arXiv subjects

Rajesh Kumar

Publications and source records attributed to Rajesh Kumar.

At least 55 records · Page 3Linked to original sources

arXiv2025

Novel Rewiring Mechanism for Restoration of the Fragmented Social Networks after Attacks

Real-world complex systems exhibit intricate interconnections and dependencies, especially social networks, technological infrastructures, and communication networks. These networks are prone to disconnection due to random failures or external attacks on their components. Therefore, managing the security and resilience of such networks is a prime concern, particularly at the time of disaster. Therefore, in this research work, network is reconstructed by rewiring/addition of the edges and robustness of the networks is measured. To this aim, two approaches namely (i) Strategic rewiring (ii) budget constrained optimal rewiring are adopted. While current research often assesses robustness by examining the size of the largest connected component, this approach fails to capture the complete spectrum of vulnerability. The failure of a small number of connections leads to a sparser network yet connected network. Thus, the present research work delves deeper into evaluating the robustness of the restored network by evaluating Laplacian Energy to better comprehend the system's behavior during the restoration of the network still considering the size of the largest connected component attacks.

cs.SI↗

arXiv2025

Rational Extension of Quantum Anisotropic Oscillator Potentials with Linear and/or Quadratic Perturbations

We present a comprehensive study of the rational extension of the quantum anisotropic harmonic oscillator (QAHO) potentials with linear and/or quadratic perturbations. For the one-dimensional harmonic oscillator plus imaginary linear perturbation ($iλx$), we show that the rational extension is possible not only for the even but also for the odd co-dimensions $m$. In two-dimensional case, we construct the rational extensions for QAHO potentials with quadratic ($λ\, xy$) perturbation both when $λ$ is real or imaginary and obtain their solutions. Finally, we extend the discussion to the three-dimensional QAHO with linear and quadratic perturbations and obtain the corresponding rationally extended potentials. For all these cases, we obtain the conditions under which the spectrum remains real and also when there is degeneracy in the system.

quant-ph↗

arXiv2025

Exploring the role of $d^*$ hexaquarks on quark deconfinement and hybrid stars

We investigate the impact of the $d^*$(2380) hexaquark on the equation of state (EoS) of dense matter within hybrid stars (HSs) using the Chiral Mean-Field model (CMF). The hexaquark is included as a new degree of freedom in the hadronic phase, and its influence on the deconfinement transition to quark matter is explored. We re-parametrize the CMF model to ensure compatibility with recent astrophysical constraints, including the observation of massive pulsars and gravitational wave events. Our results show that the presence of $d^*$ significantly modifies the EoS, leading to a softening at high densities and a consequent reduction in the predicted maximum stellar masses. Furthermore, we examine the possibility of a first-order deconfinement phase transition within the context of the extended stability branch of slow stable HSs (SSHSs). We find that the presence of hexaquarks can delay the deconfinement phase transition and reduce the associated energy density gap, affecting the structure and stability of HSs. Our results suggest that, as the hexaquark appearance tends to destabilize stellar configurations, fine tuning of model parameters is required to obtain both the presence of hexaquarks and quark deconfinement in these systems. In this scenario, the SSHS branch plays a crucial role in obtaining HSs with hexaquarks that satisfy current astrophysical constraints. Our work provides new insights into the role of exotic particles like $d^*$ in dense matter and the complex interplay between hadronic and quark degrees of freedom inside compact stellar objects.

nucl-th↗

arXiv2025

Neutron stars and Constraints for the Equation of State of Dense Matter

Neutron stars provide a natural laboratory for studying the properties of dense nuclear matter under extreme conditions. In this proceeding, we review our current understanding of dense isospin symmetric and asymmetric matter and neutron star physics. We focus on modern theoretical, experimental, and observational constraints, including first-principle calculations from lattice and perturbative Quantum Chromodynamics (QCD), as well as chiral effective field theory approaches at nuclear densities. From the experimental perspective, constraints on the equation of state arise from heavy-ion collisions, low-energy nuclear physics, and astrophysical observations, including neutron star masses, radii, and gravitational wave signatures from mergers. These multidisciplinary comparisons are crucial for bridging the gap between nuclear physics and astrophysical observations, in order to expand our knowledge of matter at supra-nuclear densities.

nucl-th↗

arXiv2025

Modern nuclear and astrophysical constraints of dense matter in a renormalized chiral approach

