{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.
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.
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.
