Engineering and Applied Science Letters (EASL)

This research investigates the influence of the Cattaneo-Christov double diffusive flow of ferromagnetic hybrid nanofluids, taking into account heterogeneous-homogeneous chemical reactions, heat radiative flux, and the Soret-Dufour effect. The mathematical modeling of the system of equations results in the formulation of partial differential equations (PDEs). These PDEs were subsequently transformed into total differential equations (ODEs) via the application of similarity transformation. The resultant modified ODEs were addressed utilizing an innovative approach known as the spectral relaxation method (SRM). This methodology was employed to solve the system of ODEs in an iterative manner, following the Gauss-Seidel procedure. The findings of this investigation indicate that the heterogeneous-homogeneous chemical reaction significantly influences the fluid concentration, leading to a reduction in the concentration profile. An elevated level of thermal radiation was found to enhance both the fluid temperature and the velocity contour. Conversely, an increase in the magnetic field strength was noted to diminish the velocity contour. The current analysis was compared with previous studies and was found to exhibit a strong correlation.

In this paper, by using a new identity we establish some trapezoidal type inequalities for functions whose modulus of the first derivatives are \( \left( s,m\right)\)-preinvex via Caputo fractional derivatives.

This review provides a comprehensive overview of the synthesis process of nanoscale materials and highlights key characterization methods used for nanomaterials and biomaterials. It emphasizes the importance of effective techniques for investigating materials at the nanoscale, as these are too small for the human eye to detect. The review also explores various approaches to producing nanoscale materials and offers insights into the application, development, advantages, and limitations of different experimental methods for nanoparticle characterization. A particular focus is placed on advanced characterization techniques and their role in data interpretation, aiming to guide novice researchers in the field of nanoscience and nanotechnology.

We consider non oscillatory functions and prove an everywhere Fourier Inversion Theorem for functions of very moderate decrease. The proofs rely on some ideas in nonstandard analysis.

To solve the approximate analytic solutions of the quadratic Riccati differential equations, this study introduces a hybrid method that combines an accelerated variant of the Adomian decomposition method (AADM) proposed by I. El-Kalla with the Ramadan Group transform (RGT). This hybrid technique produces accurate and dependable results, outperforming the regular Adomian decomposition method (RADM) and the Newton- Raphson version of Adomian polynomials in terms of accuracy. Three examples are provided here to demonstrate good accuracy and fast convergence when compared to the exact solution and other recent analytical methods using Shifted Chebyshev polynomials, Variation of Parameters Method (VPM), Bezier polynomials, homotopy analysis method (HAM), and Newton – Raphson based modified Laplace Adomian decomposition method.

The performance of an antireflection coating entirely depends on the proportion of light energy transmitted or reflected by the coating material. To enhance the transmittance of an antireflection coating, evaluation of the amount of the light energy transmitted to generate charge carriers is very critical. Thus, in this paper, we demonstrate the effect of sputtering power and gas flow rate on the optical transmittance of aluminium oxide (Al\(_{2}\)O\(_{3}\)) and copper-doped zinc sulphide (ZnS:Cu) antireflection nanostructures. To this end, radiofrequency sputtering was used for the deposition of ZnS:Cu, using the ZnS:Cu target (94/6.0%) using argon (99.9% pure), and direct current sputtering was used for the deposition of Al\(_{2}\)O\(_{3}\) using the aluminium target (99.99% purity) and oxygen (99.9% pure). The gas flow rates of 40 to 100 sccm were used. The sputtering power values of 70 W to 140 W were used at a low process pressure of \(6.5 \times 10^{-3}\). The transmittance was observed to decrease with an increase in sputter power and deposition time. However, the transmittance of single-layer nanofilms was lower than that of the double-layer nanostructures. For photovoltaic applications, the Al\(_{2}\)O\(_{3}\)/ZnS:Cu(112.1 nm) nanostructure exhibited the highest transmittance of 96.9% at \(\lambda=780\) nm. The reflectance of the nanostructures increased with an increase in coating time and sputtering power, with the lowest value of 3.03% recorded at 360 nm. The nanostructures are crystalline, smooth, and dense but the crystallite sizes decreased from 0.02508 to 0.02071 \(\text{\AA}\) with an increase in gas flow rate. This decrease in crystallinity was due to the reduced adatom migration on the substrate. The optimal gas flow rate was 100 sccm, in which the Al\(_{2}\)O\(_{3}\)/ZnS:Cu(117 nm) had the highest transmittance of 97.7% at \(\lambda=741\) nm. The results demonstrate the potential use of Al\(_{2}\)O\(_{3}\)/ZnS:Cu nanostructures as antireflection materials for photovoltaic solar cells.