We explore the Quantum Chromodynamics (QCD) phase diagram's complexities, including quark deconfinement transitions, liquid-gas phase changes, and critical points, using the chiral mean-field (CMF) model that is able to capture all these features. We introduce a vector meson renormalization within the CMF framework, enabling precise adjustments of meson masses and coupling strengths related to vector meson interactions. Performing a new fit to the deconfinement potential, we are able to replicate recent lattice QCD results, low energy nuclear physics properties, neutron star observational data, and key phase diagram features as per modern constraints. This approach enhances our understanding of vector mesons' roles in mediating nuclear interactions and their impact on the equation of state, contributing to a more comprehensive understanding of the QCD phase diagram and its implications for nuclear and astrophysical phenomena.

nucl-th↗

arXiv2025

Identification of High Impedance Faults Utilizing Recurrence Plots

This paper presents a systematic approach to detecting High Impedance Faults (HIFs) in medium voltage distribution networks using recurrence plots and machine learning. We first simulate 1150 internal faults, including 300 HIFs, 1000 external faults, and 40 normal conditions using the PSCAD/EMTDC software. Key features are extracted from the 3-phase differential currents using wavelet coefficients, which are then converted into recurrence matrices. A multi-stage classification framework is employed, where the first classification stage identifies internal faults, and the second stage distinguishes HIFs from other internal faults. The framework is evaluated using accuracy, precision, recall, and F1 score. Tree-based classifiers, particularly Random Forest and Decision Tree, achieve superior performance, with 99.24% accuracy in the first stage and 98.26% in the second. The results demonstrate the effectiveness of integrating recurrence analysis with machine learning for fault detection in power distribution networks.

eess.SP↗

arXiv2025

Interacting mesons as degrees of freedom in a chiral model

We study the equation of state of hot and dense hadronic matter using an extended Chiral Mean Field (CMF) model framework where the addition is the inclusion of interactions of thermally excited mesons. This is implemented by calculating the in-medium masses of pseudoscalar and vector mesons, obtained through the explicit chiral symmetry-breaking and vector interaction terms in the Lagrangian, respectively, prior to applying the mean-field approximation. As a result, the in-medium meson contributions generate a feedback term to the CMF's equations of motion, which then modifies the equation of state. With this improvement, we quantify the effect on the equation of state of strongly interacting matter through comparisons with state-of-the-art lattice QCD results and other hadronic models like the Hadron Resonance Gas model. We find that the results of the updated hadronic CMF model with an improved meson description (mCMF) provide a better agreement with lattice-QCD data for thermodynamic state variables across a wide range of temperatures and baryon chemical potentials.

nucl-th↗

arXiv2025

A crayfish-optimized wavelet filter and its application to fault diagnosis

Industrial machine fault diagnosis ensures the reliability and functionality of the system, but identifying informative frequency bands in vibration signals can be challenging due to low signal-to-noise ratio (SNR), background noise, and random interferences. The wavelet filter is commonly used for this purpose, but its parameters are crucial for locating the informative frequency band to extract repetitive transients. This study utilizes a crayfish optimization algorithm (COA) to optimize the wavelet filter adaptively for extracting fault characteristics. COA uses correlated kurtosis (CK) as a fitness function while addressing issues related to inaccurate CK period through an updation process. The proposed methodology is applied to different industrial cases and compared with existing methods, demonstrating its superiority in extracting informative frequencies.

eess.SP↗

arXiv2025

Advancing machine fault diagnosis: A detailed examination of convolutional neural networks

The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) have emerged as a powerful tool, offering robust and accurate fault detection and classification capabilities. This comprehensive review delves into the application of CNNs in machine fault diagnosis, covering its theoretical foundation, architectural variations, and practical implementations. The strengths and limitations of CNNs are analyzed in this domain, discussing their effectiveness in handling various fault types, data complexities, and operational environments. Furthermore, we explore the evolving landscape of CNN-based fault diagnosis, examining recent advancements in data augmentation, transfer learning, and hybrid architectures. Finally, we highlight future research directions and potential challenges to further enhance the application of CNNs for reliable and proactive machine fault diagnosis.

