochastic finite element formulations to describe random material properties.

This paper discloses the connection between the spectral representation theory of wide‐sense stationary stochastic fields and the Karhunen‐Loève theorem of general second‐order stochastic fields, and obtains a Fourier‐Karhunen‐Loève representation for the former stochastic fields.

The purpose of this study is to build a high accuracy thrust model under various small turbojet engine shaft speeds by using robust, ordinary, linear and nonlinear least squares estimation methods for target drone applications.

The dynamic shaft speeds from the test experiment of a target drone engine is conducted. Then, thrust values are calculated. Based on these, the engine thrust is modeled by robust linear and nonlinear equations. The models are benefited from quadratic, power and various series expansion functions with several coefficients to optimize the model parameters.

The error terms and accuracy of the model are given using sum of squared errors, root mean square error (RMSE) and R-squared (R²) error definitions. Based on the multiple analyses, it is seen that the RMSE values are no more than 17.7539 and the best obtained result for robust least squares estimation is 15.0086 for linear at all cases. Furthermore, the R² value is found to be 0.9996 as the highest with the nonlinear Fourier series expansion model.

The motivation behind this paper is to propose robust nonlinear thrust models based on power, Fourier and various series expansion functions for dynamic shaft speeds from the test experiments.

Spatial autocorrelation in regression residuals is a major issue for the modeller because it disturbs parameter estimates and invalidates the reliability of conclusions drawn from models. The purpose of this paper is to develop an approach which generates new spatial predictors that can be mapped and qualitatively analysed while controlling for spatial autocorrelation among residuals.

This paper explores an alternate approach using a Fourier polynomial function based on geographical coordinates to construct an additional spatial predictor that allows to capture the latent spatial pattern hidden among residuals. An empirical validation based on hedonic modelling of sale prices variation using a large dataset of house transactions is provided.

Results show that the spatial autocorrelation problem is under control as shown by low Moran's I indexes. Moreover, this geo‐statistical approach provides coefficients on environmental amenities that are still highly significant by capturing only the remaining spatial autocorrelation.

The originality of this paper relies on the development of a new model that allows considering, simultaneously spatial and time dimension while measuring the marginal impact of environmental amenities on house prices avoiding competition with the weight matrix needed in most spatial econometric models.

Recently, the micro-positioning technology has become more important for achieving the requirement of precision machinery. The piezo-actuator plays a very important role in this application area. A model-free adaptive sliding controller with fuzzy compensation is proposed for a piezo-actuated micro-drilling process control in this paper. The paper aims to discuss these issues.

Due to the system's nonlinear and time-varying characteristics, this control strategy employs the functional approximation technique to establish the unknown function for releasing the model-based requirement of the sliding mode control. In addition, a fuzzy scheme with online learning ability is augmented to compensate for the finite approximation error and facilitate the controller design.

The Lyapunov direct method can be applied to find adaptive laws for updating coefficients in the approximating series and tuning parameter in the fuzzy compensator to guarantee the control system stability. With the addition adaptive fuzzy compensator, as less as five Fourier series functions can be used to approximate the nonlinear time-varying function for designing a sliding mode controller for micro-drilling process control.

The important advantages of this approach are to achieve the sliding mode controller design without the system dynamic model requirement and release the trial-and-error work of selecting approximation function.

Purpose – The purpose of this paper is to examine the relationship between banking sector efficiency and economic growth in Africa.

Methodology/approach – The paper used the stochastic frontier approach stating the banking sector cost function as a Fourier flexible to estimate bank efficiency. We then used the Arellano–Bond GMM estimator to investigate the relationship between banking sector efficiency and economic growth. Annual data for banking sector financial statements were used in estimating efficiency scores.

Findings – The study found banking sector efficiency in the sample to be 69%. We also found a positive relationship between banking sector efficiency and economic growth, confirming the critical role banks play in the economy.

Practical implications – Banking sector efficiency score of 69% implies banks in Africa could save up to 31% of their total cost if they were to operate efficiently. Policy direction should therefore focus on policies and incentives that will improve the efficiency of the banking sector and hence economic growth. The study brings to the fore the importance of the qualitative aspect of the banking sector in allocating financial resources in the real economy. Focus in the real economy should not be only on the size of the banking system but also on the quality with which resources are allocated.

Originality/value of paper – This study is among the first dedicated solely to African countries. It does set the pace for future research in the area and also confirms in Africa the Schumpeterian hypothesis that the banking sector is key in allocating resources in the real economy.

