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  • Open Access

    ARTICLE

    DDNet: A Novel Dynamic Lightweight Super-Resolution Algorithm for Arbitrary Scales

    Yiqiao Gong1,2, Chunlai Wu1, Wenfeng Zheng1,*, Siyu Lu3, Guangyu Xu4, Lijuan Zhang5, Lirong Yin6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2223-2252, 2025, DOI:10.32604/cmes.2025.072136 - 26 November 2025

    Abstract Recent Super-Resolution (SR) algorithms often suffer from excessive model complexity, high computational costs, and limited flexibility across varying image scales. To address these challenges, we propose DDNet, a dynamic and lightweight SR framework designed for arbitrary scaling factors. DDNet integrates a residual learning structure with an Adaptively fusion Feature Block (AFB) and a scale-aware upsampling module, effectively reducing parameter overhead while preserving reconstruction quality. Additionally, we introduce DDNetGAN, an enhanced variant that leverages a relativistic Generative Adversarial Network (GAN) to further improve texture realism. To validate the proposed models, we conduct extensive training using the More >

  • Open Access

    ARTICLE

    Solid Model Generation and Shape Analysis of Human Crystalline Lens Using 3D Digitization and Scanning Techniques

    José Velázquez, Dolores Ojados, Adrián Semitiel, Francisco Cavas*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1821-1837, 2025, DOI:10.32604/cmes.2025.071131 - 26 November 2025

    Abstract This research establishes a methodological framework for generating geometrically accurate 3D representations of human crystalline lenses through scanning technologies and digital reconstruction. Multiple scanning systems were evaluated to identify optimal approaches for point cloud processing and subsequent development of parameterized solid models, facilitating comprehensive morpho-geometric characterization. Experimental work was performed at the 3D Scanning Laboratory of SEDIC (Industrial Design and Scientific Calculation Service) at the Technical University of Cartagena, employing five distinct scanner types based on structured light, laser, and infrared technologies. Test specimens—including preliminary calibration using a lentil and biological analysis of a human… More >

  • Open Access

    ARTICLE

    A Lightweight Explainable Deep Learning for Blood Cell Classification

    Ngoc-Hoang-Quyen Nguyen1, Thanh-Tung Nguyen2, Anh-Cang Phan3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2435-2456, 2025, DOI:10.32604/cmes.2025.070419 - 26 November 2025

    Abstract Blood cell disorders are among the leading causes of serious diseases such as leukemia, anemia, blood clotting disorders, and immune-related conditions. The global incidence of hematological diseases is increasing, affecting both children and adults. In clinical practice, blood smear analysis is still largely performed manually, relying heavily on the experience and expertise of laboratory technicians or hematologists. This manual process introduces risks of diagnostic errors, especially in cases with rare or morphologically ambiguous cells. The situation is more critical in developing countries, where there is a shortage of specialized medical personnel and limited access to… More > Graphic Abstract

    A Lightweight Explainable Deep Learning for Blood Cell Classification

  • Open Access

    ARTICLE

    Automatic Potential Safety Hazard Detection for High-Speed Railroad Surrounding Environment Using Lightweight Hybrid Dual Tasks Architecture

    Zheda Zhao, Tao Xu, Tong Yang, Yunpeng Wu*, Fengxiang Guo*

    Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1457-1472, 2025, DOI:10.32604/sdhm.2025.069611 - 17 November 2025

    Abstract Utilizing unmanned aerial vehicle (UAV) photography to timely detect and evaluate potential safety hazards (PSHs) around high-speed rail has great potential to complement and reform the existing manual inspections by providing better overhead views and mitigating safety issues. However, UAV inspections based on manual interpretation, which heavily rely on the experience, attention, and judgment of human inspectors, still inevitably suffer from subjectivity and inaccuracy. To address this issue, this study proposes a lightweight hybrid learning algorithm named HDTA (hybrid dual tasks architecture) to automatically and efficiently detect the PSHs of UAV imagery. First, this HDTA… More >

  • Open Access

    ARTICLE

    The Advanced Structural Health Monitoring by Non-Destructive Self-Powered Wireless Lightweight Sensor

    Wael A. Altabey*

    Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1529-1545, 2025, DOI:10.32604/sdhm.2025.069003 - 17 November 2025

    Abstract This paper aims to study a novel smart self-powered wireless lightweight (SPWL) bridge health monitoring sensor, which integrates key technologies such as large-scale, low-power wireless data transmission, environmental energy self-harvesting, and intelligent perception, and can operate stably for a long time in complex and changing environments. The self-powered system of the sensor can meet the needs of long-term bridge service performance monitoring, significantly improving the coverage and efficiency of monitoring. By optimizing the sensor system design, the maximum energy conversion of the energy harvesting unit is achieved. In order to verify the function and practicality More > Graphic Abstract