cs.LG↗

arXiv2025

Building Neutron Stars with the MUSES Calculation Engine

Exploring the equation of state of dense matter is an essential part of interpreting the observable properties of neutron stars. We present here the first results for dense matter in the zero-temperature limit generated by the MUSES Calculation Engine, a composable workflow management system that orchestrates calculation and data processing stages comprising a collection of software modules designed within the MUSES framework. The modules presented in this work calculate equations of state using algorithms spanning three different theories/models: (1) Crust Density Functional Theory, valid starting at low densities, (2) Chiral Effective Field Theory, valid around saturation density, and (3) the Chiral Mean Field model, valid beyond saturation density. Lepton contributions are added through the Lepton module to each equation of state, ensuring charge neutrality and the possibility of $β$-equilibrium. Using the Synthesis module, we match the three equations of state using different thermodynamic variables and different methods. We then couple the complete equation of state to a novel full-general-relativity solver (QLIMR) module that calculates neutron star properties. We find that the matching performed using different thermodynamic variables affects differently the range obtained for neutron star masses and radii (although never beyond a few percent difference). We also investigate the universality of equation of state-independent relations for our matched stars. Finally, for the first time, we use the Flavor Equilibration module to estimate bulk viscosity and flavor relaxation charge fraction and rates (at low temperature) for Chiral Effective Field Theory and the Chiral Mean Field model.

nucl-th↗

arXiv2025

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations

Chart interpretation is crucial for visual data analysis, but accurately extracting information from charts poses significant challenges for automated models. This study investigates the fine-tuning of DEPLOT, a modality conversion module that translates the image of a plot or chart to a linearized table, on a custom dataset of 50,000 bar charts. The dataset comprises simple, stacked, and grouped bar charts, targeting the unique structural features of these visualizations. The finetuned DEPLOT model is evaluated against its base version using a test set of 1,000 images and two metrics: Relative Mapping Similarity (RMS), which measures categorical mapping accuracy, and Relative Number Set Similarity (RNSS), which evaluates numerical interpretation accuracy. To further explore the reasoning capabilities of large language models (LLMs), we curate an additional set of 100 bar chart images paired with question answer sets. Our findings demonstrate that providing a structured intermediate table alongside the image significantly enhances LLM reasoning performance compared to direct image queries.

cs.CL↗

arXiv2024

Weak convergence analysis for non-linear collisional induced breakage equation with singular kernel

The phenomenon of collisional breakage in particulate processes has garnered significant interest due to its wide-ranging applications in fields such as milling, astrophysics, and disk formation. This study investigates the analysis of the pure collisional breakage equation (CBE), characterized by its nonlinear nature with presence of locally bounded collision kernels and singular breakage kernels. Employing a finite volume scheme (FVS), we discretize the continuous equation and investigate the weak convergence of the approximated solution of the conservative scheme towards the continuous solution of CBE. A weight function is introduced to ensure the conservation of the scheme. The non-negativity of the approximated solutions is also shown with the assistance of the mathematical induction approach. Our approach relies on the weak $L^1$ compactness argument, complemented by introducing a stable condition on the time step.

math.AP↗

arXiv2024

Finite volume convergence analysis and error estimation for non-linear collisional induced breakage equation

This article focuses on the finite volume method (FVM) as an instrument tool to deal with the non-linear collisional-induced breakage equation (CBE) that arises in the particulate process. Notably, we consider the non-conservative approximation of the CBE. The analysis of weak convergence of the approximated solutions under a feasible stability condition on the time step is investigated for locally bounded breakage and collision kernels. Subsequently, explicit error estimation of the FVM solutions in uniform mesh having the kernels in the class of $W_{loc}^{1,\infty}$ space. It is also shown numerically for the first-order convergent scheme by taking numerical examples.

math.NA↗

arXiv2024

Rational Extension of Anisotropic Harmonic Oscillator Potentials in Higher Dimensions

This paper presents the first-order supersymmetric rational extension of the quantum anisotropic harmonic oscillator (QAHO) in multiple dimensions, including full-line, half-line, and their combinations. The exact solutions are in terms of the exceptional orthogonal polynomials. The rationally extended potentials are isospectral to the conventional QAHOs.

quant-ph↗

arXiv2024

Intelligent fault diagnosis of worm gearbox based on adaptive CNN using amended gorilla troop optimization with quantum gate mutation strategy

