Rajan Aravind^1,2, Sundararajan Natarajan¹, Krishnankutty Jayakumar², Ratna Kumar Annabattula^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 997-1032, 2024, DOI:10.32604/cmes.2024.053047 - 27 September 2024

Abstract Understanding the probabilistic nature of brittle materials due to inherent dispersions in their mechanical properties is important to assess their reliability and safety for sensitive engineering applications. This is all the more important when elements composed of brittle materials are exposed to dynamic environments, resulting in catastrophic fatigue failures. The authors propose the application of a non-intrusive polynomial chaos expansion method for probabilistic studies on brittle materials undergoing fatigue fracture when geometrical parameters and material properties are random independent variables. Understanding the probabilistic nature of fatigue fracture in brittle materials is crucial for ensuring the… More >

Giada Faraco, Andrea Vincenzo De Nunzio, Nicola Ivan Giannoccaro^*, Arcangelo Messina

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 739-762, 2024, DOI:10.32604/sdhm.2024.052663 - 20 September 2024

Abstract The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring, such as that carried out by a series of accelerometers placed on the structure, is certainly a goal of extreme and current interest. In the present work, the results obtained from the processing of experimental data of a real structure are shown. The analyzed structure is a lattice structure approximately 9 m high, monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels. The data used refer to continuous monitoring that lasted for a total of 1… More >

Umar Shafique^1,*, Mohamed Mahyoub Al-Shamiri², Ali Raza³, Emad Fadhal^4,*, Muhammad Rafiq^5,6, Nauman Ahmed^5,7

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 311-329, 2024, DOI:10.32604/cmes.2024.052383 - 20 August 2024

Abstract Based on the World Health Organization (WHO), Meningitis is a severe infection of the meninges, the membranes covering the brain and spinal cord. It is a devastating disease and remains a significant public health challenge. This study investigates a bacterial meningitis model through deterministic and stochastic versions. Four-compartment population dynamics explain the concept, particularly the susceptible population, carrier, infected, and recovered. The model predicts the nonnegative equilibrium points and reproduction number, i.e., the Meningitis-Free Equilibrium (MFE), and Meningitis-Existing Equilibrium (MEE). For the stochastic version of the existing deterministic model, the two methodologies studied are transition… More >

Islam Zada^1,*, Mohammed Naif Alatawi², Syed Muhammad Saqlain¹, Abdullah Alshahrani³, Adel Alshamran⁴, Kanwal Imran⁵, Hessa Alfraihi⁶

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835 - 15 August 2024

Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >

Zhang Zhi¹, Haiyu Huang², Wei Xiong², Yijia Zhou³, Mingyu Yan^3,*, Shaolian Xia², Baofeng Jiang², Renbin Su², Xichen Tian⁴

Energy Engineering, Vol.121, No.6, pp. 1557-1576, 2024, DOI:10.32604/ee.2024.047401 - 21 May 2024

Abstract Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existing scenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios, which threatens the robustness of stochastic unit commitment and hinders its application. This paper provides a stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming and Benders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouples the primal problem into the master problem and two types of subproblems. In the master problem, the committed generator is determined, while the feasibility and… More >

Ali S. Alghamdi^1,*, Mohamed A. Zohdy², Saad Aldoihi^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1339-1370, 2024, DOI:10.32604/cmes.2024.048839 - 20 May 2024

Abstract In the contemporary era, the global expansion of electrical grids is propelled by various renewable energy sources (RESs). Efficient integration of stochastic RESs and optimal power flow (OPF) management are critical for network optimization. This study introduces an innovative solution, the Gaussian Bare-Bones Levy Cheetah Optimizer (GBBLCO), addressing OPF challenges in power generation systems with stochastic RESs. The primary objective is to minimize the total operating costs of RESs, considering four functions: overall operating costs, voltage deviation management, emissions reduction, voltage stability index (VSI) and power loss mitigation. Additionally, a carbon tax is included in… More >

Chuandong Qin^1,2, Yu Cao^1,*, Liqun Meng¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1975-1994, 2024, DOI:10.32604/cmc.2024.049228 - 15 May 2024

Abstract Brain tumors come in various types, each with distinct characteristics and treatment approaches, making manual detection a time-consuming and potentially ambiguous process. Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes. Machine learning models have become key players in automating brain tumor detection. Gradient descent methods are the mainstream algorithms for solving machine learning models. In this paper, we propose a novel distributed proximal stochastic gradient descent approach to solve the L-Smooth Support Vector Machine (SVM) classifier for brain tumor detection. Firstly, the smooth hinge loss is… More >

Hao Sun^*, Xiubo Chen, Kaiguo Yuan

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2361-2373, 2024, DOI:10.32604/cmc.2024.049159 - 15 May 2024

Abstract Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data. However, there is still a potential risk of privacy leakage, for example, attackers can obtain the original data through model inference attacks. Therefore, safeguarding the privacy of model parameters becomes crucial. One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process. However, the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes. To solve the above… More >

Hongsheng Su, Lixia Dong^*, Xiaoying Yu, Kai Liu

Energy Engineering, Vol.121, No.4, pp. 973-986, 2024, DOI:10.32604/ee.2023.043497 - 26 March 2024

Abstract Time based maintenance (TBM) and condition based maintenance (CBM) are widely applied in many large wind farms to optimize the maintenance issues of wind turbine gearboxes, however, these maintenance strategies do not take into account environmental benefits during full life cycle such as carbon emissions issues. Hence, this article proposes a carbon emissions computing model for preventive maintenance activities of wind turbine gearboxes to solve the issue. Based on the change of the gearbox state during operation and the influence of external random factors on the gearbox state, a stochastic differential equation model (SDE) and More > Graphic Abstract

Saima Rashid^1,2,*, Fahd Jarad^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2289-2327, 2024, DOI:10.32604/cmes.2023.028773 - 11 March 2024

Abstract Because of the features involved with their varied kernels, differential operators relying on convolution formulations have been acknowledged as effective mathematical resources for modeling real-world issues. In this paper, we constructed a stochastic fractional framework of measles spreading mechanisms with dual medication immunization considering the exponential decay and Mittag-Leffler kernels. In this approach, the overall population was separated into five cohorts. Furthermore, the descriptive behavior of the system was investigated, including prerequisites for the positivity of solutions, invariant domain of the solution, presence and stability of equilibrium points, and sensitivity analysis. We included a stochastic More >