Lina Dong, Yeming Dai^*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2913-2956, 2024, DOI:10.32604/cmes.2024.049214

Abstract This study delves into the formation dynamics of alliances within a closed-loop supply chain (CLSC) that encompasses a manufacturer, a retailer, and an e-commerce platform. It leverages Stackelberg game for this exploration, contrasting the equilibrium outcomes of a non-alliance model with those of three differentiated alliance models. The non-alliance model acts as a crucial benchmark, enabling the evaluation of the motivations for various supply chain entities to engage in alliance formations. Our analysis is centered on identifying the most effective alliance strategies and establishing a coordination within these partnerships. We thoroughly investigate the consequences of… More >

Naif Alotaibi¹, A. S. Al-Moisheer², Ibrahim Elbatal¹, Mohammed Elgarhy^3,4, Ehab M. Almetwally^1,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2795-2823, 2024, DOI:10.32604/cmes.2024.049188

Abstract This article introduces a novel variant of the generalized linear exponential (GLE) distribution, known as the sine generalized linear exponential (SGLE) distribution. The SGLE distribution utilizes the sine transformation to enhance its capabilities. The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues. The suggested model incorporates a hazard rate function (HRF) that may display a rising, J-shaped, or bathtub form, depending on its unique characteristics. This model includes many well-known lifespan distributions as separate sub-models. The suggested model is accompanied with a range of More >

Deepak Upreti¹, Eunmok Yang², Hyunil Kim^3,*, Changho Seo^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2239-2274, 2024, DOI:10.32604/cmes.2024.048932

Abstract Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise More >

Weijian Song^1,, Xi Li^1,, Peng Chen^1,*, Juan Chen¹, Jianhua Ren², Yunni Xia^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3001-3016, 2024, DOI:10.32604/cmes.2024.048563

Abstract With the rapid development of Internet of Things (IoT) technology, IoT systems have been widely applied in healthcare, transportation, home, and other fields. However, with the continuous expansion of the scale and increasing complexity of IoT systems, the stability and security issues of IoT systems have become increasingly prominent. Thus, it is crucial to detect anomalies in the collected IoT time series from various sensors. Recently, deep learning models have been leveraged for IoT anomaly detection. However, owing to the challenges associated with data labeling, most IoT anomaly detection methods resort to unsupervised learning techniques.… More >

Mojtaba Yari^1,*, Manoj Khandelwal², Payam Abbasi³, Evangelos I. Koutras⁴, Danial Jahed Armaghani^5,*, Panagiotis G. Asteris⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2207-2238, 2024, DOI:10.32604/cmes.2024.048071

Abstract Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to More >

Heng Zhang^1,2, Chao Su^2,*, Xiaohu Chen¹, Zhizhong Song¹, Weijie Zhan³

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2977-3000, 2024, DOI:10.32604/cmes.2024.047972

Abstract Temperature-induced cracking during the construction of mass concrete is a significant concern. Numerical simulations of concrete temperature have primarily assumed that the concrete is placed in an open environment. The problem of heat transfer between the air and concrete has been simplified to the concrete’s heat dissipation boundary. However, in the case of tubular concrete structures, where air inlet and outlet are relatively limited, the internal air temperature does not dissipate promptly to the external environment as it rises. To accurately simulate the temperature and creep stress in tubular concrete structures with enclosed air spaces… More >

Javaid Ali¹, Armando Ciancio², Kashif Ali Khan³, Nauman Raza^4,5, Haci Mehmet Baskonus^6,*, Muhammad Luqman¹, Zafar-Ullah Khan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2275-2296, 2024, DOI:10.32604/cmes.2024.046923

Abstract This study proposes a structure-preserving evolutionary framework to find a semi-analytical approximate solution for a nonlinear cervical cancer epidemic (CCE) model. The underlying CCE model lacks a closed-form exact solution. Numerical solutions obtained through traditional finite difference schemes do not ensure the preservation of the model’s necessary properties, such as positivity, boundedness, and feasibility. Therefore, the development of structure-preserving semi-analytical approaches is always necessary. This research introduces an intelligently supervised computational paradigm to solve the underlying CCE model’s physical properties by formulating an equivalent unconstrained optimization problem. Singularity-free safe Padé rational functions approximate the mathematical More >

Ziqiao Zhou¹, Tianyang Zhou^1,*, Jinghao Xu², Junhu Zhu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2613-2634, 2024, DOI:10.32604/cmes.2023.028553

Abstract Intelligent penetration testing is of great significance for the improvement of the security of information systems, and the critical issue is the planning of penetration test paths. In view of the difficulty for attackers to obtain complete network information in realistic network scenarios, Reinforcement Learning (RL) is a promising solution to discover the optimal penetration path under incomplete information about the target network. Existing RL-based methods are challenged by the sizeable discrete action space, which leads to difficulties in the convergence. Moreover, most methods still rely on experts’ knowledge. To address these issues, this paper… More >

Qusay M. Salih^1,2,*, Md. Arafatur Rahman³, Ahmad Firdaus¹, Mohammed Rajih Jassim⁴, Hasan Kahtan⁵, Jasni Mohamad Zain⁶, Ahmed Hussein Ali⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2165-2206, 2024, DOI:10.32604/cmes.2024.028132

Abstract The race to develop the next generation of wireless networks, known as Sixth Generation (6G) wireless, which will be operational in 2030, has already begun. To realize its full potential over the next decade, 6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ones. However, the studies about 6G are mainly limited and scattered, whereas no bibliometric study covers the 6G field. Thus, this study aims to review, examine, and summarize existing studies and research activities in 6G. This study has examined the Scopus database through a bibliometric analysis of more than More > Graphic Abstract

Samar M. Alqhtani¹, Toufique A. Soomro^2,*, Faisal Bin Ubaid³, Ahmed Ali⁴, Muhammad Irfan⁵, Abdullah A. Asiri⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1539-1562, 2024, DOI:10.32604/cmes.2024.051475

Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for… More > Graphic Abstract