Hongrong Wang^1,2, Xinjian Li^3,4, Xingjing She¹, Wenjian Ma^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2039-2055, 2025, DOI:10.32604/cmes.2025.072572 - 26 November 2025

Abstract In modern complex systems, real-time regression prediction plays a vital role in performance evaluation and risk warning. Nevertheless, existing methods still face challenges in maintaining stability and predictive accuracy under complex conditions. To address these limitations, this study proposes an online prediction approach that integrates event tracking sensitivity analysis with machine learning. Specifically, a real-time event tracking sensitivity analysis method is employed to capture and quantify the impact of key events on system outputs. On this basis, a mutual-information–based self-extraction mechanism is introduced to construct prior weights, which are then incorporated into a LightGBM prediction More >

Azhar Iqbal Kashif Butt^1,*, Tariq Ismaeel^2,*, Sara Khan², Muhammad Imran³, Waheed Ahmad², Ismail Abdulrashid⁴, Muhammad Sajid Riaz⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3463-3492, 2025, DOI:10.32604/cmes.2025.069277 - 30 September 2025

Abstract Malaria is a significant global health challenge. This devastating disease continues to affect millions, especially in tropical regions. It is caused by Plasmodium parasites transmitted by female Anopheles mosquitoes. This study introduces a nonlinear mathematical model for examining the transmission dynamics of malaria, incorporating both human and mosquito populations. We aim to identify the key factors driving the endemic spread of malaria, determine feasible solutions, and provide insights that lead to the development of effective prevention and management strategies. We derive the basic reproductive number employing the next-generation matrix approach and identify the disease-free and… More >

Sen Yang¹, Jie Zhong¹, Boyu Gan¹, Yi Sun¹, Changming Bu¹, Mingtao Zhang¹, Jiehong Li^1,*, Yang Yu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2943-2967, 2025, DOI:10.32604/cmes.2025.067282 - 30 September 2025

Abstract Foam concrete is widely used in engineering due to its lightweight and high porosity. Its compressive strength, a key performance indicator, is influenced by multiple factors, showing nonlinear variation. As compressive strength tests for foam concrete take a long time, a fast and accurate prediction method is needed. In recent years, machine learning has become a powerful tool for predicting the compressive strength of cement-based materials. However, existing studies often use a limited number of input parameters, and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.… 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 >

Jianchang Hou¹, Zhanpeng Jiang¹, Fenghe Wu¹, Hui Lian¹, Zhaohua Wang², Zijian Liu³, Weicheng Li^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1545-1572, 2025, DOI:10.32604/cmes.2025.068482 - 31 August 2025

Abstract In this paper, a topology optimization method for coordinated stiffness and strength design is proposed under mass constraints, utilizing the Solid Isotropic Material with Penalization approach. Element densities are regulated through sensitivity filtering to mitigate numerical instabilities associated with stress concentrations. A p-norm aggregation function is employed to globalize local stress constraints, and a normalization technique linearly weights strain energy and stress, transforming the multi-objective problem into a single-objective formulation. The sensitivity of the objective function with respect to design variables is rigorously derived. Three numerical examples are presented, comparing the optimized structures in terms More >

Yizhe Han^1,2, Guangjing Huang¹, Fei Xiao¹, Zhiyin Huang^3,*, Yuting Dai¹

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.7, pp. 1657-1671, 2025, DOI:10.32604/fdmp.2025.067528 - 31 July 2025

Abstract During high-speed forward flight, helicopter rotor blades operate across a wide range of Reynolds and Mach numbers. Under such conditions, their aerodynamic performance is significantly influenced by dynamic stall—a complex, unsteady flow phenomenon highly sensitive to inlet conditions such as Mach and Reynolds numbers. The key features of three-dimensional blade stall can be effectively represented by the dynamic stall behavior of a pitching airfoil. In this study, we conduct an uncertainty quantification analysis of dynamic stall aerodynamics in high-Mach-number flows over pitching airfoils, accounting for uncertainties in inlet parameters. A computational fluid dynamics (CFD) model… More >

Fan Li¹, Hongxue Liu², Yongsong Li², Leilei Chen², Haojie Lian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2785-2809, 2025, DOI:10.32604/cmes.2025.066001 - 30 June 2025

Abstract This study explores a sensitivity analysis method based on the boundary element method (BEM) to address the computational complexity in acoustic analysis with ground reflection problems. The advantages of BEM in acoustic simulations and its high computational cost in broadband problems are examined. To improve efficiency, a Taylor series expansion is applied to decouple frequency-dependent terms in BEM. Additionally, the Second-Order Arnoldi (SOAR) model order reduction method is integrated to reduce computational costs and enhance numerical stability. Furthermore, an isogeometric sensitivity boundary integral equation is formulated using the direct differentiation method, incorporating Cauchy principal value More >

Ahed Habib^1,*, Mohamed Maalej², Samir Dirar³, M. Talha Junaid², Salah Altoubat²

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 789-817, 2025, DOI:10.32604/sdhm.2025.064882 - 30 June 2025

Abstract Carbon nanotube-reinforced cement composites have gained significant attention due to their enhanced mechanical properties, particularly in compressive and flexural strength. Despite extensive research, the influence of various parameters on these properties remains inadequately understood, primarily due to the complex interactions within the composites. This study addresses this gap by employing machine learning techniques to conduct a sensitivity analysis on the compressive and flexural strength of carbon nanotube-reinforced cement composites. It systematically evaluates nine data-preprocessing techniques and benchmarks eleven machine-learning algorithms to reveal trade-offs between predictive accuracy and computational complexity, which has not previously been explored… More >

Ala’a R. Al-Shamasneh¹, Faten Khalid Karim², Arsalan Mahmoodzadeh^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 281-303, 2025, DOI:10.32604/cmc.2025.065748 - 09 June 2025

Abstract Soilcrete is a composite material of soil and cement that is highly valued in the construction industry. Accurate measurement of its mechanical properties is essential, but laboratory testing methods are expensive, time-consuming, and include inaccuracies. Machine learning (ML) algorithms provide a more efficient alternative for this purpose, so after assessment with a statistical extraction method, ML algorithms including back-propagation neural network (BPNN), K-nearest neighbor (KNN), radial basis function (RBF), feed-forward neural networks (FFNN), and support vector regression (SVR) for predicting the uniaxial compressive strength (UCS) of soilcrete, were proposed in this study. The developed models… More >

Hadi Fattahi¹, Hossein Ghaedi¹, Danial Jahed Armaghani^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 747-766, 2025, DOI:10.32604/cmes.2025.062390 - 11 April 2025

Abstract In contemporary geotechnical projects, various approaches are employed for forecasting the settlement of shallow foundations (S_m). However, achieving precise modeling of foundation behavior using certain techniques (such as analytical, numerical, and regression) is challenging and sometimes unattainable. This is primarily due to the inherent nonlinearity of the model, the intricate nature of geotechnical materials, the complex interaction between soil and foundation, and the inherent uncertainty in soil parameters. Therefore, these methods often introduce assumptions and simplifications, resulting in relationships that deviate from the actual problem’s reality. In addition, many of these methods demand significant investments of… More >