Research Article
Control and Optimization
Maryam Najimi; Akbar Hashemi Borzabadi
Abstract
This paper addresses the challenges of power control, radar assignment, and signal timing to improve the detection and tracking of multiple targets within a mono-static cognitive radar network. A fusion center is utilized to integrate target velocity ...
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This paper addresses the challenges of power control, radar assignment, and signal timing to improve the detection and tracking of multiple targets within a mono-static cognitive radar network. A fusion center is utilized to integrate target velocity data gathered by radars. The primary objective is to minimize the mean square error in target velocity estimation while adhering to constraints related to global detection probability and total radar power consumption for effective target detection and tracking. The optimization problem is formulated and a low-complexity method is proposed using the genetic algorithm (GA). In this approach, the radars and their transmission powers are represented as chromosomes and the network's quality of service (QoS) requirements serve as inputs to the GA. The output of the GA is the mean error square of the target velocity estimation. Once the problem is resolved, the power allocation for each radar assigned to a specific target is determined. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing detection performance and improving tracking accuracy when compared to other benchmark algorithms.
Research Article
Control and Optimization
Alireza Fakharzadeh Jahromi; Mahin Azizi Karachi; Hajar Alimorad
Abstract
Cancer is a class of diseases characterized by uncontrolled cell growth that affects immune cells. There are several treatment options available, including surgery, chemotherapy, hormonal therapy, radiation therapy, targeted therapy, and ...
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Cancer is a class of diseases characterized by uncontrolled cell growth that affects immune cells. There are several treatment options available, including surgery, chemotherapy, hormonal therapy, radiation therapy, targeted therapy, and palliative care. Among these, chemotherapy is one of the most widely used and recognized methods. This paper presents a novel model designed to control cancer cell growth based on a system of nonlinear fractional differential equations with delay in chemotherapy. The model focuses on the competition between tumor and immune cells to minimize the number of tumor cells and determine the optimal dosage of the administered drug. It can simulate various scenarios and predict the outcomes of different chemotherapy regimens. By employing discretization and the Grunwald-Letnikov method, we aim to gain insights into why some patients respond well to chemotherapy while others do not. The results may also help identify potential drug targets and optimize existing treatments.
Research Article
Control and Optimization
Soghra Mikaeyl Nejad
Abstract
Gene expression signatures reflect the response of cell tissues to diseases, genetic disorders, and drug treatments, containing hidden patterns that can provide valuable insights for biological research and cancer diagnostics. This studyproposes a hybrid ...
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Gene expression signatures reflect the response of cell tissues to diseases, genetic disorders, and drug treatments, containing hidden patterns that can provide valuable insights for biological research and cancer diagnostics. This studyproposes a hybrid deep learning approach combining convolutional neural networks (CNNs) and support vector machines (SVMs) to classify cancer types using unstructured gene expression data. We applied three hybrid CNN-SVM models to a dataset of 10,340 samples spanning 33 cancer types from the Cancer Genome Atlas. The CNN component extracted latent features from the gene expression data, while the SVM replaced the softmax layer to enhance classification robustness. Among the proposed models, the Hybrid-CNN-SVM model achieved superior performance, demonstrating excellent prediction accuracy and outperforming other models. This study highlights the potential of hybrid deep learning frameworks for cancer type prediction and underscores their applicability to high-dimensional genomic datasets.
Research Article
Control and Optimization
Narjes Amiri; Hadi Nasseri; Davood Darvishi
Abstract
This paper explores a specific category of optimization management models tailored for wireless communication systems. To enhance the efficiency of managing these systems, we introduce a fuzzy relation multi-objective programming approach. We define the concept of a feasible ...
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This paper explores a specific category of optimization management models tailored for wireless communication systems. To enhance the efficiency of managing these systems, we introduce a fuzzy relation multi-objective programming approach. We define the concept of a feasible index set and present a novel algorithm, termed the feasible index set algorithm, which is designed to determine the optimal lexicographic solution to the problem, demonstrating polynomial computational complexity. Previous studies have indicated that the emission base stations within wireless communication systems can be effectively modeled using a series of fuzzy relation inequalities through max-product composition. This topic is also addressed in our paper. Wireless communication is widely employed across various sectors, encompassing mobile communication and data transmission. In this framework, information is transmitted via electromagnetic waves generated by fixed emission base stations.
