Magnetohydrodynamic (MHD) flow of nanofluid with double stratification and slip conditions

This paper concerns with the analysis of double stratification in magnetohydrodynamic (MHD) flow of nanofluid by a stretching cylinder. Brownian motion and thermophoresis effects are present in the transport equations. The flow is subjected to velocity, thermal and solutal slip conditions. Non-linear ordinary differential equations are obtained from the governing non-linear partial differential equations after using appropriate transformations. The resulting non-linear ordinary differential equations are solved for the convergent series solutions. The velocity, temperature and concentration profiles are illustrated for different emerging parameters. Velocity distribution decays for higher estimation of velocity slip parameter. Furthermore, temperature decreases and concentration enhances for higher values of thermal stratification parameter and thermophoresis parameter, respectively. Numerical results for the skin friction, Nusselt number and Sherwood number are also presented and examined. Comparison between the published limiting solutions and present results is found in an excellent agreement.     

Hall effects and entropy generation applications for peristaltic flow of modified hybrid nanofluid with electroosmosis phenomenon

The electroosmotic peristaltic flow of modified hybrid nanofluid in presence of entropy generation has been presented in this thermal model. The Hall impact and thermal radiation with help of nonlinear relations has also been used to modify the analysis. The assumed flow is considered due to a non-uniform trapped channel. The properties of modified hybrid nanofluid model are focused with interaction of three distinct types of nanoparticles namely copper ($Cu)$, silver ($Ag$) and aluminum oxide ($A{l}_2{O}_3).$ The mathematical modeling and significances of entropy generation and Bejan number are identified. With certain flow assumptions, the governing equations are attained for optimized peristaltic electroosmotic problem. Widely used assumptions of long wave length and low Reynolds number reduced the governing equations in ordinary differential equations. The ND solver is flowed for the solution process. The physical significant of results is observed by assigning the numerical values to parameters.     

Evaluation of chemometric techniques and artificial neural networks for cancer screening using Cu, Fe, Se and Zn concentrations in blood serum

It is known that variations in the concentrations of certain trace elements in bodily fluids may be an indication of an alteration of the organism's normal functioning. Multivariate analysis of such data, and its comparison against proper reference values, can thus be employed as possible screening tests. Such analysis has usually been conducted by means of chemometric techniques and, to a lower extent, backpropagation artificial neural networks. Despite the excellent classification capacities of the latter, its development can be time-consuming and computer-intensive. Probabilistic artificial neural networks represent another attractive, yet scarcely evaluated classification technique which could be employed for the same purpose. The present work was aimed at comparing the performance of two chemometric techniques (principal component analysis and logistic regression) and two artificial neural networks (a backpropagation and a probabilistic neural network) as screening tools for cancer. The concentrations of copper, iron, selenium and zinc, which are known to play an important role in body chemistry, were used as indicators of physical status. Such elements were determined by total reflection X-ray fluorescence spectrometry in a sample of blood serum taken from individuals who had been given a positive (N = 27) or a negative (N = 32) diagnostic on various forms of cancer. The principal components analysis served two purposes: (i) initial screening of the data; and, (ii) reducing the dimension of the data space to be input to the networks. The logistic regression, as well as both artificial neural networks showed an outstanding performance in terms of distinguishing healthy (specificity: 90-100%) or unhealthy individuals (sensitivity: 100%). The probabilistic neural network offered additional advantages when compared to the more popular backpropagation counterpart. Effectively, the former is easier and faster to develop, for a smaller number of variables has to be optimized, and there are no risk of overtraining.