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  • Open Access

    ARTICLE

    Algorithmically Enhanced Data-Driven Prediction of Shear Strength for Concrete-Filled Steel Tubes

    Shengkang Zhang1, Yong Jin2,*, Soon Poh Yap1,*, Haoyun Fan1, Shiyuan Li3, Ahmed El-Shafie4, Zainah Ibrahim1, Amr El-Dieb5

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075351 - 29 January 2026

    Abstract Concrete-filled steel tubes (CFST) are widely utilized in civil engineering due to their superior load-bearing capacity, ductility, and seismic resistance. However, existing design codes, such as AISC and Eurocode 4, tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core. To address this limitation, this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer (PKO), a nature-inspired algorithm, to enhance the accuracy of shear strength prediction for CFST columns. Additionally, quantile regression is employed to construct prediction intervals for… More >

  • Open Access

    ARTICLE

    Heating the Future: Solar Hot Water Collectors for Energy-Efficient Homes in Sweden

    Mehran Karimi1, Hesamodin Heidarigoujani1, Mehdi Jahangiri1,*, Milad Torabi Anaraki2, Daryosh Mohamadi Janaki3

    Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070190 - 27 January 2026

    Abstract The technical, economic, and environmental performance of solar hot-water (SWH) systems for Swedish residential apartments—where approximately 80% of household energy is devoted to space heating and sanitary hot-water production—was assessed. Two collector types, flat plate (FP) and evacuated tube (ET), were simulated in TSOL Pro 5.5 for five major cities (Stockholm, Göteborg, Malmö, Uppsala, Linköping). Climatic data and cold-water temperatures were sourced from Meteonorm 7.1, and economic parameters were derived from recent national statistics and literature. All calculations explicitly accounted for heat losses from collectors, storage tanks, and internal and external piping systems, and established… More >

  • Open Access

    ARTICLE

    Hederagenin Alleviated Ovariectomy-Induced Bone Loss through the Regulation of Innate Immune Signaling in Mice

    Zhitao Yang1,#, Huanyu Cheng1,#, Xinli Liu1, Jie Li1, Xin Ming1, Beibei Li1, Luyao Zhang1, Chunqing MA1, Yi Jiao1, Shenjia Wu1, Ibrar Muhammad Khan2, Guanghua Xiong1, Hongcheng Wang1,*, Yong Liu1,*

    BIOCELL, Vol.50, No.1, 2026, DOI:10.32604/biocell.2025.072736 - 23 January 2026

    Abstract Objectives: Postmenopausal osteoporosis is the most common form of osteoporosis in clinical practice, affecting millions of postmenopausal women worldwide. Postmenopausal osteoporosis demands safe and effective therapies. This study aimed to evaluate the potential of hederagenin (Hed) for treating osteoporosis and to elucidate its underlying mechanisms of action. Methods: The anti-osteoporotic potential of Hed was assessed by investigating its effects on ovariectomy (OVX)-induced bone loss in mice and on receptor activator of NF-kappaB ligand (RANKL)-induced osteoclast differentiation in RAW264.7 cells. Network pharmacology analysis and molecular docking were employed to identify key targets, which were subsequently validated experimentally. Results:More >

  • Open Access

    ARTICLE

    Advanced Video Processing and Data Transmission Technology for Unmanned Ground Vehicles in the Internet of Battlefield Things (loBT)

    Tai Liu1,2, Mao Ye2,*, Feng Wu3, Chao Zhu2, Bo Chen2, Guoyan Zhang1,*

    CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072692 - 12 January 2026

    Abstract With the continuous advancement of unmanned technology in various application domains, the development and deployment of blind-spot-free panoramic video systems have gained increasing importance. Such systems are particularly critical in battlefield environments, where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles (UGVs). However, conventional video surveillance systems suffer from several limitations, including limited field of view, high processing latency, low reliability, excessive resource consumption, and significant transmission delays. These shortcomings impede the widespread adoption of UGVs in battlefield settings. To overcome these… More >

  • Open Access

    ARTICLE

    Research on the Classification of Digital Cultural Texts Based on ASSC-TextRCNN Algorithm

    Zixuan Guo1, Houbin Wang2, Sameer Kumar1,*, Yuanfang Chen3

    CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072064 - 12 January 2026

    Abstract With the rapid development of digital culture, a large number of cultural texts are presented in the form of digital and network. These texts have significant characteristics such as sparsity, real-time and non-standard expression, which bring serious challenges to traditional classification methods. In order to cope with the above problems, this paper proposes a new ASSC (ALBERT, SVD, Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model. Based on the framework of TextRCNN, the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding. Combined with the dual attention mechanism, the… More >

