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Cilia serves numerous biological functions in the human body. Malfunctioning of nonmotile or motile cilia will have different kinds of consequences for human health. More specifically, the directed and rhythmic beat of motile cilia facilitates the unidirectional flow of fluids that are crucial in both homeostasis and the development of ciliated tissues. In cilia-dependent hydrodynamic flows, tapering geometries look a lot like the structure of biological pathways and vessels, like airways and lymphatic vessels. In this paper, the Carreau fluid model through the cilia-assisted tapered channel (asymmetric) under the influence of induced magnetic field and convective heat transfer is investigated.

Lubrication theory is a key player in the mathematical formulation of momentum, magnetic field and energy equations. The formulated nonlinear and coupled differential equations are solved with the aid of the homotopy perturbation method (HPM). The graphical results are illustrated with the help of the computational software “Mathematica.”

The impact of diverse emerging physical parameters on velocity, induced magnetic field, pressure rise, current density and temperature profiles is presented graphically. It is observed that the cilia length parameter supported the velocity and current density profiles, while the Hartman number and Weissenberg number were opposed. A promising effect of emerging parameters on streamlines is also perceived.

The study provides novel aspects of cilia-driven induced magnetohydrodynamics flow of Carreau fluid under the influence of induced magnetic field and convective heat transfer through the asymmetric tapered channel.

The digital media recording and broadcasting classroom using Internet real-time intelligent image positioning and opinion monitoring in communication teaching is researched and analyzed.

First, spatial grid positioning and monitoring and image intelligent recognition technologies were used to extract and analyze teaching images by mastering Internet of Things (IoT) technology and establishing an intelligent image positioning and opinion monitoring digital media recording and broadcasting system framework. Next, a positioning node algorithm was utilized to measure the image distance, and then a moving node location model under the IoT was established. In addition, a radial basis function (RBF) neural network was used to realize the signal transmission function. The experimental data of the adopted RBF based on the optimization of the adaptive cuckoo search (ACS-RBF) neural network, particle swarm algorithm neural network, and method of least squares optimization were compared and analyzed. In addition, a more efficient RBF neural network was adopted. Finally, the digital media recording and broadcasting classroom scheme of real-time intelligent image positioning and opinion monitoring was designed. In addition, the application environment of digital media actual teacher teaching was detected, and recording and broadcasting pictures were analyzed and researched.

The actual value, predicted value, and the number of predicted samples of the ACS-RBF model were all better than those of the two other neural networks. According to the analysis and comparison of the sampling optimization Monte Carlo localization (SOMCL), Monte Carlo, and genetic algorithm optimization-based Monte Carlo positioning algorithms, the SOMCL algorithm showed better robustness, and its positioning efficiency was superior to that of the two other algorithms. In addition, the SOMCL algorithm greatly reduced the positioning and monitoring energy consumption.

The application of real-time intelligent image positioning and monitoring technology in actual communication teaching was realized in the study.

The purpose of this research is to study the performance of piezoresistive pressure sensors using polysilicon as the piezoresistive material, which is typically used to measure pressure in high-temperature environments.

The performance of this sensor is enhanced by studying the influence of multi-turn configuration at which the piezoresistors are arranged. Different configurations are studied and compared by laying down their analytical solution.

The validation of analytical results is accomplished through finite element analysis using the software COMSOL Multiphysics. The best configuration, which uses a partial triple-turn configuration, was able to achieve a sensitivity of 116.00 mV/V/MPa over a simulated pressure range of 0 to 500 KPa.

The literature shows the study of single-turn and double-turn meander-shaped configuration of micro-electromechanical systems piezoresistive pressure sensor but multi-turn meander-shaped configuration using a square silicon diaphragm has not been reported. Its study has reflected promising results than its counterparts based on key performance parameters such as sensitivity and linearity and are more effective to be used for automotive, aviation, biomedical and consumer electronics applications.

Based on previous results of the existence, uniqueness, and regularity conditions for a continuous dynamic model for a parallel-plate electrostatic micro-electron-mechanical-systems with the fringing field, the purpose of this paper concerns a Galerkin-FEM procedure for deformable element deflection recovery. The deflection profiles are reconstructed by assigning the dielectric properties of the moving element. Furthermore, the device’s use conditions and the deformable element’s mechanical stresses are presented and discussed.

The Galerkin-FEM approach is based on weighted residuals, where the integrals appearing in the solution equation have been solved using the Crank–Nicolson algorithm.

Based on the connection between the fringing field and the electrostatic force, the proposed approach reconstructs the deflection of the deformable element, satisfying the conditions of existence, uniqueness and regularity. The influence of the electromechanical properties of the deformable plate on the method has also been considered and evaluated.

The developed analytical model focused on a rectangular geometry.

The device studied is suitable for industrial and biomedical applications.

This paper proposed numerical approach characterized by low CPU time enables the creation of virtual prototypes that can be analyzed with significant cost reduction and increased productivity.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.