This current research investigates the combined possessions of viscous dissipation and radiation on magnetohydrodynamic (MHD) Casson hybrid nanofluid (HNF) flow via a stretching sheet embedded in a permeable medium. T...
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This current research investigates the combined possessions of viscous dissipation and radiation on magnetohydrodynamic (MHD) Casson hybrid nanofluid (HNF) flow via a stretching sheet embedded in a permeable medium. The HNF consists of 2 different nanoparticles, alumina A l 2 O 3 and molybdenum disulphide M o S 2 deferred in a base fluid of blood, aiming to increase the thermal conductivity beyond that of conventional nanofluids. A two-dimensional, steady, incompressible boundary-layer flow is considered, incorporating the non-Newtonian properties of a Casson fluid. The governing PDEs accounting for velocity and energy are transformed into a system of nonlinear ODEs through similarity transformations. These transform equations are semi-numerically answered via the BVPh 1.0 and BVPh 2.0 in Mathematica with HAM. These Parametric studies show that increasing the radiation parameter and Eckert number significantly enhances the temperature distribution, while the occurrence of a magnetic field and porous medium resistance suppresses the velocity field. Also, the HNF establishes superior heat transfer performance compared to single nanoparticle suspensions. The results provide valuable insights into optimizing industrial and biomedical processes involving non-Newtonian HNFs under thermal and magnetic conditions.
The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each...
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The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.
Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge...
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In modern cancer research, the vast volume of medical data generated is often underutilised due to challenges related to patient privacy. The OncoReg Challenge addresses this issue by enabling researchers to develop a...
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Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. However, most existing approaches rely on observing all entities during training, posing a chall...
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Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small sampl...
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knowledge graph embedding plays an important role in knowledge representation, reasoning, and data mining applications. However, for multiple cross-domain knowledge graphs, state-of-the-art embedding models cannot mak...
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The convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead against efficient deployment. Channel pruning is usually applied to reduce the model r...
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Metamorphic testing seeks to verify software in the absence of test oracles. Our application domain is ocean system modeling, where test oracles rarely exist, but where symmetries of the simulated physical systems are...
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