bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing Switched Reluctance Motors For Electric Vehicles: A Multi-Criteria Decision-Making Approach Using DEMATEL
Authors: K. Indirajith  
Published: January 2025

Introduction: Switched Reluctance Motors (SRMs) are gaining prominence in electric vehicles (EVs) due to their simplicity, reliability, and cost-effectiveness. However, challenges such as torque ripple, efficiency optimization, and design complexities limit their performance. Addressing these challenges requires a systematic approach to evaluate and optimize critical factors influencing SRM design and operation.
This study applies a multi-criteria decision-making (MCDM) framework using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to enhance SRM performance. By analyzing interdependencies among 13 critical criteria, the approach identifies key factors such as efficiency and torque ripple that significantly impact motor performance and cost-effectiveness. The findings provide actionable insights for optimizing SRM design, contributing to the advancement of EV technologies and supporting sustainable transportation goals.
Objective: To enhance the performance of switched reluctance motors (SRMs) in electric vehicles (EVs) using multi-criteria decision-making (MCDM) analysis with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method.
Methods: A 13 × 13 criteria matrix assessed the interrelationships between critical factors influencing SRM design and operation. The DEMATEL method established interdependencies among the criteria and developed a cause-and-effect relationship model.
Results: Efficiency and torque ripple were identified as the most influential criteria, significantly impacting the reliability and cost-effectiveness of SRM technology. The analysis offered insights into optimizing SRM design by emphasizing key drivers. Conclusions: Integrating DEMATEL into the MCDM framework effectively addresses complex interrelated challenges in motor design and operation. This structured data-driven method advances decision-making for EV technologies and tackles critical challenges for sustainable transportation.
Even Vertex Odd Edge Root Square Mean Labelling Of Path Related Graphs
Authors: K. N. Babu  
Published: January 2025

Consider G be a graph with p vertices and q edges. A graph G is said to be even vertex odd edge root square mean labelling if there exist an injective map f :V(G)→{0,1,2,3,…,2q} such that the induced edge labels are odd and distinct which can be obtained by f^(* ) (e)=⌈√((〖f(u)〗^2 +〖f(v)〗^2)/2)⌉ or ⌊√((〖f(u)〗^2 +〖f(v)〗^2)/2)⌋. Any graph which admits even vertex odd edge root square mean labelling then it is called as even vertex odd edge root square mean labelling graph.
Optimizing Phasor Measurement Units Placement in Power Networks Using Betweenness Centrality
Authors: Saravanan M  
Published: January 2025

Introduction: Centrality concepts play a crucial role in determining the significance of a vertex based on its necessity. A network always prefers the shortest path, also known as the geodesic path, for information transmission between nodes. Betweenness centrality, which reduces geodesic distance, makes it easier to strategically position phasor measurement units (PMUs) in a power network. We present a research paper that describes an algorithm specifically designed for PMU placement in power networks. The algorithm's performance is assessed by running it on the widely used IEEE 14 and IEEE 30 buses, allowing for comparison with results obtained using other methodologies.
Automatic Urban Change Detection using LANDSAT-8 Satellite Images and Deep Learning Techniques in Australian Capital Territory (ACT), Australia
Authors: Ninad More  
Published: January 2025

