On Kolmogorov Complexity of Unitary Transformations in Quantum Computing

A Second Derivative Block Method Derived from a Family of Modified Backward Differentiation Formula (BDF) Type for Solving Stiff Ordinary Differential Equations

Equiprime Ideals and Equiprime Semimodules in Boolean Like Semirings

Numerical Solution of Temperature Profile in Annulus

Mathematical Modelling of EOR Methods

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

Surfaces in R

^{3}with densityIntroducing 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

* Associate Professor, Department of Mathematics, Aurora’s Technological Institute, Hyderabad, Telangana, India.

** Professor, Department of Mathematics, B. V. Raju Institute of Technology, Narsapur, Telangana, India.

A fuzzy set of X is a function with domain X and values in [0, 1]. If A is a fuzzy set and x ? X, then the function values A(x) are called the grade of membership of x in A. A mapping F from X to F(Y) is called a fuzzy mapping if for each x ? X; F(x) is a fuzzy set on Y and F(x)(y) denotes the degree of membership of y in F(x). Let X be a metric linear space and let W(X) denote the set of all fuzzy sets on X such that each of its α-cut is a nonempty compact and convex subset (approximate quantity) of X. A fuzzy mapping F from X to W(X) is called a fuzzy contraction mapping if there exists q ? (0, 1) such that D(F(x), F(y)) ≤ qd(x, y) for each x,y ? X. In this paper, two common coupled fixed point theorems for six self maps is proved under Wcompatible conditions in fuzzy metric spaces. Coupled fixed point and coupled point of coincidence for contractive mappings in complete fuzzy metric space is also obtained. The results obtain an extension of Theorem 2.1 by K. Pandu Ranga Rao, K. Rama Koteswara Rao, and S. Sedghi, (2014) [11]. Common Coupled Fixed Point Theorems in Fuzzy Metric Spaces. Finally, an example has been given to illustrate the usability of the main result.

* Professor, Department of Mathematics, SRKR Engineering College, Bhimavaram, Andhra Pradesh, India.

** Assistant Professor, Department of Mathematics, Vishnu Institute of Technology, Bhimavaram, Andhra Pradesh, India.

*** Retired Professor, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, Andhra Pradesh, India.

If a random variable follows a particular distribution, then the distribution of the inverse of that random variable is called inverted distribution. In this paper, the pdf of Inverse Half Logistic Distribution (IHLD) is derived. The mathematical properties of this distribution have been studied. The parameter is estimated from a complete sample using the classical maximum likelihood method. The estimating equations are modified to get simpler and efficient estimators. Two methods of modification are suggested. The sampling characteristics of the modified estimates are also presented for performance comparisons.

* Senior Assistant Professor, Department of Mathematics, Shri Shankaracharya Institute of Prof. Mgmt. and Tech., Raipur (C.G.), India.

** Associate Professor, Department of Mathematics, Government Engineering College, Raipur (C.G.), India.

In this paper, the authors have defined some properties in M-fuzzy metric spaces defined by Sedgi and Shobe [15] and they established some common coupled fixed point theorems in M-fuzzy metric space using an implicit relation. They defined the notion of (E.A.) property and common (E.A.) property for the pair of mappings defined in — fuzzy metric space. The obtained results extend, generalize, and improve several results of D - metric spaces defined by Dhage [3] and metric spaces to generalized fuzzy metric spaces or M-fuzzy metric spaces.

Associate Professor, Department of Curriculum and Instruction, North Carolina Central University, USA.

This monograph provides an epistemological rational for the Post Hoc testing of the transformative process of qualitative data into quantitative outcomes through the Tri–Squared Test introduced in the Journal on Mathematics, and further detailed in the Journal on Educational Technology, Journal on School Educational Technology, and in Journal on Educational Psychology articles. Advanced statistical measures of internal research instrument Trichotomous Repeated Measures and Trichotomous Variation of significant Transformative Trichotomy–Squared [Tri–Squared] research variables are analyzed. This additional novel approach to advanced Tri–Squared data analysis adds additional value to the mixed methods approach of research design that involves the holistic combination and comparison of qualitative and quantitative data outcomes. Multiple sequential mathematical models are provided that illustrate the entire process of advanced Tri–Analytic inquiry.

* Associate Professor, Department of Community Medicine, Konaseema Institute of Medical Sciences & Research Foundation, Amalapuram,
Andhra Pradesh, India.

** Retired Professor, Department of Statistics, Acharya Nagarjuna University, Nagarjunanagar, Andhra Pradesh, India.

Variable control charts are based on subgroup statistics and variation in the values of the subgroup statistics between subgroups. In this paper, sampling distribution of extreme order statistics for a given sample from Burr Type XII Distribution are considered and its percentiles are used to develop an extreme value control chart for a process variate. The approximation to Normal Distribution of the Burr Distribution is explored to develop the technique of popular ANalysis Of Means (ANOM) through Burr Distribution. Comparative study of the approximations are made. The results are explained by an illustration.