  • Open Access

    ARTICLE

    MFF-YOLO: A Target Detection Algorithm for UAV Aerial Photography

    Dike Chen1,2,3, Zhiyong Qin2, Ji Zhang2, Hongyuan Wang1,2,*

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.072494 - 09 December 2025

    Abstract To address the challenges of small target detection and significant scale variations in unmanned aerial vehicle (UAV) aerial imagery, which often lead to missed and false detections, we propose Multi-scale Feature Fusion YOLO (MFF-YOLO), an enhanced algorithm based on YOLOv8s. Our approach introduces a Multi-scale Feature Fusion Strategy (MFFS), comprising the Multiple Features C2f (MFC) module and the Scale Sequence Feature Fusion (SSFF) module, to improve feature integration across different network levels. This enables more effective capture of fine-grained details and sequential multi-scale features. Furthermore, we incorporate Inner-CIoU, an improved loss function that uses auxiliary More >

  • Open Access

    ARTICLE

    CLF-YOLOv8: Lightweight Multi-Scale Fusion with Focal Geometric Loss for Real-Time Night Maritime Detection

    Zhonghao Wang1,2, Xin Liu1,2,*, Changhua Yue3, Haiwen Yuan4

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.071813 - 09 December 2025

    Abstract To address critical challenges in nighttime ship detection—high small-target missed detection (over 20%), insufficient lightweighting, and limited generalization due to scarce, low-quality datasets—this study proposes a systematic solution. First, a high-quality Night-Ships dataset is constructed via CycleGAN-based day-night transfer, combined with a dual-threshold cleaning strategy (Laplacian variance sharpness filtering and brightness-color deviation screening). Second, a Cross-stage Lightweight Fusion-You Only Look Once version 8 (CLF-YOLOv8) is proposed with key improvements: the Neck network is reconstructed by replacing Cross Stage Partial (CSP) structure with the Cross Stage Partial Multi-Scale Convolutional Block (CSP-MSCB) and integrating Bidirectional Feature Pyramid More >

  • Open Access

    ARTICLE

    A Composite Loss-Based Autoencoder for Accurate and Scalable Missing Data Imputation

    Thierry Mugenzi, Cahit Perkgoz*

    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.070381 - 10 November 2025

    Abstract Missing data presents a crucial challenge in data analysis, especially in high-dimensional datasets, where missing data often leads to biased conclusions and degraded model performance. In this study, we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision. The proposed loss combines (i) a guided, masked mean squared error focusing on missing entries; (ii) a noise-aware regularization term to improve resilience against data corruption; and (iii) a variance penalty to encourage expressive yet stable reconstructions. We evaluate the proposed model across four missingness mechanisms, such as Missing… More >

  • Open Access

    ARTICLE

    Multi-Constraint Generative Adversarial Network-Driven Optimization Method for Super-Resolution Reconstruction of Remote Sensing Images

    Binghong Zhang, Jialing Zhou, Xinye Zhou, Jia Zhao, Jinchun Zhu, Guangpeng Fan*

    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068309 - 10 November 2025

    Abstract Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring, urban planning, and disaster assessment. However, traditional methods exhibit deficiencies in detail recovery and noise suppression, particularly when processing complex landscapes (e.g., forests, farmlands), leading to artifacts and spectral distortions that limit practical utility. To address this, we propose an enhanced Super-Resolution Generative Adversarial Network (SRGAN) framework featuring three key innovations: (1) Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing; (2) A multi-loss joint optimization strategy… More >

  • Open Access

    ARTICLE

    Numerical Study of Fluid Loss Impact on Long-Term Performance of Enhanced Geothermal Systems under Varying Operational Parameters

    Yongwei Li1, Kaituo Jiao2,*, Dongxu Han3, Bo Yu2, Xiaoze Du1

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3453-3479, 2025, DOI:10.32604/cmes.2025.073239 - 23 December 2025

    Abstract The permeability contrast between the Hot Dry Rock (HDR) reservoir and the surrounding formations is a key factor governing fluid loss in Enhanced Geothermal Systems (EGS). This study thus aims to investigate its impact on system performance under varying operating conditions, and a three-dimensional thermo–hydro–mechanical (THM) coupled EGS model is developed based on the geological parameters of the GR1 well in the Qiabuqia region. The coupled processes of fluid flow, heat transfer, and geomechanics within the reservoir under varying reservoir–surrounding rock permeability contrasts, as well as the flow and heat exchange along the wellbores from… More >

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