Earthline Journal of Mathematical Sciences

Generalized Sasakian Manifolds: Pseudosymmetry Characterizations Related to Some Important Curvature Tensors

2026-02-02T14:53:27+00:00

In this study, generalized Sasakian space forms are examined on $W_{5}-,W_{6},W_{7}$, and $W_{9}-$ curvature tensors. Moreover, special curvature conditions with the help of $W_{5}-,W_{6},W_{7}$, $W_{9}-$ pseudosymmetry and $W_{5}-,W_{6},W_{7}$, $W_{9}-$ Ricci pseudosymmetry are defined. The behavior for the generalized Sasakian space form is then represented in accordance with these concepts.

Refinements and Extensions of Classical Integral Inequalities: Hölder, Hardy, Minkowski, Clarkson, and Schweitzer

2026-02-02T16:05:22+00:00

Integral inequalities are a fundamental part of modern mathematical analysis and the theory of function spaces. In this paper, we present several refinements and extensions to classical integral inequalities, with a particular focus on those of Hölder, Hardy, Minkowski, Clarkson, and Schweitzer. First, we apply Hölder's inequality to find new refined bounds. Then, we establish Hölder-type inequalities using extended Young's inequalities. Consequently, we derive Hardy-type derivative inequalities with an optimal weight factor. After that, we introduce the Minkowski-Clarkson relation and variation for two functions. Lastly, we formulate a weighted generalisation of Schweitzer's inequality incorporating parametric functions. Concrete examples involving the beta and gamma functions demonstrate the sharpness and applicability of the proposed bounds, showing measurable improvements upon their classical counterparts.

A Collection of Inequalities Involving Power Exponential and Logarithmic Functions

2026-02-03T14:38:12+00:00

Power exponential functions and logarithmic functions, are two classes of functions which are ubiquitous in Mathematical Analysis with lots of contemporary applications. In this article, interpolation type inequalities involving power exponential and logarithmic functions are derived, and the techniques applied to derive these inequalities are not the usual that somebody encounters in the literature. All the results are derived using functional estimates and popular integral inequalities such as the Chebyshev integral inequality version. Most of the authors in the literature use monotonicity properties and series expansions, whereas in the current work the inequalities are rigorously proved using predominantly functional estimates, which is a technique more encountered in Functional Analysis and PDEs. To the best of our knowledge, the inequalities are new in the literature and the methods to yield the inequalities is novel and non trivial. This work serves in dual manner, having a research and pedagogical purpose and contributes to the field of Mathematical Analysis and Inequalities.

A Dual Sampling Approach for Improved Classifier Performance on Imbalance Datasets

2026-02-05T17:03:23+00:00

Background: The inability of traditional machine learning models to adequately classify minority instances in imbalanced datasets is a known challenge that militate against the successful application of these models in several real-world domains. To address this problem, several techniques including data sampling are mostly used. Though reducing the imbalance ratio via sampling is reported to improve classifier performance, most approaches do not consider the intra-class distribution of instances while sampling, which often lead to loss of significant information or on the contrary cause data redundancy. Methods: This study proposes a novel Dual Sampling Technique (DST) that minimises these challenges and enhances classifier performance on imbalance datasets. The technique proceeds by first clustering a training set into a number of clusters determined a priori using the elbow method. Sampling ratios are computed from each cluster and either random undersampling or a novel average oversampling technique or both are used to perform sampling in each cluster depending on the imbalance ratio. The resulting datasets are used to train Random Forest, Decision Tree and K-Nearest Neighbor classifiers and their performance evaluated. Findings: Experimental results showed that the performance of the classifiers significantly improved in most cases when the proposed technique is used to sample the training set prior to model building than when Random Undersampling (RUS), Random Oversampling (ROS), Synthetic Minority Oversampling Technique (SMOTE) and Cluster-Based Undersampling (CBU) are used. Novelty: The novelty of the proposed technique lies in the exploration of a unique concept that sought to minimise the imbalance ratio in datasets while maintaining their natural distribution by uniquely performing both undersampling and oversampling on the same dataset.

Chatterjea Polynomial Contraction Mapping Theorem in Metric Spaces with Application

2026-02-09T16:02:30+00:00

In this paper, we introduce the notion of polynomial Chatterjea contraction mapping in metric spaces, and obtain a fixed point theorem. Some consequences of the main result and a conjecture are stated. The conjecture is illustrated with an example, and the conjecture is used to show existence and uniqueness of solutions for a certain class of fractional differential equations.

An Analysis of Certain Properties of a Subclass of p-Valent Functions Determined by a Generalized Derivative Operator

2026-02-16T02:10:12+00:00

This study investigates a specific subclass of multivalent functions defined via the application of a generalized derivative operator. Various associated properties are examined, including coefficient inequalities, growth and distortion estimates, the characterization of extreme points, and the determination of radii of close-to-convexity, starlikeness, and convexity for these subclasses.

On New Sulaiman-type Hardy Integral Inequalities

2026-02-16T15:34:56+00:00

In this article, we present new integral inequalities that relate a function to its primitive in the context of $L^p$-spaces on finite intervals. These inequalities can be presented as variations of the Sulaiman-type Hardy integral inequalities. One of our approach combines a new rearrangement of key integrals with the Chebyshev integral inequality. We then derive reverse forms of these inequalities to demonstrate the flexibility and broad applicability of the method.

Mixed Convective EMHD Flow in Stratified Fluids over a Stretching Plate with Single Slip and Cross-Diffusion

2026-02-18T02:39:10+00:00

This study examines the influence of stratification on mixed convective electro-magnetohydrodynamic (EMHD) flow over a stretching plate in the presence of velocity slip conditions. The analysis is motivated by the relevance of EMHD flows in advanced thermal management systems, microfluidic devices, and electrically conducting fluids subjected to magnetic fields. The coupled governing equations describing momentum, heat transfer, and mass diffusion within the boundary layer are formulated as nonlinear partial differential equations. By introducing appropriate similarity transformations, these equations are reduced to a system of ordinary differential equations, enabling efficient numerical treatment.The resulting boundary value problem is solved numerically using a shooting technique based on Newton’s--Raphson method in conjunction with a fourth-order Runge–Kutta integration scheme. The effects of key physical parameters, including magnetic field strength, Biot number, chemical reaction parameter, Eckert number, Prandtl number, Lewis number, suction parameter, and thermal stratification, are systematically investigated. The numerical results reveal that an increase in magnetic field intensity, Biot number, chemical reaction rate, Eckert number, and thermal stratification parameter leads to a significant enhancement in thermal boundary layer thickness, indicating stronger thermal diffusion within the flow field. In contrast, higher values of the Prandtl number, suction parameter, and Lewis number are found to suppress thermal boundary layer development due to reduced thermal and mass diffusivity.

Overall, the findings provide valuable insights into the complex interplay between electromagnetic effects, stratification, and transport phenomena in EMHD flows, contributing to the improved design and optimization of engineering systems involving stratified electrically conducting fluids.