Statistical Analysis on Job Satisfaction of Academic Staff at College of Natural Science in Jimma University, Ethiopia: A Cross-Sectional Study
Tolerance Based Rough Algebras Induced by Blocks
Significance of Thermal Boundary Layer Analysis of MHD Chemically Radiative Dissipative Casson Nanofluid Flow over a Stretching Sheet with Heat Source
Transient Analysis of M/M/1 Fractional Queue
A Novel Multi-Viewpoints Based Cosine Similarity Visual Technique for an Effective Assessment of Clustering Tendency
An Introduction to Various Types of Mathematics Teaching Aids
A Simple Method of Numerical Integration for a Class of Singularly Perturbed Two Point Boundary Value Problems
A New Approach to Variant Assignment Problem
A Homotopy Based Method for Nonlinear Fredholm Integral Equations
Proof of Beal's Conjecture and Fermat Last Theorem using Contra Positive Method
Trichotomy–Squared – A Novel Mixed Methods Test and Research Procedure Designed to Analyze, Transform, and Compare Qualitative and Quantitative Data for Education Scientists who are Administrators, Practitioners, Teachers, and Technologists
Algorithmic Triangulation Metrics for Innovative Data Transformation: Defining the Application Process of the Tri–Squared Test
A New Hilbert-Type Inequality In Whole Plane With The Homogeneous Kernel Of Degree 0
Introducing Trinova: “Trichotomous Nomographical Variance” a Post Hoc Advanced Statistical Test of Between and Within Group Variances of Trichotomous Categorical and Outcome Variables of a Significant Tri–Squared Test
Surfaces in R3 with density
We introduce a notion of Kolmogorov complexity of unitary transformation, which can (roughly) be understood as the least possible amount of information required to fully describe and reconstruct a given finite unitary transformation. In the context of quantum computing, it corresponds to the least possible amount of data to define and describe a quantum circuit or quantum computer program. Our Kolmogorov complexity of unitary transformation is built upon Kolmogorov "qubit complexity" of Berthiaume, W. Van Dam and S. Laplante via mapping from unitary transformations to unnormalized density operators, which are subsequently "purified" into unnormalized vectors in Hilbert space. We discuss the optimality of our notion of Kolmogorov complexity in a broad sense.
In this work, a second derivative block method derived from a family of modified backward differentiation formula (bdf) type for solving stiff ordinary differential equations has been constructed. Choosing a step number, k = 4, four discrete methods with uniform order 7 are obtained using the multistep collocation approach. The stability properties of the new method have been established. The solutions of two problems have been computed and compared with the corresponding exact and other existing solutions. Solutions are presented on graphs and the associated absolute errors are compared in tables.
In this paper, the concepts of equiprime ideals and equiprime semi-modules in Boolean like semirings (now onwards denote ℜB) are introduced and illustrated with examples. It is established that if Ҏ is the equiprime ℜB–Ideal of the ℜB –module Ń then (Ҏ: Ń) is equiprime ideal of ℜB. Also it is approved that if Ń is an equiprime ℜB–module then (0: Ń) is an equiprime ideal of ℜB. Also, an important characterization of the equiprime ideal of ℜB in the semimodule structure over ℜB is put forward. Using this result, it can be proved that ℜB is equiprime if there exists a faithful equiprime ℜB–module Ń. Finally, it is proved that if Ń is an equiprime ℜB–module and Ϯ is an invariant subgroup of ℜB such that Ϯ is not contained in (0: Ń), then Ń is an equiprime Ϯ–module.
Mathematics Subject Classification: 16Y30, 16Y80.
The model of temperature profile in the flow of non-Newtonian second-order fluid, flowing in an annulus, is investigated. The boundary of annulus is considered porous. Appropriate similarity transformation is used to convert non-linear PDEs into non-linear ODEs. The obtained differential equations are solved numerically. The nature of the temperature profile is presented graphically for the various physical parameters.
The choice of appropriate and affordable procedures to boost oil recovery is usually recognized as one of the main challenges in reservoir development due to the huge demand for crude oil. Reservoir flow simulators are valuable tools for understanding and forecasting fluid flow in complex systems. The goal of this study is to run a mathematical model to evaluate the performance of various oil recovery methods, as well as to validate the model's accuracy with simulated field data. Thereby, the results of this developed model indicate that the model is approximately matched with the simulated field data. Enhanced oil recovery typically refers to chemical, miscible, thermal, and microbial processes. A system of nonlinear partial differential equations composed of Darcy's and mass conservation equations governs the model. The system is then numerically solved using the IMPEC (Implicit Pressure and Explicit Concentration) scheme by a finite difference method. We chose this approach because the experimental approaches are not only time consuming, but also costly. As a result, mathematical models could aid in the understanding of a reservoir and how such processes can be optimized to maximize oil recovery while lowering production costs. This paper provides a brief overview of mathematical modelling of various enhanced oil recovery methods, focusing on developing a generalized framework and describing some of the key challenges and opportunities.