The Remote Sensing based Satellite data is useful as a very powerful tools for providing a complete information for an accurate urban area monitoring. Identifying the urban change detection is a main part of professional urban drafting, regional based urban development in fraction of low cost effective along with less time duration compared to common methods (e.g. manually field survey report, Aerial Imagery etc.). The main goal of the proposed work is to monitoring various changes in features on the complete surface of earth of various pixel resolution can be carried out by low-cost remote sensing-based LANDSAT-8 satellite images. The urban change detection is examining the various changes in urban area between two periods: 1) Baseline period 2) More recent period. In the previously, the classical change detection (CD-Algorithm) is inaccurately identifying both the spatial information and various scale variations within satellite images. To overcome the problem, the present research work introduces an advanced Deep Learning based novel Change Detection (Bitemporal_CD) technique that accurately extracts complete spatial information at various level of scales variations to simply address both issues. The new proposed model is based on Multi Scale and Multi Depth (MSMD) approach deep neural network that generates all binary information change based map that simply integrates with complete information of different sizes of patches at various decision parameter. It is used LANDSAT-8 satellites images originally from Australian Capital Territory (ACT), Suburban Nicholls, and District: Gungahlin- Australia, because total area of the ACT is 2,351.7 km2 of which 61% area is hilly or complete mountainous. The proposed advanced deep learning model is estimated in comparison with both Bi_Temporal Change Vector Analysis (Bi_Temporal CVA) and Support-based Vector Machine (SVM). On the basis of Deep learning change detection results, our proposed model shows a notable advancement in the performance of Cohen’s_kappa coefficient (KC) compared to both SVM as well as Bi_Temporal CVA, with high increases of approximately 12.87% and 30.37% individually. Finally, the proposed Multi Scale and Multi Depth approach deep neural network model performs superior in detecting various changes including across all level of metrics with high accuracy.
Open Maps via δ-open Sets in Pythagorean Fuzzy Topological Spaces and its Applications
Authors: A. Vadivel  
Published: January 2025

In this paper, we introduce the concept of Pythagorean fuzzy (resp. , , or )-open mappings are introduced and their properties are discussed. In current scenario people with symptom of Covid-19 like fever, cough, sneezing, sore throat, loss of taste and smell etc., were panic about the disease, and the diagnosis of Covid-19 takes many hours and people cannot go for the test frequently. Some other diseases like flu, pneumonia, cold etc., also has the same symptoms. Each patients has unique experience of that particular symptom and some time they may not experience that symptom even though they were affected by the Covid-19. Also, in this paper we tried to diagnosis Covid-19 with the help of picture fuzzy sets which helps to record all symptoms in prÃ©cised manner.
Introduction: This paper introduces the concept of Pythagorean fuzzy open mappings and their relevance to COVID-19 symptom diagnosis. Due to the overlapping symptoms of COVID-19 with other illnesses, a Pythagorean fuzzy set approach is utilized to more accurately capture and analyze symptom patterns.
Objectives: The objective is to develop a precise mapping model that utilizes Pythagorean fuzzy sets to differentiate between COVID-19 symptoms and other similar conditions, aiming to improve diagnostic speed and accuracy.
Methods: The method involves formulating fuzzy sets based on COVID-19 symptoms, computing distances (Hamming, Euclidean) between patients' symptoms and ideal symptom patterns, and identifying cases with higher COVID-19 risk.
Results: The approach successfully identifies patients with symptoms closest to COVID-19 indicators, offering a more systematic and timely diagnosis. Results suggest that this fuzzy set model can enhance early detection and intervention for affected individuals
Conclusions: In this paper, Some new notions of strongly Pythagorean fuzzy open (closed) maps called Pythagorean fuzzy -open and Pythagorean fuzzy -closed maps are introduced and discussed their relationship between their near mappings with examples. Also, we have tried to diagnosis Covid-19 with the help of Pythagorean fuzzy sets which helps to record all symptoms in prÃ©cised manner. In future, researchers can extend this model to other extensions of fuzzy sets such as rough sets and utilize the interdependency among the various evaluation criteria for better judgement.
Analytical and Computational Investigation of Non-Steady State Reaction and Kinetics at Spherical Ultramicroelectrodes Concerning Conducting Polymer Modification using Homotopy Perturbation Method
Authors: A. Uma  
Published: January 2025

In this work, mathematical modelling of non-steady state reaction and kinetics at spherical ultramicroelectrodes within conducting polymer modification is considered. The main objective of this work is to propose a new analytical formulation for the system of nonlinear non-steady state reaction diffusion equation in spherical ultramicroelectrodes. Employing Homotopy Perturbation Method, the concentrations of species, mediator and current may all be obtained analytically for all conceivable experimental results of the parameter. The accumulated analytical outcomes are analyzed with numerical simulations and implemented to investigate various parameters. By comparing the analytical solution with numerical findings, the accuracy of the method is presented. In order to better understand the system dynamics, a numerical simulation of the issue is also provided through Matlab Software. The new analytical results contribute to optimizing the consistency of this model. These novel approaches produce a compact set of analytical approximations that possess straightforward to compute and verify as well.
Inverse Independent Outer Connected Domination Number of Few Classes of Graphs
Authors: G Menon  
Published: January 2025

