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 >

Xiaolin Li¹, Xinyue Li¹, Liming Zhou^2,*, Hangran Yang¹, Xiaoqing Yuan¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3821-3841, 2025, DOI:10.32604/cmc.2025.059937 - 06 March 2025

Abstract Magneto-electro-elastic (MEE) materials are widely utilized across various fields due to their multi-field coupling effects. Consequently, investigating the coupling behavior of MEE composite materials is of significant importance. The traditional finite element method (FEM) remains one of the primary approaches for addressing such issues. However, the application of FEM typically necessitates the use of a fine finite element mesh to accurately capture the heterogeneous properties of the materials and meet the required computational precision, which inevitably leads to a reduction in computational efficiency. To enhance the computational accuracy and efficiency of the FEM for heterogeneous… More >

Xueli Shen, Meng Wang^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4631-4647, 2025, DOI:10.32604/cmc.2025.059149 - 06 March 2025

Abstract In the field of Weakly Supervised Semantic Segmentation (WSSS), methods based on image-level annotation face challenges in accurately capturing objects of varying sizes, lacking sensitivity to image details, and having high computational costs. To address these issues, we improve the dual-branch architecture of the Conformer as the fundamental network for generating class activation graphs, proposing a multi-scale efficient weakly-supervised semantic segmentation method based on the improved Conformer. In the Convolution Neural Network (CNN) branch, a cross-scale feature integration convolution module is designed, incorporating multi-receptive field convolution layers to enhance the model’s ability to capture long-range… More >

Naikang Zhong¹, Xiao Lin^1,2,3,4,*, Wen Du⁵, Jin Shi⁶

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5285-5306, 2025, DOI:10.32604/cmc.2025.059102 - 06 March 2025

Abstract Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images. Obtaining class-specific precise representations at different scales is a key aspect of feature representation. However, existing methods often rely on the single-scale deep feature, neglecting shallow and deeper layer features, which poses challenges when predicting objects of varying scales within the same image. Although some studies have explored multi-scale features, they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales. To address these issues, we propose… More >

Chengzhang Zhu^1,2, Ahmed Alasri¹, Tao Xu³, Yalong Xiao^1,2,*, Abdulrahman Noman¹, Raeed Alsabri¹, Xuanchu Duan⁴, Monir Abdullah⁵

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5153-5167, 2025, DOI:10.32604/cmc.2024.058647 - 06 March 2025

Abstract Globally, diabetic retinopathy (DR) is the primary cause of blindness, affecting millions of people worldwide. This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment. Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment. However, traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level. On the other hand, models that focus on global semantic-level information might overlook critical, subtle local pathological features. To address this issue, we propose an… More >

Cheng Sun^1,3, Chen Zhao^2,3, Guohua Wang^2,3, Qianwen Mao^2,3, Rong Han^2,3,*

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 455-479, 2025, DOI:10.32604/phyton.2025.061462 - 06 March 2025

Abstract Enhanced UV-B radiation represents a major environmental factor impacting global cereal production. Researchers have explored various approaches to reduce the detrimental impact of UV-B radiation on crops. Recently, engineered nanoparticles, particularly cerium oxide nanoparticles (CeO₂-NPs), have attracted widespread interest for their ability to boost plant tolerance to a range of abiotic stresses. This study investigates how CeO₂-NPs application affects the morphology, physiology, biochemistry, and transcriptomics profiles of wheat seedling roots subjected to enhanced UV-B stress. The findings demonstrate that CeO₂-NPs notably promoted root length, fresh and dry weights, and root activity (p < 0.05) under enhanced UV-B… More >

Wentao Sheng^*

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 331-345, 2025, DOI:10.32604/phyton.2025.061424 - 06 March 2025

Abstract Pyrola atropurpurea Franch is an important annual herbaceous plant. Few genomic analyses have been conducted on this plant, and chloroplast genome research will enrich its genomics basis. This study is based on high-throughput sequencing technology and Bioinformatics methods to obtain the sequence, structure, and other characteristics of the P. atropurpurea chloroplast genome. The result showed that the chloroplast genome of P. atropurpurea has a double-stranded circular structure with a total length of 172,535 bp and a typical four-segment structure. The genome has annotated a total of 132 functional genes, including 43 tRNAs, 8 rRNAs, 76 protein-coding genes, and… More >

Syed Zain Ul Abideen¹, Mian Muhammad Kamal^2,*, Eaman Alharbi³, Ashfaq Ahmad Malik⁴, Wadee Alhalabi⁵, Muhammad Shahid Anwar^6,*, Liaqat Ali⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2191-2210, 2025, DOI:10.32604/cmes.2025.061744 - 03 March 2025

Abstract The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet of Things (IoT) applications, particularly in terms of ultra-reliable, secure, and energy-efficient communication. This study explores the integration of Reconfigurable Intelligent Surfaces (RIS) into IoT networks to enhance communication performance. Unlike traditional passive reflector-based approaches, RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes, addressing critical IoT challenges such as energy efficiency, limited communication range, and double-fading effects in backscatter communication. We propose a novel computational framework that combines… More >

Baoping Zou^1,2, Chenhao Pei^1,*, Qizhi Chen^1,2, Yansheng Deng^1,2, Yongguo Chen^1,2, Xu Long^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2457-2485, 2025, DOI:10.32604/cmes.2025.061429 - 03 March 2025

Abstract The utilization of multi-field coupling simulation methods has become a pivotal approach for the investigation of intricate fracture behavior and interaction mechanisms of rock masses in deep strata. The high temperatures, pressures and complex geological environments of deep strata frequently result in the coupling of multiple physical fields, including mechanical, thermal and hydraulic fields, during the fracturing of rocks. This review initially presents an overview of the coupling mechanisms of these physical fields, thereby elucidating the interaction processes of mechanical, thermal, and hydraulic fields within rock masses. Secondly, an in-depth analysis of multi-field coupling is… More >

Sonia Khan¹, Naqash Younas², Musaed Alhussein³, Wahib Jamal Khan², Muhammad Shahid Anwar^4,*, Khursheed Aurangzeb³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2641-2660, 2025, DOI:10.32604/cmes.2025.060973 - 03 March 2025

Abstract Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks. However, existing methods often fail in dynamic and high-demand environments, leading to resource bottlenecks and increased energy consumption. This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management (QIARM) model, which introduces novel algorithms inspired by quantum principles for enhanced resource allocation. QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically. In addition, an energy-aware scheduling module minimizes power More >