The worm gearbox is a high-speed transmission system that plays a vital role in various industries. Therefore it becomes necessary to develop a robust fault diagnosis scheme for worm gearbox. Due to advancements in sensor technology, researchers from academia and industries prefer deep learning models for fault diagnosis purposes. The optimal selection of hyperparameters (HPs) of deep learning models plays a significant role in stable performance. Existing methods mainly focused on manual tunning of these parameters, which is a troublesome process and sometimes leads to inaccurate results. Thus, exploring more sophisticated methods to optimize the HPs automatically is important. In this work, a novel optimization, i.e. amended gorilla troop optimization (AGTO), has been proposed to make the convolutional neural network (CNN) adaptive for extracting the features to identify the worm gearbox defects. Initially, the vibration and acoustic signals are converted into 2D images by the Morlet wavelet function. Then, the initial model of CNN is developed by setting hyperparameters. Further, the search space of each Hp is identified and optimized by the developed AGTO algorithm. The classification accuracy has been evaluated by AGTO-CNN, which is further validated by the confusion matrix. The performance of the developed model has also been compared with other models. The AGTO algorithm is examined on twenty-three classical benchmark functions and the Wilcoxon test which demonstrates the effectiveness and dominance of the developed optimization algorithm. The results obtained suggested that the AGTO-CNN has the highest diagnostic accuracy and is stable and robust while diagnosing the worm gearbox.

eess.SP↗

arXiv2024

Hybridized Projected Differential Transform Method For collisional-breakage equation

The non-linear collision induced fragmentation plays a crucial role in modeling several engineering and physical problems. In contrast to linear breakage, it has not been thoroughly investigated in the existing literature. This study introduces an innovative method that leverages the Elzaki integral transform as a preparatory step to enhance the accuracy and convergence of domain decomposition, used alongside the projected differential transform method to obtain closed-form or series approximations of solutions for the collisional breakage equation (CBE). A significant advantages of this technique is its capability to directly address both linear and nonlinear differential equations without the need for discretization or linearization. The mathematical framework is reinforced by a thorough convergence analysis, applying fixed point theory within an adequately defined Banach space. Additionally, error estimates for the approximated solutions are derived, offering more profound insights into the accuracy and dependability of the proposed method. The validity of this approach is demonstrated by comparing the obtained results with exact or finite volume approximated solutions considering several physical examples. Interestingly, the proposed algorithm yields accurate approximations for the number density functions as well as moments with fewer terms and maintains higher precision over extended time periods.

cs.CE↗

arXiv2024

Effect of Perturbation and Topological Structure on Synchronization Dynamics in Multilayer Networks

The way the topological structure transforms from a decoupled to a coupled state in multiplex networks has been extensively studied through both analytical and numerical approaches, often utilizing models of artificial networks. These studies typically assume uniform interconnections between layers to simplify the analytical treatment of structural properties in multiplex networks. However, this assumption is not applicable for real networks, where the heterogeneity of link weights is an intrinsic characteristic. Therefore, in this paper, link weights are calculated considering the node's reputation and the impact of the inter-layer link weights are assessed on the overall network's structural characteristics. These characteristics include synchronization time, stability of synchronization, and the second-smallest eigenvalue of the Laplacian matrix (algebraic connectivity). Our findings reveal that the perturbation in link weights (intra-layer) causes a transition in the algebraic connectivity whereas variation in inter-layer link weights has a significant impact on the synchronization stability and synchronization time in the multiplex networks. This analysis is different from the predictions made under the assumption of equal inter-layer link weights.

cs.SI↗

arXiv2024

Deep Generative Attacks and Countermeasures for Data-Driven Offline Signature Verification

This study investigates the vulnerabilities of data-driven offline signature verification (DASV) systems to generative attacks and proposes robust countermeasures. Specifically, we explore the efficacy of Variational Autoencoders (VAEs) and Conditional Generative Adversarial Networks (CGANs) in creating deceptive signatures that challenge DASV systems. Using the Structural Similarity Index (SSIM) to evaluate the quality of forged signatures, we assess their impact on DASV systems built with Xception, ResNet152V2, and DenseNet201 architectures. Initial results showed False Accept Rates (FARs) ranging from 0% to 5.47% across all models and datasets. However, exposure to synthetic signatures significantly increased FARs, with rates ranging from 19.12% to 61.64%. The proposed countermeasure, i.e., retraining the models with real + synthetic datasets, was very effective, reducing FARs between 0% and 0.99%. These findings emphasize the necessity of investigating vulnerabilities in security systems like DASV and reinforce the role of generative methods in enhancing the security of data-driven systems.

cs.CV↗