Wang Zhang^1,#, Haozhuo Cao^2,#, Qiangqiang Yao^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072948 - 12 January 2026

Abstract Recent advances in deep learning have significantly improved image deblurring; however, existing approaches still suffer from limited global context modeling, inadequate detail restoration, and poor texture or edge perception, especially under complex dynamic blur. To address these challenges, we propose the Multi-Resolution Fusion Network (MRFNet), a blind multi-scale deblurring framework that integrates progressive residual connectivity for hierarchical feature fusion. The network employs a three-stage design: (1) TransformerBlocks capture long-range dependencies and reconstruct coarse global structures; (2) Nonlinear Activation Free Blocks (NAFBlocks) enhance local detail representation and mid-level feature fusion; and (3) an optimized residual subnetwork… More >

Yuanyuan Wang^1,*, Yemeng Zhu¹, Xiuchuan Chen¹, Tongtong Yin¹, Shiwei Su²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072404 - 12 January 2026

Abstract To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects, similar defects and background features, and similarities between different defects, this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network (MSESE), which is built upon the You Only Look Once version 11 nano (YOLOv11n). To address the difficulty of locating defect edges, we first propose an edge enhancement module (EEM), apply it to the process of multiscale feature extraction, and then propose a multiscale edge enhancement… More >

Chenxi Xiu^1,2,*, Xihua Chu², Ao Mei¹, Liangfei Gong¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-39, 2026, DOI:10.32604/cmc.2025.073193 - 09 December 2025

Abstract Granular materials exhibit complex macroscopic mechanical behaviors closely related to their micro-scale microstructural features. Traditional macroscopic phenomenological elasto-plastic models, however, usually have complex formulations and lack explicit relations to these microstructural features. To avoid these limitations, this study proposes a micromechanics-based softening hyperelastic model for granular materials, integrating softening hyperelasticity with microstructural insights to capture strain softening, critical state, and strain localization behaviors. The model has two key advantages: (1) a clear conceptualization, straightforward formulation, and ease of numerical implementation (via Abaqus UMAT subroutine in this study); (2) explicit incorporation of micro-scale features (e.g., contact… More >

Yanting Zhang¹, Qiyue Liu^1,2, Chuanzhao Tian^1,2,*, Xuewen Li¹, Na Yang¹, Feng Zhang¹, Hongyue Zhang³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068403 - 10 November 2025

Abstract High-resolution remote sensing images (HRSIs) are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies. However, their significant scale changes and wealth of spatial details pose challenges for semantic segmentation. While convolutional neural networks (CNNs) excel at capturing local features, they are limited in modeling long-range dependencies. Conversely, transformers utilize multihead self-attention to integrate global context effectively, but this approach often incurs a high computational cost. This paper proposes a global-local multiscale context network (GLMCNet) to extract both global and local multiscale contextual information from HRSIs.… More >

Nurlan Zhangabay^1,*, Marco Bonopera^2,*, Konstantin Avramov³, Maryna Chernobryvko³, Svetlana Buganova⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 241-261, 2025, DOI:10.32604/cmes.2025.069544 - 30 October 2025

Abstract This research extends ongoing efforts to develop methods for reinforcing damaged main gas pipelines to prevent catastrophic failure. This study establishes the use of scaled-down experimental models for assessing the dynamic strength of damaged pipeline sections reinforced with wire wrapping or composite sleeves. A generalized dynamic model is introduced for numerical simulation to evaluate the effectiveness of reinforcement techniques. The model incorporates the elastoplastic behavior of pipe and wire materials, the influence of temperature on mechanical properties, the contact interaction between the pipe and the reinforcement components (including pretensioning), and local material failure under transient… More >

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

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-1, 2025, DOI:10.32604/icces.2025.010700

Abstract High entropy alloys (HEA) are considered as the candidate materials for the next generation of nuclear systems due to the excellent high temperature properties and radiation resistance. However, for the lack of atomic lattice distortion information from the micromechanical description, the existing simulation methods are difficult to capture the microstructure and damage evolution of the HEA at submicron scale. To address this, we develop the random field theory informed discrete dislocation dynamics simulations based on the results of high-resolution transmission electron microscopy to systematically clarify the role of heterogeneous lattice strain on the complex interactions… More >

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 >