International Journal of Applied Science https://j.ideasspread.org/index.php/ijas <p>International Journal of Applied Science (IJAS) is an international, double-blind peer-reviewed, open-access journal, published by IDEAS SPREAD INC. It publishes original research, applied, and educational articles in all areas of applied science. It provides an academic platform for professionals and researchers to contribute innovative work in the field.<br>Authors are encouraged to submit complete, unpublished, original works that are not under review in any other journals. The scopes of the journal include, but are not limited to, the following fields: Agriculture, Biological Engineering and Application, Applied Mathematics and Statistics, Applied Physics and Engineering, Applied Chemistry and Materials Sciences, Civil Engineering and Architecture, Computer and Information Sciences and Application, Energy, Environmental Science and Engineering, Mechanics, Metrology, Military Science, Space Science, Sports Science, Ergonomics, Health Sciences, Fisheries science, Food Science, Forestry and all the fields related to applied science.<br>The journal is published in both print and online versions. The online version is free access and download.</p> IDEAS SPREAD INC en-US International Journal of Applied Science 2576-7240 <p>Copyright for this article is retained by the author(s), with first publication rights granted to the journal.<br>This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).</p> On Farm Performance Evaluation of Exotic Chickens in Central Tigray, Northern Ethiopia https://j.ideasspread.org/index.php/ijas/article/view/1272 <p class="text"><span lang="EN-US">The study was carried out on two purposively selected districts of central zone of Tigray, viz., Mereb Leke and Tahtay Michew. The aim of the study was to evaluate the on-farm performance of three exotic chicken strains under the farmer condition. A total of 96 households (48 household per district) were participated and then the selected strains (Sasso Rhode Island Red, Kuroiler and Koekoek) were distributed. GLM procedure was used for the on-farm data analysis. Data collected during the entire study were, growth data, fertility and hatchability, egg production, weight and age at point of lay. Better overall average daily body weight gains were achieved from 12-16 and 16-20 weeks by kuroiler chicken strains with value of 10.29, 13.68 gram respectively. Egg at first lay of SRIR and Kuroiler strains were 23.69 and 25.25 weeks respectively. The overall average egg production in the study area was 63.36% which was scored by SRIR strain. According to this study, based on their fast-growing kuroiler strain and based on their egg production potential SRIR strain were recommended to the study area and like agro ecologies. </span></p> Berhe Teklay ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2024-08-07 2024-08-07 7 2 p1 p1 10.30560/ijas.v7n2p1 Ergodic Foundations of Langevin-Based MCMC https://j.ideasspread.org/index.php/ijas/article/view/1297 <p>In this work, we provide a comprehensive theoretical analysis of Langevin diffusion and its applications to Markov Chain Monte Carlo (MCMC) methods. We establish the ergodicity of continuous-time Langevin diffusion processes, proving their convergence to target distributions under suitable regularity conditions. The analysis is then extended to discrete-time settings, examining the properties of the Unadjusted Langevin Algorithm (ULA) and the Metropolis-Adjusted Langevin Algorithm (MALA). Employing tools from stochastic processes, ergodic theory, and Markov chain theory, we establish strong convergence results using Foster-Lyapunov drift conditions, coupling arguments, and geometric ergodicity. The paper explores connections between Langevin diffusion and optimal transport theory, highlighting recent developments in adaptive methods, transport map accelerated MCMC, and applications to high-dimensional Bayesian inference. Our theoretical results provide insights into algorithm design, parameter tuning, and convergence diagnostics for Langevin-based MCMC methods, bridging the gap between theory and practice in the development of efficient sampling algorithms for complex probability distributions.</p> Ruoming Geng ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2024-09-05 2024-09-05 7 2 p8 p8 10.30560/ijas.v7n2p8