Božo Damjanović^*, Pejo Konjatić, Marko Katinić

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1735-1753, 2025, DOI:10.32604/cmes.2025.072256 - 26 November 2025

Abstract Ensuring the structural integrity of piping systems is crucial in industrial operations to prevent catastrophic failures and minimize shutdown time. This study investigates a transportation-damaged pipe exposed to high-temperature conditions and cyclic loading, representing a realistic challenge in plant operation. The objective was to evaluate the service life and integrity assessment parameters of the damaged pipe, subjected to 22,000 operational cycles under two daily charge and discharge conditions. The flaw size in the damaged pipe was determined based on a failure assessment procedure, ensuring a conservative and reliable input. The damage was characterized as a… More >

Meshari D. Alanazi¹, Gehan Elsayed^2,*, Turki M. Alanazi³, Anis Sahbani⁴, Amr Yousef^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2277-2309, 2025, DOI:10.32604/cmes.2025.070726 - 26 November 2025

Abstract Traffic congestion plays a significant role in intelligent transportation systems (ITS) due to rapid urbanization and increased vehicle concentration. The congestion is dependent on multiple factors, such as limited road occupancy and vehicle density. Therefore, the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment. Conventional prediction systems face difficulties in identifying highly congested areas, which leads to reduced prediction accuracy. The problem is addressed by integrating Graph Neural Networks (GNN) with the Lion Swarm Optimization (LSO) framework to tackle the congestion prediction problem. Initially, the traffic information is… More >

Nada Alasbali^*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4943-4958, 2025, DOI:10.32604/cmc.2025.068330 - 23 October 2025

Abstract Intelligent Traffic Management (ITM) has progressively developed into a critical component of modern transportation networks, significantly enhancing traffic flow and reducing congestion in urban environments. This research proposes an enhanced framework that leverages Deep Q-Learning (DQL), Game Theory (GT), and Stochastic Optimization (SO) to tackle the complex dynamics in transportation networks. The DQL component utilizes the distribution of traffic conditions for epsilon-greedy policy formulation and action and choice reward calculation, ensuring resilient decision-making. GT models the interaction between vehicles and intersections through probabilistic distributions of various features to enhance performance. Results demonstrate that the proposed More >

Mingxuan Lei¹, Jiayin Xu², Xiaoying Hu², Lin Feng³, Baoping Luo^4,5,6,*

BIOCELL, Vol.49, No.10, pp. 1947-1965, 2025, DOI:10.32604/biocell.2025.068926 - 22 October 2025

Abstract Background: Hepatocellular carcinoma (HCC) typically begins inconspicuously and progresses swiftly, leading to most patients being diagnosed at an advanced stage. Accordingly, a pressing priority is to clarify the development mechanisms of HCC and devise efficient intervention and treatment protocols. Methods: An upstream miRNA of solute carrier transporter family 1 member 5 (SLC1A5) was predicted to be miR-122-5p by various databases, and a dual-luciferase reporter gene assay was used to verify the SLC1A5- and miR-122-5p-targeting relationship. SLC1A5 and miR-122-5p expression in HCC cells was quantitatively assessed using quantitative reverse transcription polymerase chain reaction (qRT–PCR). Western blotting… More >

Sagvan Y. Musa^1,2, Zanyar A. Ameen^3,*, Wafa Alagal⁴, Baravan A. Asaad^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3493-3529, 2025, DOI:10.32604/cmes.2025.070019 - 30 September 2025

Abstract Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility, cost-effectiveness, and environmental impact, often under uncertainty and incomplete information. These complex decisions require input from various stakeholders, including planners, policymakers, engineers, and community representatives, whose opinions may differ or contradict. Traditional decision-making approaches struggle to effectively handle such bipolar and multivalued expert evaluations. To address these challenges, we propose a novel decision-making framework based on Pythagorean fuzzy N-bipolar soft expert sets. This model allows experts to express both positive and negative opinions on a multinary scale, capturing nuanced judgments with higher accuracy. It… More >