Research Article
Control and Optimization
Hasan Barzegar; Mohsen Sayadi; Saeid Alikhani; Nima Ghanbari
Abstract
An irregularity measure (IM) of a connected graph $G$ is defined as a non-negative graph invariant that satisfies the condition $IM(G) = 0$ if and only if $G$ is a regular graph. Among the prominent degree-based irregularity measures are Bell's degree variance, denoted as ...
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An irregularity measure (IM) of a connected graph $G$ is defined as a non-negative graph invariant that satisfies the condition $IM(G) = 0$ if and only if $G$ is a regular graph. Among the prominent degree-based irregularity measures are Bell's degree variance, denoted as $Var_B(G)$, and degree deviation, represented as $S(G)$. Specifically, they are defined by the equations $Var_B(G) = \frac{1}{n} \sum_{i=1}^{n} \left( d_i - \frac{2m}{n} \right)^2$ and $S(G)=\sum_{i=1}^n \left|d_i- \frac{2m}{n}\right |$, where $m$ is the number of edges and $n$ is the number of vertices in $G$. This paper studies the properties of Bell's degree-variance and degree deviation for acyclic, unicyclic, and cactus graphs. Our analysis shows how these measures relate to graph topology and structure, influencing the overall irregularity. Additionally, we identify and analyze optimal graphs that minimize both irregularity measures, providing insights into their implications for network design, data structure optimization, and real-world applications. This study contributes to the understanding of graph irregularity and offers a framework for future research into irregularity measures across different classes of graphs.
Research Article
Control and Optimization
Narjes Sabeghi
Abstract
A critical aspect of successful project management is ensuring that execution aligns with the baseline schedule. However, traditional project control methods often struggle to effectively address the uncertainties and deviations that can arise during project execution, leading ...
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A critical aspect of successful project management is ensuring that execution aligns with the baseline schedule. However, traditional project control methods often struggle to effectively address the uncertainties and deviations that can arise during project execution, leading to delays and inefficiencies. To tackle these challenges, this paper introduces a novel heuristic approach based on the Tabu Search (TS) algorithm for identifying discrete control points throughout the project life cycle. These control points enable proactive monitoring, timely deviation detection, and corrective actions, significantly minimizing project delays. Unlike traditional scheduling techniques, which can be rigid and reactive, our proposed method dynamically adjusts control points to enhance project oversight. Experimental results on benchmark instances from the Kolisch library demonstrate that our approach significantly reduces project delays, with up to 20% improvements compared to initial schedules in certain scenarios. These findings underscore the effectiveness of the TS algorithm in enhancing project control strategies, highlighting its potential applicability in real-world project management scenarios.
Research Article
Control and Optimization
Rasoul Hatamian; Seyed Amjad Samareh Hashemi
Abstract
This paper presents an iterative computational method for addressing constrained nonlinear optimal control problems, specifically those involving terminal state, state saturation, and control saturation constraints. The proposed approach reformulates the original ...
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This paper presents an iterative computational method for addressing constrained nonlinear optimal control problems, specifically those involving terminal state, state saturation, and control saturation constraints. The proposed approach reformulates the original problem into a sequence of constrained linear time-varying quadratic optimal control problems. This is achieved by iteratively approximating the nonlinear dynamic system using constrained linear time-varying models. Each reformulated problem is then converted into a standard quadratic programming problem by applying Chelyshkov polynomials in conjunction with a collocation method. Finally, the resulting problems are solved to obtain optimal control solutions
Research Article
Control and Optimization
Gassan A.M.O. Farah; Abdulaziz Mukhtar; Kailash C. Patidar
Abstract
Malaria continues to represent a significant public health concern in Sudan, with cases rising over 40% from 2015 to 2020. This research investigates how climate change affects malaria transmission patterns using a mathematical model in an ordinary differential equation framework. ...
