In this study, we focus into the non-relativistic wave equation described by the Schrodinger equation, specifically considering angular-dependent potentials within the context of a topological defect background genera...
详细信息
In this study, we focus into the non-relativistic wave equation described by the Schrodinger equation, specifically considering angular-dependent potentials within the context of a topological defect background generated by a cosmic string. Our primary goal is to explore quasi-exactly solvable problems by introducing an extended ring-shaped potential. We utilize the Bethe ansatz method to determine the angular solutions, while the radial solutions are obtained using special functions. Our findings demonstrate that the eigenvalue solutions of quantum particles are intricately influenced by the presence of the topological defect of the cosmic string,resulting in significant modifications compared to those in a flat space background. The existence of the topological defect induces alterations in the energy spectra, disrupting ***, we extend our analysis to study the same problem in the presence of a ring-shaped potential against the background of another topological defect geometry known as a point-like global monopole. Following a similar procedure, we obtain the eigenvalue solutions and analyze the results. Remarkably, we observe that the presence of a global monopole leads to a decrease in the energy levels compared to the flat space results. In both cases, we conduct a thorough numerical analysis to validate our findings.
The SCADA systems in the Smart Grid Network (SGN) are increasingly facing cyber threats and divers attacks due to their known proprietary vulnerabilities, most often leading to power instability and cascading failures...
详细信息
We consider the matching augmentation problem (MAP), where a matching of a graph needs to be extended into a 2-edge-connected spanning subgraph by adding the minimum number of edges to it. We present a polynomial-time...
详细信息
Federated learning (FL) and split learning (SL) enable collaborative model training while preserving data privacy. However, these approaches are inherently designed to create a single model and involves sharing of sen...
详细信息
Existing statistical learning guarantees for general kernel regressors often yield loose bounds when used with finite-rank kernels. Yet, finite-rank kernels naturally appear in several machine learning problems, e.g. ...
详细信息
We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the c...
详细信息
We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.
In the last two decades, the number of rapidly increasing cyber incidents (i.e., data theft and privacy breaches) shows that it is becoming enormously difficult for conventional defense mechanisms and architectures to...
详细信息
In the last two decades, the number of rapidly increasing cyber incidents (i.e., data theft and privacy breaches) shows that it is becoming enormously difficult for conventional defense mechanisms and architectures to neutralize modern cyber threats in a real-time situation. Disgruntled and rouge employees/agents and intrusive applications are two notorious classes of such modern threats, referred to as Insider Threats, which lead to data theft and privacy breaches. To counter such state-of-the-art threats, modern defense mechanisms require the incorporation of active threat analytics to proactively detect and mitigate any malicious intent at the employee or application level. Existing solutions to these problems intensively rely on co-relation, distance-based risk metrics, and human judgment. Especially when humans are kept in the loop for access-control policy-related decision-making against advanced persistent threats. As a consequence, the situation can escalate and lead to privacy/data breaches in case of insider threats. To confront such challenges, the security community has been striving to identify anomalous intent for advanced behavioral anomaly detection and auto-resiliency (the ability to deter an ongoing threat by policy tuning). Towards this dimension, we aim to review the literature in this domain and evaluate the effectiveness of existing approaches per our proposed criteria. According to our knowledge, this is one of the first endeavors toward developing evaluation-based standards to assess the effectiveness of relevant approaches in this domain while considering insider employees and intrusive applications simultaneously. There have been efforts in literature towards describing and understanding insider threats in general. However, none have addressed the detection and deterrence element in its entirety, hence making our contribution one of a kind. Towards the end of this article, we enlist and discuss the existing data sets. The data sets can help
This study focuses on the role and potential of telerehabilitation in pediatric occupational therapy in Pakistan. It addresses the hurdles encountered when delivering therapy services amid the backdrop of the COVID-19...
详细信息
Rice classification plays a critical role in global agriculture, impacting the food security of over 3.5 billion people worldwide. As the complexity of this task has grown, advanced Machine Learning (ML) and Deep Lear...
详细信息
Rice classification plays a critical role in global agriculture, impacting the food security of over 3.5 billion people worldwide. As the complexity of this task has grown, advanced Machine Learning (ML) and Deep Learning (DL) models have emerged as powerful tools for accurate variety identification. These sophisticated algorithms excel in processing extensive datasets, leveraging key characteristics such as grain size, color, and texture to make precise predictions. However, a persistent challenge in this field is a class imbalance, where certain rice varieties are significantly underrepresented in datasets. This imbalance can severely impact model performance, particularly for minority classes, leading to biased predictions that favor more abundant varieties. Many existing models struggle to effectively address this issue, often prioritizing majority classes at the expense of overall generalization across all rice types. To tackle this critical gap in rice classification, we introduce a novel hybrid model: the XGBoost Multi-Layer 33Perceptron (XGB-MLP). This innovative approach is specifically designed to handle class imbalance, ensuring fair and accurate classification across all rice varieties, regardless of their representation in the dataset. Our model demonstrates remarkable versatility, effectively accommodating binary and multi-class scenarios while maintaining robust performance in the face of imbalanced data. As part of our contribution, we have also developed a new dataset intentionally incorporating class imbalance. This dataset serves as a rigorous benchmark for evaluating our model's performance against existing works in the field. The results of our comprehensive evaluation are compelling, with the XGB-MLP model achieving outstanding accuracy across various classification tasks: 99.86 % for binary class, 99.95 % for multi-class, and 98.46 % for a challenging multi-class scenario using a merged dataset. These impressive results not only surpass the pe
A finite set P of points in the plane is n-universal with respect to a class C of planar graphs if every n-vertex graph in C admits a crossing-free straight-line drawing with vertices at points of P. For the class of ...
详细信息
暂无评论