Ziming Hu¹, Jingfa Li^1,*, Chaoyang Fan¹, Jiale Xiao¹, Huijie Huang², Bo Yu¹, Baocheng Shi¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.010823

Abstract Hydrogen production from offshore wind power is an effective means to address the challenges of wind power grid integration and has emerged as a focal point in the development and research of offshore wind energy in recent years. However, the current state of hydrogen storage and transportation technologies for offshore applications lacks comprehensive economic analysis. This study aims to provide a thorough economic evaluation of these technologies by considering both fixed investment costs and operational and maintenance costs. A levelized cost model is employed to analyze four offshore hydrogen storage and transportation schemes: gas hydrogen… More >

Nezir Aydin^1,2,*, Melike Cari³, Betul Kara³, Ertugrul Ayyildiz^1,3

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2625-2650, 2025, DOI:10.32604/cmc.2025.067290 - 23 September 2025

Abstract Transportation systems are rapidly transforming in response to urbanization, sustainability challenges, and advances in digital technologies. This review synthesizes the intersection of artificial intelligence (AI), fuzzy logic, and multi-criteria decision-making (MCDM) in transportation research. A comprehensive literature search was conducted in the Scopus database, utilizing carefully selected AI, fuzzy, and MCDM keywords. Studies were rigorously screened according to explicit inclusion and exclusion criteria, resulting in 73 eligible publications spanning 2006–2025. The review protocol included transparent data extraction on methodological approaches, application domains, and geographic distribution. Key findings highlight the prevalence of hybrid fuzzy AHP and… More >

Asmat Ullah¹, Yar Muhammad^1,*, Bakht Zada¹, Korhan Cengiz², Nikola Ivković^3,*, Mario Konecki³, Abid Yahya⁴

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3767-3786, 2025, DOI:10.32604/cmc.2025.067145 - 23 September 2025

Abstract Low visibility conditions, particularly those caused by fog, significantly affect road safety and reduce drivers’ ability to see ahead clearly. The conventional approaches used to address this problem primarily rely on instrument-based and fixed-threshold-based theoretical frameworks, which face challenges in adaptability and demonstrate lower performance under varying environmental conditions. To overcome these challenges, we propose a real-time visibility estimation model that leverages roadside CCTV cameras to monitor and identify visibility levels under different weather conditions. The proposed method begins by identifying specific regions of interest (ROI) in the CCTV images and focuses on extracting specific… More >

Manuel J. C. S. Reis^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3023-3039, 2025, DOI:10.32604/cmc.2025.066390 - 23 September 2025

Abstract The Vehicle Routing Problem with Time Windows (VRPTW) presents a significant challenge in combinatorial optimization, especially under real-world uncertainties such as variable travel times, service durations, and dynamic customer demands. These uncertainties make traditional deterministic models inadequate, often leading to suboptimal or infeasible solutions. To address these challenges, this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms (GA) with Local Search (LS), while incorporating stochastic uncertainty modeling through probabilistic travel times. The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance. This adaptivity enhances the algorithm’s… More >

Mohammad Nowsin Amin Sheikh^1,2,*, Muhammad Saibtain Raza¹, I-Shyan Hwang^1,*, Md. Alamgir Hossain³, Ihsan Ullah¹, Tahmid Hasan⁴, Mohammad Syuhaimi Ab-Rahman⁵

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4043-4066, 2025, DOI:10.32604/cmc.2025.065850 - 23 September 2025

Abstract The rapid advancement of the Internet of Things (IoT) has heightened the importance of security, with a notable increase in Distributed Denial-of-Service (DDoS) attacks targeting IoT devices. Network security specialists face the challenge of producing systems to identify and offset these attacks. This research manages IoT security through the emerging Software-Defined Networking (SDN) standard by developing a unified framework (RNN-RYU). We thoroughly assess multiple deep learning frameworks, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Feed-Forward Convolutional Neural Network (FFCNN), and Recurrent Neural Network (RNN), and present the novel usage of Synthetic Minority Over-Sampling More >