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The heterogeneous fluid model is expressed for the nanofluid flow to study the significance of the Fourier’s and Fick’s laws. The magnetohydrodynamics couple-stress bioconvective nanofluid flow is considered across an extending surface with the effect of exponential heat source/sink and stratified boundary conditions. The solid nanoparticulates and concentrations of motile microorganisms are added to the nonlinear system of differential equations conveying the non-Newtonian nanoliquid flow model. Moreover, the combined effect of the heat flux and thermal radiation is also evaluated. The similarity transformations are employed to transfigure the system of partial differential equations into the lowest order of ordinary differential equations. The Artificial Neural Network Levenberg Marquardt Back-propagation optimization algorithm is employed to solve these equations. To authenticate the outcomes, the dataset is formed using the MATLAB package “bvp4c”. The dataset is created for diverse circumstances of flow factors, as well as validation and testing of the Artificial Neural Network. The accuracy of the model is estimated through numerous statistical tools (histogram, curve fitting, regression measures, and performance plots). The outcomes are presented through the table and figures. It has been noticed that the couple-stress nanofluid flow declines with the influence of magnetic field and mixed convection. The couple-stress nanofluid temperature augments with the enhancement of the thermophoresis effect, buoyancy ratio factor, Rayleigh number, and thermal radiation. Moreover, the concentration curve lessens under the impact of the Lewis number while enriched with the outcome of the concentration stratification parameter. The absolute error of reference and targeted date is attained within 10${}^{-3}$–10${}^{-6}$ that proves the exceptional precision of the results.

The impact localization in composite panels is assessed using two machine learning techniques: least square support vector machines (LSSVM) and artificial neural networks (ANN) with local strain signals from piezoelectric sensors. Sensor signals from impact experiments on a composite plate as well as signals simulated by a finite element model are used to train and test models. A comparative study shows that LSSVM achieves better accuracy than ANN on identifying location of impacts for a combination of large mass impact and small mass impact, in particular when less data is available for training which is more appropriate for real aeronautical application. Additionally, LSSVM is more capable of identifying new impact events which have not been considered in the training process.

The evolution of communication technologies with high-frequency radio-frequency (RF) devices increased the demand for compact and efficient designs. Micro-electromechanical systems (MEMS) technology revolutionized microwave and RF applications because of its ability to be engineered into miniaturized devices that are highly linear and power efficient. It is more challenging to perform numerical analysis and optimization of such complex MEMS devices. Electromagnetic (EM) simulation-based optimization software using different methodologies employing coupled domains and time domain analysis of MEMS devices requires repeated simulation, which makes it computationally expensive. The artificial neural network (ANN) model is an alternative to these conventional simulation-based design methodologies to expedite the design process. ANN models for RF and microwave modeling are known to be effective, precise, and flexible. ANN is capable of producing accurate results with less computational time than sophisticated EM models. An overview of various RF MEMS components and an introduction to ANN are provided in this chapter. In addition, this chapter presents the concept of modeling an RF MEMS shunt switch using ANN as a case study.