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Influence of slip parameter, viscous dissipation and Joule heating effect on boundary layer flow and heat transfer over a power-law stretching wedge-shaped surface with the correlation coefficient and multiple regressions

Ali Mohammad, Alim Md. Abdul

International Journal of Applied Mechanics and Engineering

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2022

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Vol. 27, no. 2

1--21

EN

The influence of slip parameter, viscous dissipation, and Joule heating parameter on MHD boundary layer nanofluid flow over a permeable wedge-shaped surface was analysed. The PDEs and the associated boundary conditions were transformed to a set of non-similar ODEs and the obtained system of equations was solved numerically with the help of the spectral quasi-linearization method (SQLM) by applying suitable software. This method helps to identify the accuracy and convergence of the present problem. The current numerical results were compared with previously published work and are found to be similar. The fluid velocity, fluid temperature, and nanoparticle concentration within the boundary layer region for various values of the parameters such as the slip effect, magnetic strength, Prandtl number, Lewis number, stretching ratio, viscous dissipation, suction, Brownian motion, Joule heating, heat generation, and thermophoresis are studied. It is observed that the Brownian motion, Joule heating, viscous dissipation, and thermophoresis lead to decreases in the heat and mass transfer rate. The skin friction coefficient enhances with slip, magnetic, permeability, and suction parameters, but reduces with the Brownian motion, wedge angle, and stretching ratio parameters whereas there is no effect of mixed convection, thermophoresis, heat generation parameters, the Prandtl and Eckert number.

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AutoCovNet: Unsupervised feature learning using autoencoder and feature merging for detection of COVID-19 from chest X-ray images

Rashid Nayeeb, Hossain Md Adnan Faisal, Ali Mohammad, Islam Sukanya Mumtahina, Mahmud Tanvir, Fattah Shaikh Anowarul

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1685--1701

EN

With the onset of the COVID-19 pandemic, the automated diagnosis has become one of the most trending topics of research for faster mass screening. Deep learning-based approaches have been established as the most promising methods in this regard. However, the limitation of the labeled data is the main bottleneck of the data-hungry deep learning methods. In this paper, a two-stage deep CNN based scheme is proposed to detect COVID-19 from chest X-ray images for achieving optimum performance with limited training images. In the first stage, an encoder-decoder based autoencoder network is proposed, trained on chest X-ray images in an unsupervised manner, and the network learns to reconstruct the X-ray images. An encoder-merging network is proposed for the second stage that consists of different layers of the encoder model followed by a merging network. Here the encoder model is initialized with the weights learned on the first stage and the outputs from different layers of the encoder model are used effectively by being connected to a proposed merging network. An intelligent feature merging scheme is introduced in the proposed merging network. Finally, the encoder-merging network is trained for feature extraction of the X-ray images in a supervised manner and resulting features are used in the classification layers of the proposed architecture. Considering the final classification task, an EfficientNet-B4 network is utilized in both stages. An end to end training is performed for datasets containing classes: COVID-19, Normal, Bacterial Pneumonia, Viral Pneumonia. The proposed method offers very satisfactory performances compared to the state of the art methods and achieves an accuracy of 90:13% on the 4-class, 96:45% on a 3-class, and 99:39% on 2-class classification.

3

Experimental Study on Dyeing Performance and Antibacterial Activity of Silver Nanoparticle-Immobilized Cotton Woven Fabric

Tania Imana Shahrin, Ali Mohammad, Bhuiyan Riyadh Hossen

Autex Research Journal

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2021

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Vol. 21, no. 1

45--51

EN

The purposes of the current research were to deposit the silver nanoparticles on the surface of a textile woven fabric and evaluate their dyeing performance and antibacterial activity. The synthesis of silver nanoparticle (Ag°) is done by the in situ method. Strong alkali is used to improve functionality of cellulose before the application of silver nitrate salt (AgNO3). The silver nanoparticle is formed by reduction of ascorbic acid. Various instrumental analyses are done to prove the formation of nanoparticles on the fabric surface. The morphology of nanodeposited fabric is characterized by using scanning electron microscope (SEM), elemental composition is done by energy dispersive spectroscopy, and crystallinity of nanoparticles is obtained by X-ray diffraction (XRD). Nanodeposited fabric is then dyed with direct dyestuff (Direct Red-89). Fourier transform infrared spectroscopy analysis is done to explore the bonding phenomena of un-dyed and dyed fabrics. The dyeing performance and antibacterial activity are examined on the colored fabric to investigate the dyed fabric quality after nanoparticle deposition. Results demonstrate the improvement of 54% of color strength and 11% of dye exhaustion with excellent antibacterial activity.