In a graph G = (V, E), a vertex set D ⊆ V such that for every vertex v ∈/ D the vertex v is adjacent to atleast one vertex in D and no vertex in D is adjacent to each other, then ,D is the independent dominating set.when the vertex set V D V con- tains a independent dominating set, say D′, then D′ is called the inverse independent domiating set. If the subgraph induced by V − D′ is connected then D′ is called the inverse independent outer connected dominating set. The minimum cardinality of such set of vertices is called the Inverse Independent Outer Connected Domination Number denoted by γ˜I−OC (G). In this paper we will be discussing γ˜I−OC S(n, k), γ˜I−OC S+(n, k), γ˜I−OC S++(n, k), γ˜I−OC (Sn), γ˜I−OC (G1 ◦ G2) where S(n, k), S+(n, k), S++(n, k), Sn are Sierpinski Graph, Extended Sierpinski Graphs and Sierpinski Gasket Graph respectively and G1 and G2 are two standard graphs.
A Characterization of Rings in which every Semi-Hopfian Module is Noetherian
Authors: Mankagna Albert Diompy  
Published: January 2025

Let be a ring and an -module. It is well known that Hopfian modules are semi-Hopfian, but the converse is not always true. For instance, if is a prime and is any direct sum of copies of , then is semi-Hopfian but not Noetherian. In this paper, we introduce and study a new class of rings, called SHN-rings, defined as rings for which every semi-Hopfian module is Noetherian. We investigate the structural properties of SHN-rings and explore their relationships with other well-known classes of rings and modules. Over commutative rings, we establish that SHN-rings coincide with Köthe rings, -rings, pure-semisimple rings, and Artinian principal ideal rings. Furthermore, we show that over commutative SHN-rings, the notions of multiplication modules, cyclic modules, finitely generated modules, and Noetherian modules are equivalent. Additionally, we prove that in the setting of regular rings, semiprime Artinian rings, -rings, and SHN-rings are equivalent.
Multi-Class Deep Learning-Based Enhanced Image Segmentation and Feature Extraction Technique for Early Classification of Skin Diseases
Authors: Ritika Sharma  
Published: January 2025

Skin disorders pose great health issues among millions of people across the globe and thus require accurate diagnosis and treatment. This research article proposed an enhanced deep learning-based image segmentation and feature extraction approach for the early categorization of human skin diseases utilizing the Dermnet dataset of image samples classified under 23 categories. Input images are applied during the preprocessing stage to an enhanced image segmentation technique known as enhanced holistically nested edge detection (EHNED) toward detail enrichment in edges. Further, for multiple feature extraction, the model used EfficientNet-B0 and extracted 1280 features from an image represented as a 1D array. The extracted features form a baseline for training and testing CNNs on the diagnosis of skin diseases. It tries to bridge all the loopholes from previous research by observing an extremely vast and heterogeneous dataset and uses better techniques for segmentation and feature extraction. The proposed framework is likely to increase the recall performance metric to 91%, specificity to 96%, and ability to enable early diagnosis in case of skin diseases. This enhancement is supposed to follow more effective management and better patient outcomes. Findings in this study are likely to contribute toward dermatology research and promote intelligent systems in diagnostics for automatic classification of skin diseases.
Magnetohydrodynamic Effect on 3D Nanofluid Flow via Stretching/Shrinking Surface
Authors: S. Rakmaiah  
Published: January 2025

This study presents a numerical technique to analyze the MHD effect on the motion of 3D NFs via shrinking or SS under convective BCs. The investigation incorporates convective and VSC. The gov. eqs. are transformed into a set of coupled NLODEs using appropriate ST. These transformed nonlinear equations are calculated using R-K-F method combined with the shooting technique. The influence of various physical parameters on , and distributions is illustrated graphically. Other that, the study evaluates SF coefficient and HTR for different NFs parameters.
Theoretical Analysis of Nonlinear Reaction-diffusion Equation on Immobilised Alpha-Chymotrypsin in Acetonitrile Solvent
Authors: A. Dorathy Cathrine  
Published: January 2025