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Malaria continues to represent a significant public health concern in Sudan, with cases rising over 40% from 2015 to 2020. This research investigates how climate change affects malaria transmission patterns using a mathematical model in an ordinary differential equation framework. The analysis involves calculating the basic reproduction number and evaluating the system's qualitative properties to gain insights into disease dynamics. Additionally, a sensitivity analysis is conducted to evaluate how climatic conditions, e.g., rainfall and temperature, influence key model parameters. Statistical approaches are utilized to estimate parameters and calibrate the model using empirical data from Sudan, ensuring consistency between the model and observed trends. Numerical simulations demonstrate the growing influence of climate variability on the spatial distribution of malaria vectors and the transmission progression over time. The study establishes a strong association between climatic changes and the exacerbation of malaria prevalence in Sudan. These findings emphasize the urgent need for climate-adaptive strategies, including improved vector control, strengthened surveillance systems, and climate-resilient public health interventions, to address the increased risks posed by changing environmental conditions. The research provides valuable insights to inform evidence-based policies aimed at reducing malaria transmission in Sudan and other regions that are experiencing similar challenges due to climate change.
Research Article
Control and Optimization
Amal Kumar Adak; Nil Kamal
Abstract
The incorporation of Pythagorean fuzzy sets into credit risk assessment represents a relatively innovative approach for predicting loan defaults, offering a more precise and adaptable tool for financial institutions. Key customer information—such as credit history, credit ...
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The incorporation of Pythagorean fuzzy sets into credit risk assessment represents a relatively innovative approach for predicting loan defaults, offering a more precise and adaptable tool for financial institutions. Key customer information—such as credit history, credit mix, credit utilization, duration of credit history, income level, and employment stability—is obtained as linguistic variables. These linguistic assessments are then transformed into Pythagorean fuzzy numbers. The combined Pythagorean fuzzy information is subsequently processed using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This approach employs a modified accuracy function to determine the Pythagorean fuzzy positive ideal solution and the Pythagorean fuzzy negative ideal solution. For distance calculations within the TOPSIS framework, spherical distance measurements are utilized. Alternatives are ranked based on the relative closeness coefficient and an adjusted index, collectively facilitating decision-making. The practical applicability of the proposed model is demonstrated through an illustrative numerical example.
Research Article
Control and Optimization
Afrah Kadhim Saud Al-tameemi; Mahmoud Mahmoudi; Majid Darehmiraki
Abstract
This study introduces an innovative approach for addressing optimal control problems related to parabolic partial differential equations (PDEs) through the application of rational radial basis functions (RBFs). Parabolic PDEs, which are instrumental in modeling time-dependent processes such as heat transfer ...
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This study introduces an innovative approach for addressing optimal control problems related to parabolic partial differential equations (PDEs) through the application of rational radial basis functions (RBFs). Parabolic PDEs, which are instrumental in modeling time-dependent processes such as heat transfer and diffusion, pose significant computational challenges in optimal control due to the requirement for precise approximations of both state and adjoint equations. The proposed approach exploits the adaptability and spectral accuracy of rational RBFs within a meshless framework, effectively addressing the limitations of traditional discretization methods. By enhancing the accuracy and efficiency of control strategies, this method significantly contributes to advancing the theory and application of optimal control in dynamic systems. The tunable shape parameters of rational RBFs allow for accurate representation of solution characteristics, including steep gradients and localized behaviors. Additionally, their meshless framework adeptly accommodates complex geometries and boundary conditions, ensuring computational efficiency through the generation of sparse and well-conditioned system matrices. This paper also introduces a novel hybrid rational RBF, termed the Gaussian rational hybrid RBF. The efficacy of the proposed approach is validated through a series of benchmark tests and practical applications, highlighting its ability to achieve high accuracy with reduced computational effort. The findings illustrate the potential of rational RBFs as a robust and versatile tool for solving optimal control problems governed by parabolic PDEs, paving the way for further exploration of advanced rational RBF-based techniques in the field of computational optimal control.
