Xiang Gao, Zhao Zhang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.011092

Abstract Microstructures is the key factor determining the properties of the additively manufactured components [1]. It can be highly affected by the temperatures generated during the additive manufacturing process. Phase field method, as established based on Ginzburg-Landau theory is an efficient tool to simulate the microstructural evolutions in additive manufacturing [2]. It can be used to simulate solidification, diffusion, phase transformation and grain growth [3]. Here we compared the new progress on the phase field method in the field of additive manufacturing. Due to the differences between the temperature field and the grain field, how to… More >

Weizheng Lu, Shuo Wang, Yang Chen, Jia Li^*, Qihong Fang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.010711

Abstract The multi-principal elemental alloys (MPEAs) exhibit excellent combinations of mechanical properties and radiation-resistant, are considered potential candidates for aerospace industries and advanced reactors. However, the quantitative contribution of microstructure on the strengthening mechanism remains challenging at the micro-scale, which greatly limits the long-term application. To address this, we developed a hierarchical multiscale simulation framework that covers potential physical mechanisms to explore the hardening effects of chemical short-range order (CSRO) and irradiation defects in MPEA. Firstly, by combining atomic simulation, discrete dislocation dynamics, and crystal plasticity finite element method, a hierarchical cross-scale model covering heterostructure lattice… More >

Jianjun Liu¹, Yang Li^2,*, Xiaoting Sun^3,*, Xiaohui Wang¹, Hanjiang Luo²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3551-3568, 2025, DOI:10.32604/cmc.2025.066864 - 23 September 2025

Abstract Multimodal image fusion plays an important role in image analysis and applications. Multimodal medical image fusion helps to combine contrast features from two or more input imaging modalities to represent fused information in a single image. One of the critical clinical applications of medical image fusion is to fuse anatomical and functional modalities for rapid diagnosis of malignant tissues. This paper proposes a multimodal medical image fusion network (MMIF-Net) based on multiscale hybrid attention. The method first decomposes the original image to obtain the low-rank and significant parts. Then, to utilize the features at different More >

Xi Li^1,2, Runpu Nie^1,*, Zhaoyong Fan², Lianying Zou², Zhenhua Xiao², Kaile Dong¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2959-2984, 2025, DOI:10.32604/cmc.2025.066740 - 23 September 2025

Abstract With the rapid development of robotics, grasp prediction has become fundamental to achieving intelligent physical interactions. To enhance grasp detection accuracy in unstructured environments, we propose a novel Cross-Multiscale Adaptive Collaborative and Fusion Grasp Detection Network (CMACF-Net). Addressing the limitations of conventional methods in capturing multi-scale spatial features, CMACF-Net introduces the Quantized Multi-scale Global Attention Module (QMGAM), which enables precise multi-scale spatial calibration and adaptive spatial-channel interaction, ultimately yielding a more robust and discriminative feature representation. To reduce the degradation of local features and the loss of high-frequency information, the Cross-scale Context Integration Module (CCI) More >

Xiaoping Yang^1,2,3, Jinku Qiu^2,3,4, Xifa Gong⁵, Jin Ye⁵, Fei Yao^5,*, Jiaqiao Chen⁶, Xianzan Luo⁶, Da Qin⁶

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1515-1539, 2025, DOI:10.32604/cmc.2025.067518 - 29 August 2025

Abstract In contemporary society, rapid and accurate optical cable fault detection is of paramount importance for ensuring the stability and reliability of optical networks. The emergence of novel faults in optical networks has introduced new challenges, significantly compromising their normal operation. Machine learning has emerged as a highly promising approach. Consequently, it is imperative to develop an automated and reliable algorithm that utilizes telemetry data acquired from Optical Time-Domain Reflectometers (OTDR) to enable real-time fault detection and diagnosis in optical fibers. In this paper, we introduce a multi-scale Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM) deep… More >

Da Li¹, Yuqing Tian¹, Chengxin Weng^2,3, Fuyou Liang^1,4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2867-2882, 2025, DOI:10.32604/cmes.2025.066444 - 30 June 2025

Abstract Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a life support intervention for patients with refractory cardiogenic shock or severe cardiopulmonary failure. However, the choice of cannulation strategy remains contentious, partly due to insufficient understanding of hemodynamic characteristics associated with the site of arterial cannulation. In this study, a geometrical multiscale model was built to offer a mathematical tool for addressing the issue. The outflow cannula of ECMO was inserted into the ascending aorta in the case of central cannulation, whereas it was inserted into the right subclavian artery (RSA) or the left iliac artery (LIA) in… More >

Zhigao Zeng¹, Jun Liu¹, Bing Zheng², Shengqiu Yi¹, Xinpan Yuan¹, Qiang Liu^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 513-536, 2025, DOI:10.32604/cmc.2025.063257 - 09 June 2025

Abstract To solve the problems of complex lesion region morphology, blurred edges, and limited hardware resources for deploying the recognition model in pneumonia image recognition, an improved EfficientNetV2 pneumonia recognition model based on multiscale attention is proposed. First, the number of main module stacks of the model is reduced to avoid overfitting, while the dilated convolution is introduced in the first convolutional layer to expand the receptive field of the model; second, a redesigned improved mobile inverted bottleneck convolution (IMBConv) module is proposed, in which GSConv is introduced to enhance the model’s attention to inter-channel information,… More >

Chaozhi Cai, Yuqi Ren, Yingfang Xue^*, Jianhua Ren

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 665-682, 2025, DOI:10.32604/sdhm.2025.059044 - 03 April 2025

Abstract Rolling bearings are important parts of industrial equipment, and their fault diagnosis is crucial to maintaining these equipment’s regular operations. With the goal of improving the fault diagnosis accuracy of rolling bearings under complex working conditions and noise, this study proposes a multiscale information fusion method for fault diagnosis of rolling bearings based on fast Fourier transform (FFT) and variational mode decomposition (VMD), as well as the Senet (SE)-TCNnet (TCN) model. FFT is used to transform the original one-dimensional time domain vibration signal into a frequency domain signal, while VMD is used to decompose the… More >

Qingxiang Pei^1,2, Fan Li^2,3, Ziheng Fei⁴, Haojie Lian^2,3, Xiaohui Yuan^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 79-96, 2025, DOI:10.32604/cmc.2025.061741 - 26 March 2025

Abstract This paper employs the Direct Finite Element Squared (DFE²) method to develop Sparse Polynomial Chaos Expansions (SPCE) models for analyzing the electromechanical properties of multiscale piezoelectric structures. By incorporating variations in piezoelectric and elastic constants, the DFE² method is utilized to simulate the statistical characteristics—such as expected values and standard deviations—of electromechanical properties, including Mises stress, maximum in-plane principal strain, electric potential gradient, and electric potential, under varying parameters. This approach achieves a balance between computational efficiency and accuracy. Different SPCE models are used to investigate the influence of piezoelectric and elastic constants on multiscale piezoelectric More >

Xin Wen^*, Xiao Zheng, Yu He

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4371-4388, 2025, DOI:10.32604/cmc.2025.060661 - 06 March 2025

Abstract Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation. However, existing detection methods often struggle with challenges such as complex defect morphology, texture similarity, and fuzzy edges, leading to poor accuracy and missed detections. In order to resolve these problems, we propose MSCM-Net (Multi-Scale Cross-Modal Network), a multiscale cross-modal framework focused on detecting rail surface defects. MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps, effectively capturing and enhancing features at different scales for each modality. To… More >