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
Job satisfaction is an attitude that is the outcome of balancing and summarizing of numerous individual likes and dislikes that an employee experiences while performing his or her job. People's wants and requirements are always changing and rising in modern society; when these needs are not met, people get unhappy. The purpose of this study was to assess the level of job satisfaction among academic staff members at Jimma University College of Natural Science. A crosssectional study design was used in this research. The subjects who were involved in the study were 280 academic staff members in College of Natural Science. The study used both descriptive and inferential statistics method of data analysis, which means from descriptive statistics frequency table, cross tab, and from inferential statistics chi-square test of association and Ordinary Logistic Regression have been used. Most of the academic staff members are moderately satisfied with their job, P-value=0.000 indicates there is enough evidence to reject the null hypothesis at α=5% level of significance. Then there is a association between employees, ages and job satisfaction and P-value=0.000 indicates there is relationship between the level of job satisfaction and relationship of employees who work in college of Natural science. Mean level of job satisfaction of the employees depends on the relationships of academic staff members. Age, relationship with workers, work environment, education level, and monthly salary shows relationship with the level of job satisfaction. Academic staff members are not very satisfied with their job due to monthly salary, employment, scarcity of time, and educational level.
The theory of rough sets has been classified into several directions where the information granules are generated using tolerances, pre-orders and binary relations. Maximal Compatibility Block (MCB) plays an important role in case of tolerance relations. In the present work, the lattice structure of rough sets generated by MCBs is explored. The concepts are applied to establish equivalence between the isomorphism of graphs with isomorphism of rough lattices constructed using MCBs.
A mathematical model that incorporates thermal emission, glutinous indulgence, heat source/sink, substance response, with suction was used to learn the MHD pour of Casson nanofluid in excess of a nonlinearly porous stretched page. There are a series of nonlinear ordinary differential equations that govern the biased differential equations through proper resemblance transformations, as well as then solved by the Homotopy Analysis Approach (HAM). Numerical data and plots are employed to examine the physical limitations on liquid speed, heat, and attentiveness. To examine the flow characteristics at the wall, the skin friction coefficients, local Nusselt digit, and Sherwood numbers are in addition evaluated. With much acclaim, a link between penetrable findings for specific cases is discovered.
Queuing models consisting of servers which may not work with full efficiency are modelled with the help of fractional differential equations. Such problems with partial activity of the server are designed with the help of differentialdifference equations involving fractional derivatives whose solutions are expressed in terms of Mittag-Leffler function. In this paper, we propose an alternative approach which gives a transient solution of fractional M/M/1 queue in matrix form. The results obtained by this new approach are justified by comparing them with solutions of classical queue which are available in the literature. Efficacy of the model can be assessed by computing its state probabilities and also measures such as expected number of customers in the system etc. Also, the variations in these measures with respect to partial activity of the server have been presented graphically and numerically. Further, a mathematical procedure to find optimal efficiency of the server has been discussed.
Data clustering is an unsupervised technique that can be used to partition the data into groups based on the similarities of the retrieved objects using different distance metrics like Euclidean, cosine, etc. In contrast to Euclidean, the cosine computes the object's similarity by considering both the magnitude and direction of the data vectors. As a result, it performed far better than a standard Euclidean distance metric in applications involving real-time data clustering. The initial k-value (clustering tendency) is required by top clustering techniques like k-means and hierarchical approaches to determine the clusters' quality. Users with knowledge can assign the k-value. However, sometimes the right k-value in such algorithms may need to be assigned. After a thorough review of the work, it was discovered that the visual technique known as visual assessment of (cluster) tendency (VAT) effectively addresses the clustering tendency issue. It uses the Euclidean metric to find the similarity features in its algorithm. Another enhanced visual technique, cosinebased VAT (cVAT), outperformed the VAT for text data and speech clustering applications. However, the similarity features are extracted about a single viewpoint in cVAT. This paper develops the multi-viewpoints-based cosine similarity measure (MVPCSM) for a more informative assessment. Instead of using a single reference point like a typical cosine measure, the MVPCSM generates precise similarity characteristics using several views. The performance of the existing and proposed technique (MVPCSM-VAT) is evaluated using clustering accuracy (CA) and normalized mutual information (NMI). It has been demonstrated that the proposed MVPCSM-VAT is 15-25% more efficient than VAT and cVAT in terms of the parameters of CA and NMI. The proposed method successfully obtains more quality data clusters than MVS-VAT.