In this article we studied and juxtaposed nonparametric Least Square and the Olanrewaju-Olanrewaju regression-type \({L_{(O – O){\lambda _{\gamma (\left| \theta \right|)}}}}\) kernels for supervised Support Vector Regressor (SVR) machine learning of hyperplane regression in a bivariate setting. The nonparametric kernels used to expound the SVR were Bisquare, Gaussian, Triweight, Uniform, Epanechnikov, and Triangular. Lagrangian multiplier estimation technique was adopted in estimating the involved SVR hyperplane regression coefficients as well as other embedded coefficients in each of the stated kernels. In addition, point estimate of the Euclidean distance (\(r\)) and error margin (\(d\)) in each of the SVR kernels were carved-out. In demonstration to the annual birthrate and its percentage change (\(\Delta \% \)) of the Nigeria populace from 1950 to 2023, the Olanrewaju-Olanrewaju regression-type kernel for SVR robustly outperformed the nonparametric and Least Square kernel-based SVRs with a miniature Cross-Validation index of -1205.49. 5.9% and 3.2% hyperplane estimated regression coefficients from the Olanrewaju-Olanrewaju kernel-based SVR were recorded for the annual birthrate and its percentage change (\(\Delta \% \)) respectively. Interpretably, this connotes that for every one percent increment in the annual birthrate per 1000, the mean rate of the Nigeria populace from 1950 to 2023 increased by 5.9% while other variables were held constant. Similarly, its percentage change per 1000 increased by 3.2% while other variables were held constant. In recommendation, the nonparametric and Olanrewaju-Olanrewaju regression-type SVRs as well as the Least Square SVR were pinpointed for future consideration of categorical, missing and zero bivariate observations.

An experimental study conducted by Ankit Kumar and colleagues (Kumar, Gupta, Pandey, Govil, and Patel, “Status of Arsenic Contamination in District Lakhimpur, Uttar Pradesh, India,” in Emerging Trends in Science, Social Science and Engineering, edited by Aggarwal, Pandey, Naik, Mishra, Raj, Tripathi, and Shukla, pp. 60-73, ISBN 9789358380125, Astitva Prakashan, Bilaspur, Chhattisgarh) has identified significant levels of arsenic contamination in the groundwater of Lakhimpur district, Uttar Pradesh. Their findings indicate that arsenic levels are notably higher in the shallow regions compared to the deeper India Mark II regions across eight selected study sites. Building on these findings, this paper aims to apply a dose-response Hill model to analyze and explain the observed patterns of arsenic contamination in the groundwater resources of Lakhimpur district.

The ability of organisms or organic compounds to reduce metal ions and stabilize them into nanoparticles is known as green synthesis. Various synthesis methods have been developed, each with its own advantages and drawbacks. In recent years, nanomaterials have found extensive applications in biological sciences, particularly in health and veterinary medicine. For these applications, it is crucial that nanomaterials are biocompatible and non-toxic. Consequently, researchers have increasingly focused on biological synthesis routes. Drawing inspiration from the ancient Indian system of medicine, Ayurveda, some researchers have recently synthesized nanomaterials using Indian cow urine. This review aims to catalog the various nanomaterials produced using Indian cow urine and to discuss their catalytic and biological activities.

This study focused on developing mathematical algorithms for the perpetual Ethiopian calendar and similar calendars. The primary objective was to demonstrate the methodology for creating these algorithms. The research identified that arithmetic progression, ceiling function, congruence modulo, floor function, and Bahre Hasabe are fundamental concepts necessary for this development. Utilizing these concepts, the study successfully developed mathematical algorithms for the perpetual Ethiopian calendar and analogous calendars.