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When using a machine vision inspection system for rail surface defect detection, many complex factors such as illumination changes, reflection inequality, shadows, stains and rust might inevitably deform the scanned rail surface image. This paper aims to reduce the influence of these factors, a pipeline of image processing algorithms for robust defect detection is developed.

First, a new inverse Perona-Malik (P-M) diffusion model is presented for image enhancement, which takes the reciprocal of gradient as feature to adjust the diffusion coefficients, and a distinct nearest-neighbor difference scheme is introduced to select proper defect boundaries during discretized implementation. As a result, the defect regions are sufficiently smoothened, whereas the faultless background remains unchanged. Then, by subtracting the diffused image from the original image, the defect features will be highlighted in the difference image. Subsequently, an adaptive threshold binarization, followed by an attribute opening like filter, can easily eliminate the noisy interferences and find out the desired defects.

Using data from our developed inspection apparatus, the experiments show that the proposed method can attain a detection and measurement precisions as high as 93.6 and 85.9 per cent, respectively, while the recovery accuracy remains 93 per cent. Additionally, the proposed method is computationally efficient and can perform robustly even under complex environments.

A pipeline of algorithms for rail surface detection is proposed. Particularly, an inverse P-M diffusion model with a distinct discretization scheme is introduced to enhance the defect boundaries and suppress noises. The performance of the proposed method has been verified with real images from our own developed system.

Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments.

Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image.

On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models.

The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

This paper aims to present development and application of the Bayesian inverse approach for retrieving parameters of non-linear diffusion coefficient based on the integral information.

The Bayes formula was used to construct posterior distribution of the unknown parameters of non-linear diffusion coefficient. The resulting aposteriori distribution of sought parameters was integrated using Markov Chain Monte Carlo method to obtain expected values of estimated diffusivity parameters as well as their confidence intervals. Unsteady non-linear diffusion equation was discretised with the Global Radial Basis Function Collocation method and solved in time using Crank–Nicholson technique.

A number of manufactured analytical solutions of the non-linear diffusion problem was used to verify accuracy of the developed inverse approach. Reasonably good agreement, even for highly correlated parameters, was obtained. Therefore, the technique was used to compute concentration dependent diffusion coefficient of water in paper.

An original inverse technique, which couples efficiently meshless solution of the diffusion problem with the Bayesian inverse methodology, is presented in the paper. This methodology was extensively verified and applied to the real-life problem.

A method has been developed for the solution of inverse heat diffusion problems to find the initial condition, boundary condition, and the source and sink function in the heat diffusion equation. The method has been used in the development of a source‐and‐sink method to find the boundary conditions in inverse Stefan problems. Green's functions have been used in the solution, and the problems are solved by using two approaches: a series solution approach, and a time incremental approach. Both can be used to find the boundary conditions without reliance on the flux information to be supplied at both sides of the interface. The methods are efficient in that they require less equations to be solved for the conditions. The numerical results have shown to be accurate, convergent, and stable. Most of all, the results do not degrade with time as in other time marching schemes reported in the literature. Algorithms can also be easily developed for the solution of the conditions.

This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming, powder metallurgy and composite material processing are briefly discussed. The range of applications of finite elements on these subjects is extremely wide and cannot be presented in a single paper; therefore the aim of the paper is to give FE researchers/users only an encyclopaedic view of the different possibilities that exist today in the various fields mentioned above. An appendix included at the end of the paper presents a bibliography on finite element applications in material processing for 1994‐1996, where 1,370 references are listed. This bibliography is an updating of the paper written by Brannberg and Mackerle which has been published in Engineering Computations, Vol. 11 No. 5, 1994, pp. 413‐55.

The purpose of this paper is to develop a numerical model for estimating the unknown boundary heat flux in a parallel plate channel for the case of a hydrodynamically and thermally developing laminar flow.

The conjugate gradient method (CGM) is used to solve the inverse problem. The momentum equations are solved using an in-house computational fluid dynamics (CFD) source code. The energy equations along with the adjoint and sensitivity equations are solved using the finite volume method.

The effects of number of measurements, distribution of measurements and functional form of unknown flux on the accuracy of estimations are investigated in this work. The prediction of boundary flux by the present algorithm is found to be quite reasonable.

It is noticed from the literature review that study of inverse problem with hydrodynamically developing flow has not received sufficient attention despite its practical importance. In the present work, a hydrodynamically and thermally developing flow between two parallel plates is considered and unknown transient boundary heat flux at the upper plate of a parallel plate channel is estimated using CGM.

The purpose of this paper is to find the solution for special cases of regular-long wave equations with fractional order using q-homotopy analysis transform method (q-HATM).

The proposed technique (q-HATM) is the graceful amalgamations of Laplace transform technique with q-homotopy analysis scheme and fractional derivative defined with Atangana-Baleanu (AB) operator.

The fixed point hypothesis considered to demonstrate the existence and uniqueness of the obtained solution for the proposed fractional-order model. To illustrate and validate the efficiency of the future technique, the authors analysed the projected nonlinear equations in terms of fractional order. Moreover, the physical behaviour of the obtained solution has been captured in terms of plots for diverse fractional order.

To illustrate and validate the efficiency of the future technique, we analysed the projected nonlinear equations in terms of fractional order. Moreover, the physical behaviour of the obtained solution has been captured in terms of plots for diverse fractional order. The obtained results elucidate that, the proposed algorithm is easy to implement, highly methodical, as well as accurate and very effective to analyse the behaviour of nonlinear differential equations of fractional order arisen in the connected areas of science and engineering.

This study aims to empirically investigate how susceptibility to social influence in new product adoption varies with one’s structural location in a social network.

The social network data were collected based on a sociometric network survey with 589 undergraduate students. Social network analysis and ordinary least squares regression analyses were used to test the hypotheses.

This study finds that consumers with high degree centrality (i.e. hubs) who have a large number of connections to others and consumers with high betweenness centrality (i.e. bridges) who connect otherwise distant groups in social networks are both less sensitive to informational influence from others. More importantly, the authors find evidence that consumers with moderate levels of degree/betweenness centrality are more susceptible to normative influence and status competition than those with low or high degree/betweenness centrality. The inverse-U patterns in the above relations are consistent with middle-status conformity and anxiety.

This research complements social influence and new product diffusion research by documenting important contingencies (i.e. network locations) in consumer susceptibility to different types of social influence from a social network perspective.

The findings will assist marketers to leverage social influence by activating relevant social ties with effective messages in their network marketing strategies.

This research provides a better understanding of the mechanisms driving susceptibility to social influence in new product diffusion.