Electro-osmotic flow of couple stress fluids in a microchannel propagated by peristalsis

A mathematical model is developed for electro-osmotic peristaltic pumping of a non-Newtonian liquid in a deformable micro-channel. Stokes’ couple stress fluid model is deployed to represent realistic working liquids. The Poisson-Boltzmann equation for electric potential distribution is implemented owing to the presence of an electrical double layer (EDL) in the micro-channel. Using long wavelength, lubrication theory and Debye-Huckel approximations, the linearized transformed dimensionless boundary value problem is solved analytically. The influence of electro-osmotic parameter (inversely proportional to Debye length), maximum electro-osmotic velocity (a function of external applied electrical field) and couple stress parameter on axial velocity, volumetric flow rate, pressure gradient, local wall shear stress and stream function distributions is evaluated in detail with the aid of graphs. The Newtonian fluid case is retrieved as a special case with vanishing couple stress effects. With increasing couple stress parameter there is a significant elevation in axial pressure gradient whereas the core axial velocity is reduced. An increase in electro-osmotic parameter induces both flow acceleration in the core region (around the channel centreline) and also enhances axial pressure gradient substantially. The study is relevant to simulation of novel smart bio-inspired space pumps, chromatography and medical microscale devices

Application of Quality of Experience in Networked Services: Review, Trend & Perspectives

Full text embargoed until 17.10.2019 (publisher's embargo period, 12 months

Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution

Diagnosis of breast cancer disease depends on human experience. It is time consuming and has an element of human error in the results. This paper presents an intelligent multi-objective classifier to Diagnose breast cancer diseases using multilayer perceptron (MLP) neural network with Differential Evolution technique. The Differential Evolution (DE) algorithm is used to solve multi-objective optimization problems by tuning MLP neural network parameters. The proposed intelligent multi-objective classifier is used for diagnosis of breast cancer disease. In addition, it utilizes the advantages of multi-objective differential evolution to optimize the number of hidden nodes in the hidden layer of the MLP neural network and also to reduce network error rate. The results indicate that the proposed intelligent multi-objective classifier is viable in breast cancer diagnosis