Showing posts with label parameter. Show all posts
Showing posts with label parameter. Show all posts

Monday, 8 September 2025

vSimplification of Lie's Similarity Transformation Method of Solving Coupled Nonlinear Partial Differential Equations and Exact Dyon Solutions SU(2)Yang-Mills-Higg's Field Equations | Chapter 5 | Research Updates in Mathematics and Computer Science Vol. 9

 

For any \(\mathrm{n}\) number of coupled nonlinear partial differential equations for spherically symmetric field equations of the typer \({ }^2\left(\frac{\partial^2 \phi_j}{\partial r^2}-\frac{\partial^2 \phi_j}{\partial t^2}\right)=F \_i(\phi \mathrm{j})\), where \(\mathrm{j}=1,2, \ldots \mathrm{n}\), are the number of dependent variables and \(\mathrm{F} \mathrm{i}\left(\phi \_\mathrm{j}\right)\) are any functions of dependent variables \(\phi_j, \mathrm{j}=1,2, . . \mathrm{n}\). and free of independent variables \(\mathrm{r}\) and \(\mathrm{t}\) then a similarity variable is found as \(\mathrm{s}(\mathrm{r}, \mathrm{t})=\mathrm{r} /\left[\left(r^2-t^2\right)-\kappa t / \tau+\kappa^2 /\left(4 \tau^2\right)\right]\), where \(\kappa\) and \(\tau \neq 0\) are arbitrary integration constants. Using \(s(r, t)\) above coupled partial differential equations can be transformed into coupled ordinary differential equations. This result may reduce lengthy calculations for finding similarity transformations of coupled partial differential equations. Using this similarity variable two exact Dyon solutions of spherically symmetric Yang-Mills-Higg's field equations are found with 'circular functions.' For which known solutions are with hyperbolic functions.

 

Author(s) Details

B.V. Baby

3/88, Jadkal Post, Udupi District, Karnataka State -576 233, India.

 

Please see the link:- https://doi.org/10.9734/bpi/rumcs/v9/607

Wednesday, 5 March 2025

Age, Sex and Season Related Changes in Growth Hormone and Cortisol Level of Black Bengal Goats during Pre-pubertal Growth Period | Chapter 8 | Contemporary Research and Perspectives in Biological Science Vol. 7

In present changing climatic conditions, the study of growth dynamics is of great significance as the demand for meat is always increasing due to increasing population. Improvement of functional longevity in goats is becoming highly relevant and is of great economic importance. Hence, determining animal growth and their inter-relationship and correlation with endocrine parameters in different seasons is imperative for determining the genetic potential and adaptation of the black Bengal breed for higher meat production. The goal of the present study was to find out the effect of sex and season on growth hormone and cortisol levels during different periods of pre-pubertal growth in summer and winter-born black Bengal kids of either sex reared under a free-range rearing system. Statistical analysis was done by using the software IBM-SPSS (Version 20.0). The general linear model technique repeated over age, groups and seasons was considered for all parameters under the study. The major finding of this study is that growth hormone was positively correlated (P ≤ 0.01) with growth parameters height, heart girth and linear length during summer and winter seasons. Cortisol was significantly (P ≤ 0.01) higher in the kids born in summer than in winter. Summer-born kids are more susceptible to stress. The levels of growth hormone can be predicted which were positively explained by age irrespective of sex. Due to climatic change, the effects of multiple environmental stressors on prepubertal growth are likely to increase. Hence, it is very critical to identify and protect agro-ecological zone-specific climate-resilient thermo-tolerant animals to sustain livestock production.

 

Author (s) Details

 

Shagufta Perveen
Department of Veterinary Physiology, College of Veterinary and Animal Sciences, Kishanganj, Bihar, India.

 

Pradip Kumar Das
Department of Veterinary Physiology, West Bengal University of Animal and Fisheries Sciences, Kolkata, West Bengal, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/crpbs/v7/3540

Wednesday, 19 February 2025

Predicting Rice Crop Yields in Prayagraj: A Comparison of Regression Techniques and Neural Networks | Chapter 3 | Geography, Earth Science and Environment: Research Highlights Vol. 5

This study investigates rice crop yield prediction in Prayagraj District, Uttar Pradesh, using twenty-nine years of historical weather and crop yield data from 1991 to 2019. The analysis divided the dataset into a calibration segment comprising 90% of the data over 26 years, with the remaining 10% used for validation. In our approach, 75.9% of the data was used to train an artificial neural network (ANN) model, and 24.1% was employed for testing and validation to ensure 100% model efficiency evaluation. Techniques such as stepwise linear regression and neural networks were applied to predict rice yields. The performance of these models was measured primarily by the Normalized Root Mean Squared Error (nRMSE), with the regression-based model achieving superior performance, indicated by the lowest nRMSE values. The study also noted the critical role of Bright Sunshine Hours, which demonstrated a significant predictive power with an nRMSE of 0.00025 and a coefficient of determination of 0.94.

 

Author (s) Details

 

Nilesh Kumar Singh
Sam Higginbottom University of Agriculture Technology and Sciences, Allahabad, Uttar Pradesh, India.

 

Shraddha Rawat
Sam Higginbottom University of Agriculture Technology and Sciences, Allahabad, Uttar Pradesh, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/geserh/v5/4249

Thursday, 30 January 2025

Simplification of Lie's Similarity Transformation Method of Solving Coupled Nonlinear Partial Differential Equations and Exact Dyon Solutions SU(2)Yang-Mills-Higg's Field Equations | Chapter 5 | Research Updates in Mathematics and Computer Science Vol. 9

For any n number of coupled nonlinear partial differential equations for spherically symmetric field equations of the typer^2 ( (ð^2 ϕ_j)/(∂r^2 )-(∂^2 ϕ_j)/(∂t^2 ))=F_i (ϕ_j ), where j=1,2,…n, are the number of dependent variables and F_i (ϕ_j ) are any functions of dependent variables ϕ_j,j=1,2,..n. and free of independent variables r and t then a similarity variable is found as s(r,t)=r/[(r^2−t^2 )−κt/τ+κ^2/(4τ^2 )], where κ and τ≠0 are arbitrary integration constants. Using s(r,t) above coupled partial differential equations can be transformed into coupled ordinary differential equations. This result may reduce lengthy calculations for finding similarity transformations of coupled partial differential equations. Using this similarity variable two exact Dyon solutions of spherically symmetric Yang-Mills-Higg's field equations are found with ‘circular functions.' For which known solutions are with hyperbolic functions.

 

Author (s) Details

 

B.V. Baby
3/88, Jadkal Post, Udupi District, Karnataka State -576 233, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/rumcs/v9/607