    The Advanced Structural Health Monitoring by Non-Destructive Self-Powered Wireless Lightweight Sensor

  • Open Access

    ARTICLE

    Improved YOLO11 for Maglev Train Foreign Object Detection

    Qinzhen Fang1,2, Dongliang Peng1,2, Lu Zeng1,2,*, Zixuan Jiang1,2

    Journal on Artificial Intelligence, Vol.7, pp. 469-484, 2025, DOI:10.32604/jai.2025.073016 - 06 November 2025

    Abstract To address the issues of small target miss detection, false positives in complex scenarios, and insufficient real-time performance in maglev train foreign object intrusion detection, this paper proposes a multi-module fusion improvement algorithm, YOLO11-FADA (Fusion of Augmented Features and Dynamic Attention), based on YOLO11. The model achieves collaborative optimization through three key modules: The Local Feature Augmentation Module (LFAM) enhances small target features and mitigates feature loss during down-sampling through multi-scale feature parallel extraction and attention fusion. The Dynamically Tuned Self-Attention (DTSA) module introduces learnable parameters to adjust attention weights dynamically, and, in combination with More >

  • Open Access

    ARTICLE

    GWO-LightGBM: A Hybrid Grey Wolf Optimized Light Gradient Boosting Model for Cyber-Physical System Security

    Adeel Munawar1, Muhammad Nadeem Ali2, Awais Qasim3, Byung-Seo Kim2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1189-1211, 2025, DOI:10.32604/cmes.2025.071876 - 30 October 2025

    Abstract Cyber-physical systems (CPS) represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing, healthcare, and autonomous infrastructure. However, their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats, potentially leading to operational failures and data breaches. Furthermore, CPS faces significant threats related to unauthorized access, improper management, and tampering of the content it generates. In this paper, we propose an intrusion detection system (IDS) optimized for CPS environments using a hybrid approach by combining a nature-inspired feature selection scheme, such as Grey Wolf Optimization (GWO),… More >

  • Open Access

    ARTICLE

    Risk Indicator Identification for Coronary Heart Disease via Multi-Angle Integrated Measurements and Sequential Backward Selection

    Hui Qi1, Jingyi Lian2, Congjun Rao2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 995-1028, 2025, DOI:10.32604/cmes.2025.069722 - 30 October 2025

    Abstract For the past few years, the prevalence of cardiovascular disease has been showing a year-on-year increase, with a death rate of 2/5. Coronary heart disease (CHD) rates have increased 41% since 1990, which is the number one disease endangering human health in the world today. The risk indicators of CHD are complicated, so selecting effective methods to screen the risk characteristics can make the risk prediction more efficient. In this paper, we present a comprehensive analysis of CHD risk indicators from both data and algorithmic levels, propose a method for CHD risk indicator identification based… More >

  • Open Access

    PROCEEDINGS

    Light Interacted Soft Units for Mechanical Logics

    Nan Yang*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-1, 2025, DOI:10.32604/icces.2025.011055

    Abstract Integrating mechanical computing capabilities into robotic materials or systems enhances their intelligence in stimulus-response processes. However, current mechanical computing systems face limitations such as incomplete functionality, inflexible computational rules, challenges in implementing sequential and random logic operations, and lack of reusability. To address these issues, we propose a straightforward design method based on logical expressions to achieve more complex computational tasks. We developed soft B-shaped mechanical metamaterial units and introduced stress inputs through compression. The outputs are represented by light-shielding effects caused by unit deformation. Using this approach, we successfully implemented logic gates and their… More >

  • Open Access

    ARTICLE

    Genome-Wide Identification and Characterization of FAR1 in Phaseolus vulgaris under Salt and Drought Stress Conditions

    Abdil Hakan Eren*

    Phyton-International Journal of Experimental Botany, Vol.94, No.10, pp. 3085-3107, 2025, DOI:10.32604/phyton.2025.069506 - 29 October 2025

    Abstract The FAR1-related sequence (FAR1) gene family consists of transcription factors that originated from transposases and is crucial for light signaling and stress adaptation in plants. Despite the recognized importance of FAR1 genes in model organisms, their genomic architecture, structural variability, and expression patterns in Phaseolus vulgaris have yet to be investigated. This study offers the inaugural comprehensive genome-wide identification and characterization of the FAR1 gene family in P. vulgaris. A total of 27 PvulFAR1 genes were identified, and their chromosomal distribution, gene structures, conserved domains, and phylogenetic relationships were analyzed systematically. The promoter regions of these genes were discovered… More >

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