This article investigated the effect of magnetic field on heat-absorbent ferrofluid in a vertical loop consisting of a pair of concentric cylinders with surface sliding and temperature jumping. For this puprose, the control equation was converted into the dimensional form using non-dimensional quantities and parameters. The system of equations was solved analytically using an integration technique. Subsequently, the solution was obtained in the form of first and second type of Bessel functions. The results are presented graphically and show that velocity increases with the increase in the nanoparticle size. Resultantly, the rate of heat transfer of the fluid in the inner cylinder reduces.

Banhatti indices of a graph were introduced by Kulli. In this paper we have computed the general K-Banhatti indices, first and second K-Banhatti indices, K hyper Banhatti indices, modified K Banhatti indices and sum connectivity Banhatti indices for hexagonal, honeycomb and honeycomb derived networks. Hexagonal and Honeycomb Networks Fazal Dayan et al.: Computing Banhatti Indices of Hexagonal, Honeycomb and Derived Networks molecular graph and calculated atom bond connectivity reverse index and product connectivity reverse index for oxide and honeycomb networks [21], the direct and inverse sum Banhatti indices for some graphs [22], multiplicative connectivity banhatti indices of benzenoid systems and polycyclic aromatic hydrocarbons [23] and reverse zagreb and reverse hyper-zagreb indices and their polynomials of rhombus silicate networks [24].S. Hayat et al. [25] derived some new classes of networks from honeycomb network by using some basic graph operations like stellation, bounded dual and medial of a graph and denoted these n-dimensional honeycomb derived network of first type as 1 . We give analytic formulas of the general K-Banhatti indices, first and second K-Banhatti indices, K hyper Banhatti indices, modified K Banhatti indices and sum connectivity Banhatti indices of the hexagonal, honeycomb and honeycomb derived networks.

It has been demonstrated that 3D Convolutional Neural Networks (CNN) are an effective technique for classifying hyperspectral images (HSI). Conventional 3D CNNs produce too many parameters to extract the spectral-spatial properties of HSIs. A channel service module and a spatial service module are utilized to optimize characteristic maps and enhance sorting performance in order to further study discriminating characteristics. In this article, evaluate CNN's methods for hyperspectral image categorization (HSI). Examined the replacement of traditional 3D CNN with mixed feature maps by frequency to lessen spatial redundancy and expand the receptive field. Evaluates several CNN stories that use image classification algorithms, elaborating on the efficacy of these approaches or any remaining holes in methods. How do improve those gaps for better image classification?

The human immunodeficiency viruses are two species of Lentivirus that infect humans. Over time, they cause acquired immunodeficiency syndrome, a condition in which progressive immune system failure allows life-threatening opportunistic infections and cancers to thrive. Human immunodeficiency virus infection came from a type of chimpanzee in Central Africa. Studies show that immunodeficiency viruses may have jumped from chimpanzees to humans as far back as the late 1800s. Over decades, human immunodeficiency viruses slowly spread across Africa and later into other parts of the world. The Susceptible-Infected-Recovered (SIR) models are significant in studying disease dynamics. In this paper, we have studied the effect of irresponsible immigrants on HIV/AIDS dynamics by formulating and considering different methods. Euler, Runge Kutta, and a Non-standard finite difference (NSFD) method are developed for the same problem. Numerical experiments are performed at disease-free and endemic equilibria points at different time step sizes 'ℎ'. The results reveal that, unlike Euler and Runge Kutta, which fail for large time step sizes, the proposed Non-standard finite difference (NSFD) method gives a convergence solution for any time step size. Our proposed numerical method is bounded, dynamically consistent, and preserves the positivity of the continuous solution, which are essential requirements when modeling a prevalent disease.