Lijuan Huang¹, Xianyi Liu², Jinping Liu^2,*, Pengfei Xu^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.068818 - 10 November 2025

Abstract The ubiquity of mobile devices has driven advancements in mobile object detection. However, challenges in multi-scale object detection in open, complex environments persist due to limited computational resources. Traditional approaches like network compression, quantization, and lightweight design often sacrifice accuracy or feature representation robustness. This article introduces the Fast Multi-scale Channel Shuffling Network (FMCSNet), a novel lightweight detection model optimized for mobile devices. FMCSNet integrates a fully convolutional Multilayer Perceptron (MLP) module, offering global perception without significantly increasing parameters, effectively bridging the gap between CNNs and Vision Transformers. FMCSNet achieves a delicate balance between computation… More >

Zhenzhen Shen^1,2, Kang Yang², Dengfeng Wei², Quansheng Liang², Zhenpeng Ma², Hong Wang², Keyu Wang², Chunwei Zhang², Xiaohu Yang^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.11, pp. 2741-2760, 2025, DOI:10.32604/fdmp.2025.070395 - 01 December 2025

Abstract The rapid prediction of seepage mass flow in soil is essential for understanding fluid transport in porous media. This study proposes a new method for fast prediction of soil seepage mass flow by combining mesoscopic modeling with deep learning. Porous media structures were generated using the Quartet Structure Generation Set (QSGS) method, and a mesoscopic-scale seepage calculation model was applied to compute flow rates. These results were then used to train a deep learning model for rapid prediction. The analysis shows that larger average pore diameters lead to higher internal flow velocities and mass flow More >

Yang Lei^1,*, Bo Jiang¹, Yucai Zhao², Gaofeng Fu³, Falin Qi¹, Tian Tian¹, Qiankuan Feng¹, Qiming Qu¹

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1657-1679, 2025, DOI:10.32604/sdhm.2025.069415 - 17 November 2025

Abstract Detecting internal defects, particularly voids behind linings, is critical for ensuring the structural integrity of aging high-speed rail (HSR) tunnel networks. While ground-penetrating radar (GPR) is widely employed, systematic quantification of performance boundaries for air-coupled (A-CGPR) and ground-coupled (G-CGPR) systems within the complex electromagnetic environment of multilayer reinforced HSR tunnels remains limited. This study establishes physics-based quantitative performance limits for A-CGPR and G-CGPR through rigorously validated GPRMax finite-difference time-domain (FDTD) simulations and comprehensive field validation over a 300 m operational HSR tunnel section. Key performance metrics were quantified as functions of: (a) detection distance (A-CGPR:… More >

Jinghua Yang¹, Bin Gong^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-2, 2025, DOI: 10.32604/icces.2025.011889

Abstract Geological stratification interpretation divides geological strata based on acquired well-logging data, providing comparative analysis results for strata and structures. This process serves as a fundamental framework for subsequent drilling and development design plans, making it a crucial step in oil exploration and development process. Traditional geological stratification interpretation methods are based primarily on geological, logging, and experimental data, with manual determination of strata boundaries to obtain interpretation results. However, manual interpretation is characterized by strong subjectivity and reliance on experience, which may compromise the quality and consistency of the results. To eliminate the dependency on… More >

Salvatore Brischetto^*, Domenico Cesare, Tommaso Mondino

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4491-4518, 2025, DOI:10.32604/cmc.2025.068518 - 23 October 2025

Abstract A three-dimensional (3D) analytical formulation is proposed to put together magnetic, electric and elastic fields to analyze the vibration modes of simply-supported layered piezo-electro-magnetic plates. The present 3D model allows analyses for layered smart plates in both open-circuit and closed-circuit configurations. The second-order differential equations written in the mixed curvilinear reference system govern the magneto-electro-elastic free vibration problem for multilayered plates. This set consists of the 3D equations of motion and the 3D divergence equations for the magnetic induction and electric displacement. Navier harmonic forms in the planar directions and the exponential matrix method in… More >

Zhiwei Ye^1,2,3, Dingfeng Song¹, Haitao Xie^1,2,3,*, Jixin Zhang^1,2, Wen Zhou^1,2, Mengya Lei^1,2, Xiao Zheng^1,2, Jie Sun¹, Jing Zhou¹, Mengxuan Li¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5509-5530, 2025, DOI:10.32604/cmc.2025.067342 - 23 October 2025

Abstract The Multilayer Perceptron (MLP) is a fundamental neural network model widely applied in various domains, particularly for lightweight image classification, speech recognition, and natural language processing tasks. Despite its widespread success, training MLPs often encounter significant challenges, including susceptibility to local optima, slow convergence rates, and high sensitivity to initial weight configurations. To address these issues, this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer (LOEV-APO), which enhances both global exploration and local exploitation simultaneously. LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling (LHS) with Opposition-Based Learning (OBL), thus… More >

Salvatore Brischetto^*, Domenico Cesare, Tommaso Mondino

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 643-668, 2025, DOI:10.32604/cmes.2025.066313 - 31 July 2025

Abstract This study proposes a three-dimensional (3D) coupled magneto-electro-elastic problem for the static analysis of multilayered plates embedding piezomagnetic and piezoelectric layers by considering both sensor and actuator configurations. The 3D governing equations for the magneto-electro-elastic static behavior of plates are explicitly show that are made by the three 3D equilibrium equations, the 3D divergence equation for magnetic induction, and the 3D divergence equation for the electric displacement. The proposed solution involves the exponential matrix in the thickness direction and primary variables’ harmonic forms in the in-plane ones. A closed-form solution is performed considering simply-supported boundary… More >

Ahmed A. Ewees^1,2,*, Mohammed A. A. Al-Qaness³, Ali Alshahrani¹, Mohamed Abd Elaziz⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2287-2303, 2025, DOI:10.32604/cmc.2025.061320 - 16 April 2025

Abstract Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output. This enhances the efficiency and reliability of renewable energy systems. Forecasting approaches inform energy management strategies, reduce reliance on fossil fuels, and support the broader transition to sustainable energy solutions. The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis. This research advances an optimized Multilayer Perceptron (MLP) model using recently proposed metaheuristic optimization algorithms, namely the Fire Hawk Optimizer (FHO)… More >

Yongfeng Tai¹, Xingyu Yan², Xiangyi Geng³, Lin Mu⁴, Mingshun Jiang², Faye Zhang^2,*

Structural Durability & Health Monitoring, Vol.19, No.2, pp. 365-383, 2025, DOI:10.32604/sdhm.2024.053998 - 15 January 2025

Abstract The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee. In engineering scenarios, only a small amount of bearing performance degradation data can be obtained through accelerated life testing. In the absence of lifetime data, the hidden long-term correlation between performance degradation data is challenging to mine effectively, which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method. To address this problem, a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed. Firstly,… More >

Jun Wang^1,2, Mingjie Wang^1,2, Zijie Li^1,2, Ken Chen^1,2, Jiatian Mei^1,2, Shu Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 485-498, 2025, DOI:10.32604/cmc.2024.057224 - 03 January 2025

Abstract In the field of intelligent education, the integration of artificial intelligence, especially deep learning technologies, has garnered significant attention. Knowledge tracing (KT) plays a pivotal role in this field by predicting students’ future performance through the analysis of historical interaction data, thereby assisting educators in evaluating knowledge mastery and tailoring instructional strategies. Traditional knowledge tracing methods, largely based on Recurrent Neural Networks (RNNs) and Transformer models, primarily focus on capturing long-term interaction patterns in sequential data. However, these models may neglect crucial short-term dynamics and other relevant features. This paper introduces a novel approach to… More >