The study focuses on immobilised Alpha-chymotrypsin operating under kinetic constraints. This model incorporates equations representing the inherent kinetics of four distinct reactions, along with the experimentally analysed physical properties of three different support materials, to estimate the immobilised biocatalyst's initial activity and nucleophile selectivity. Approximate analytical solutions of acyl donor and nucleophile substrate concentrations under steady-state conditions are derived concerning all diffusion parameters using Akbari Ganji and homotopy perturbation methods. We examine our analytical results with numerical data obtained using MATLAB software to assess accuracy. It is shown that the derived analytical results closely match the numerical results. The model reliably replicates experimentally observed trends in initial rate and nucleophile selectivity across varying enzyme loadings.
A Nonlinear Analytical Approach for Video Compression in Telecommunication
Authors: Sanket B. Kasturiwala  
Published: January 2025

Introduction: The shift from analog to digital video has revolutionized communication by offering noise resistance, clarity, ease of transmission, and interactive user experiences. Digital video supports greater content capacity, catering to diverse viewer preferences and enhancing multimedia consumption. A nonlinear analytical approach for video compression techniques, such as transform coding, predictive coding, motion compensation, and entropy coding, play a vital role in applications like video conferencing and live streaming, enabling real-time transmission while efficiently utilizing network resources and storage. The integration of these technologies fosters a dynamic, high-quality multimedia ecosystem
Objectives: Research and development in nonlinear video compression objectives focus on reducing video file sizes without compromising quality by leveraging redundancies and characteristics of video data. This is vital objectives for applications like video streaming, conferencing, digital television, and multimedia distribution, driving improvements in storage efficiency, bandwidth utilization, and user experience.
Methods: Existing methodologies for block matching motion estimation, such as FS, TSS, NTSS, DS, and ETSS, have shown limitations in video compression. To address this, a novel "Nonlinear approach" combining the Hexagonal Search (HS) and Efficient Three Step Search (ETSS) algorithms has been proposed. This technique aims to reduce redundancy, minimize search points, and improve video compression efficiency. It also delves into the complexities of motion estimation and compensation, providing insights from the conceptualization of video sequences to the implementation of motion-compensated coding techniques.
Results: All the parameters such as Sum of Absolute Difference (SAD), Mean of Absolute Difference (MAD), Mean Square Error (MSE), Peak-Signal to Noise Ratio (PSNR) and Compression Ratio are applied for the statistical performance study. Nonlinear approach gives appreciable results as compared with the existing methodologies. Video quality as also assessed through various nonlinear compression techniques give better results.
Conclusions: A Nonlinear approach to block matching motion estimation has been introduced, integrating the strengths of Efficient Three-Step Search (ETSS) and Hexagonal Search (HS). This method delivers superior results while requiring fewer search points than traditional algorithms such as TSS, FS, NTSS, and 4SS. Hexagonal Search (HS) also outperforms Diamond Search (DS) and Cross Diamond Search (CDS) in efficiency. Rigorous testing using the Akiyo.avi standard database confirms the Nonlinear approach's effectiveness, particularly in reducing computational complexity, making it a more advanced motion compensation technique compared to ETSS.
Comprehensive Numerical Analysis of Fluid Flow Characteristics in a Lid-Driven Cavity with a Central Obstacle: Flow Physics
Authors: Dalmalka Praveen Kumar  
Published: January 2025