This paper aims to clarify the relationship between mechanical behaviors and the underlying geometry of periodic cellular structures. Particularly, the answer to the following research question is investigated: Can seemingly different geometries of the repeating unit cells of periodic cellular structure result in similar functional behaviors? The study aims to cluster the geometry-functional behavior relationship into different categories.

Specifically, the effects of the geometry on the compressive deformation (mechanical behavior) responses of multiple standardized cubic periodic cellular structures (CPCS) at macro scales are investigated through both physical tests and finite element simulations of three-dimensional (3D) printed samples. Additionally, these multiple CPCS can be further nested into the shell of 3D models of various mechanical domain parts to demonstrate the influence of their geometries in practical applications.

The paper provides insights into how different CPCS (geometrically different unit cells) influence their compressive deformation behaviors. It suggests a standardized strategy for comparing mechanical behaviors of different CPCS.

This paper is the first work in the research domain to investigate if seemingly different geometries of the underlying unit cell can result in similar mechanical behaviors. It also fulfills the need to infill and lattify real functional parts with geometrically complex unit cells. Existing work mainly focused on simple shapes such as basic trusses or cubes with spherical holes.

In the vibration reduction field, constrained stand-off layer damping cylindrical shell plays an important role. However, due to the lack of accurate analysis of its damping characteristics, this hinders its further research and application. Therefore, the purpose of this paper is concerned with an accurate solution for the vibration-damping characteristics of a constrained stand-off-layer damping cylindrical shell (CSDCS) under various classical boundary conditions and conducts a further analysis.

Based on the Rayleigh–Ritz method and the Hamilton principle, a dynamic model of CSDCS is established. Then the loss factor and the frequency of CSDCS are obtained. The correctness and convergence behavior of the present model are verified by comparing the calculation results with the literature. By using for various classical boundary conditions without any special modifications in the solution procedure, the characteristics of CSDCS with S-S, C-C, C-S, C-F and S-F boundaries are discussed.

The Rayleigh–Ritz method is effective in handling the problem of CSDCS with different boundaries and an accurate solution is obtained. The boundary conditions have an important influence on the vibration and damping behavior of the CSDCS.

Based on the Rayleigh–Ritz method and Hamilton principle, a dynamic model of CSDCS is established for the first time, and then the loss factor and frequency of CSDCS are obtained. In addition, the effectiveness of adding the stand-off layer between the base shell and the viscoelastic layer is confirmed by discussing the characteristics of CSDCS with S-S, C-C, C-S, C-F and S-F boundaries.

Planning phase of a project results to series of crucial decisions which determine the path to objectives achievement. At the same time, in this phase, project encounters the highest level of uncertainty in comparison of all phases of project lifecycle. This paper aims to support early decisions of project based on the progress forecasting.

The scope of study is limited to downstream projects of petroleum industry in Iran, and the proposed model is trained and tested based on 75 Iranian completed petroleum projects. First, types of progress curve functions are investigated, and various types are studied and the most appropriate ones are selected through curve fitting. In the next step, using questionnaire, dependent and independent variables are recognized. Finally, using historical data and s-curve generator functions, a fuzzy inference system (ANFIS) based model have been developed to support early phases decision-making processes.

Based on the analysis of received questionnaires, six functional criteria in two groups as dependent variables and 25 independent variables, in two groups and four clusters are determined and categorized. Eventually, performance prediction model of a project has been developed by using Adaptive Nero Fuzzy Inference System.

The main contribution of this study to construction management knowledge is categorizing two groups of variables, which first one defines the project dynamic and the other calculates the key effects on previous one. Also, this investigation improves the current knowledge by analyzing the project system from the dynamic behavior perspective and modeling the defined variables using ANFIS tools.

The purpose of this paper is to develop an efficient numerical scheme for non-linear two-dimensional (2D) parabolic partial differential equations using modified bi-cubic B-spline functions. As a test case, method has been applied successfully to 2D Burgers equations.

The scheme is based on collocation of modified bi-cubic B-Spline functions. The authors used these functions for space variable and for its derivatives. Collocation form of the partial differential equation results into system of first-order ordinary differential equations (ODEs). The obtained system of ODEs has been solved by strong stability preserving Runge-Kutta method. The computational complexity of the method is O(p log(p)), where p denotes total number of mesh points.

Obtained numerical solutions are better than those available in literature. Ease of implementation and very small size of computational work are two major advantages of the present method. Moreover, this method provides approximate solutions not only at the grid points but also at any point in the solution domain.

First time, modified bi-cubic B-spline functions have been applied to non-linear 2D parabolic partial differential equations. Efficiency of the proposed method has been confirmed with numerical experiments. The authors conclude that the method provides convergent approximations and handles the equations very well in different cases.