Boundary layer flows and vortex dynamics are critical in understanding fluid motion in confined geometries. The lid-driven cavity (LDC) flow is a classical benchmark for analysing fluid behaviour under varying conditions, especially when obstacles are introduced. While past studies have explored primary and secondary vortex structures, the modulation of enstrophy and palinstrophy caused by obstacles has been underexplored. This study aims to bridge that gap by systematically investigating the effect of Reynolds numbers and obstacle aspect ratios on flow properties in a 2D LDC. To analyse the influence of Reynolds number (Re) and obstacle Aspect ratio (AR) on enstrophy (Z) and palinstrophy (P). To evaluate the enhancement in vortex dynamics and flow topology caused by introducing square obstacles at various aspect ratios within the cavity. To identify optimal configurations for improving fluid transport and mixing efficiency. An unsteady, laminar, incompressible flow of Newtonian fluid was simulated by the Finite volume-based Fractional Step Method. Square obstacles with aspect ratios AR=0L,0.4L, 0.5L, and 0.6L were placed at the centre of the cavity with numerical study for a range of Re=10,100,400, and 1000. The interaction between AR and Re dictates vortex dynamics in LDC flows. Higher AR disrupts flow symmetry through sharp-edge interactions, while increased Re elevates inertial effects, causing vortex splitting, secondary vortex formation, and improved fluid mixing. Obstacles boost Z and P by up to 53.08% and 50.85%, with the most significant increases for AR = 0.6L at Re = 1000 compared to a cavity without an obstacle. This suggests stronger vorticity gradients and enhanced mixing due to AR = 0.6L, as the obstacle occupies more of the cavity, restricting flow and creating stronger shear zones near the walls and the obstacle. At high Re, inertial forces dominate, leading to stronger vortex shedding, and more pronounced vorticity changes near sharp edges. Increasing Re from 10 to 1000 for AR = 0.6L raises Z and P by approximately 36.34% and 43.55%, illustrating a shift from viscous-dominated laminar to inertia-dominated flow, enhancing vortex strength, sharpening vorticity gradients, and improving mixing efficiency. The interplay between AR and Re dictates vortex dynamics, with higher AR disrupting symmetry and higher Re amplifying inertial effects, leading to vortex splitting, enhanced secondary vortices, and improved mixing. The increasing obstacle height amplifies Z and P, leading to improved fluid mixing and vortex dynamics, serving as effective tools for flow control and mixing enhancement in industrial and engineering applications.
The T-Generalized P - K Wright Function and its Properties
Authors: Arvind Maharshi  
Published: January 2025

In this paper, we introduce t-Generalized p-k Wright function (_r^(t,p))Ψ_s^k and discuss about its convergence condition. We obtain functional relation between t-Generalized p-k Wright function, Generalized p-k Wright function and generalized Wright function and some special cases have also been discussed. We obtain integral representation of t-Generalized p-k Wright function and discussed its properties.
Invariant Submanifolds of Generalized Sasakian-Space-Forms
Authors: Sagarika Nandy  
Published: January 2025

The new characterizations of invariant submanifolds of generalized Sasakian-space forms (SSF) in terms of their behavior with respect to the various curvature tensors are obtained in this work. By examining the connections between these submanifolds' second fundamental form σ and certain curvature tensors Wi with i = 2, 3, 4, 6, 7, we derive necessary and sufficient conditions for their geodesicity. We show that total geodesicity corresponds to the claim that tensor products vanish, Q(σ, Wi) = 0, subject to different non-degeneracy conditions for each curvature tensor. The essential characterization comes from the W6 curvature tensor, which suffices to fulfil 2n(f1−f3)≠0, and all other tensors lead to complementary constraints concerning the structural functions f1, f2, and f3. These findings offer various avenues for studying the geometric nature of invariant submanifolds and enhance our insights into the behaviour of generalized Sasakian-space-forms.
A Non-linear analysis of Wall Putty and ConcreteCubes for Self-Cleaning usingTitanium Dioxide
Authors: A. Hemamathi  
Published: January 2025

Concrete is the most common construction materials for building construction. However, in urban region the surface of concrete is contaminated due to air pollution. Self-cleaning construction materials have the ability to clean themselves by breaking down and removing dirt, pollutants, and other contaminants without the need for human intervention. Photocatalytic materials such as titania (TiO2) and zinc oxide (ZnOgenerally possess self- cleaning properties in it and is introduced in this study. This paper reviews the Non-linear analysis ofTitania and Zinc oxide on concrete and wall putty. The optimum dosage of Titania is investigated and the its periodic absorption is noticed to check the removal of dirt from the surface of concrete. The same principle is applied on wall putty panel. The results of the study showed that TiO2 can be used upto 10%. Increase in dosage decreased the tensile strength of finished putty layer.