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  • Open Access

    ARTICLE

    Experimental and Numerical Optimization of Prestressed Anchor Cable Support for In-Situ Large-Span Tunnel Expansion with an Energy Balance Framework

    Ying Zhu1,2, Minghui Hu3, Shengxu Wang1,2, Xiaoliang Dong3,*, Xuewen Xiao1,2, Richeng Liu3, Meng Wang1,2, Zheng Yuan3

    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.076381

    Abstract In-situ enlargement of super-large-span tunnels can intensify excavation-induced unloading in the surrounding rock, increasing deformation demand and failure risk during construction. This study combines laboratory model tests with FLAC3D simulations to evaluate the stabilizing role of prestressed anchor cables and to establish an energy-balance framework for support optimization. Comparative model tests of existing and enlarged tunnel sections, with and without anchors, show that reinforcement increases load-carrying capacity, reduces displacement, and confines damage to more localized zones. The numerical simulations reproduce displacement fields, shear-strain localization, and plastic-zone evolution with good agreement against the experimental observations. The energy More >

  • Open Access

    REVIEW

    The Trajectory of Data-Driven Structural Health Monitoring: A Review from Traditional Methods to Deep Learning and Future Trends for Civil Infrastructures

    Luiz Tadeu Dias Júnior, Rafaelle Piazzaroli Finotti, Flávio de Souza Barbosa*, Alexandre Abrahão Cury

    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.075433

    Abstract Structural Health Monitoring (SHM) plays a critical role in ensuring the safety, integrity, longevity and economic efficiency of civil infrastructures. The field has undergone a profound transformation over the last few decades, evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems. This review paper analyzes this historical trajectory, beginning with the approaches that relied on modal parameters as primary damage indicators. The advent of advanced sensor technologies and increased computational power brings a significant change, making Machine Learning (ML) a viable and powerful tool for damage assessment. More recently, Deep Learning (DL) has More >

  • Open Access

    ARTICLE

    A Novel Improved Puma Optimizer to Boost Photovoltaic Array Production in Partially Shaded Environments

    Nagwan Abdel Samee1, Ahmed Fathy2,*, Mohamed A. Mahdy3, Maali Alabdulhafith1, Essam H. Houssein4,5

    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.069931

    Abstract This research proposes an improved Puma optimization algorithm (IPuma) as a novel dynamic reconfiguration tool for a photovoltaic (PV) array linked in total-cross-tied (TCT). The proposed algorithm utilizes the Newton-Raphson search rule (NRSR) to boost the exploration process, especially in search spaces with more local regions, and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma. The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems. It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the… More >

  • Open Access

    ARTICLE

    Model Agnostic Meta Learning Ensemble Based Prediction of Motor Imagery Tasks Using EEG Signals

    Fazal Ur Rehman1, Yazeed Alkhrijah2, Syed Muhammad Usman3, Muhammad Irfan1,*

    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.076332

    Abstract Automated detection of Motor Imagery (MI) tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation. Prediction of MI tasks can be performed with the help of Electroencephalogram (EEG) signals recorded by placing electrodes on the scalp of subjects; however, accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process, the extraction of a feature vector with high interclass variance, and accurate classification. The proposed method consists of preprocessing, feature extraction, and classification. First, EEG signals are denoised using a… More >

  • Open Access

    ARTICLE

    Subtle Micro-Tremor Fusion: A Cross-Modal AI Framework for Early Detection of Parkinson’s Disease from Voice and Handwriting Dynamics

    H. Ahmed1, Naglaa E. Ghannam2,*, H. Mancy3, Esraa A. Mahareek4

    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.075732

    Abstract Parkinson’s disease remains a major clinical issue in terms of early detection, especially during its prodromal stage when symptoms are not evident or not distinct. To address this problem, we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage. We used 5 publicly accessible datasets, including UCI Parkinson’s Voice, Spiral Drawings, PaHaW, NewHandPD, and PPMI, and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation. The findings reveal that the model’s performance was superior and achieved 98.2%, a More >

  • Open Access

    ARTICLE

    Novel Statistical Shape Relation and Prediction of Personalized Female Pelvis, Pelvic Floor, and Perineal Muscle Shapes

    Tan-Nhu Nguyen1,2, Trong-Pham Nguyen-Huu1,2, Tien-Tuan Dao3,*

    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.075386

    Abstract Vaginal delivery is a fascinating physiological process, but also a high-risk process. Up to 85%–90% of vaginal deliveries lead to perineal trauma, with nearly 11% of severe perineal tearing. It is a common occurrence, especially for first-time mothers. Computational childbirth plays an essential role in the prediction and prevention of these traumas, but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity. This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation. 300 subjects were selected from public Computed… More >

  • Open Access

    ARTICLE

    CANNSkin: A Convolutional Autoencoder Neural Network-Based Model for Skin Cancer Classification

    Abdul Jabbar Siddiqui1,2,*, Saheed Ademola Bello2, Muhammad Liman Gambo2, Abdul Khader Jilani Saudagar3,*, Mohamad A. Alawad4, Amir Hussain5

    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.074283

    Abstract Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities, variations in skin texture, the presence of hair, and inconsistent illumination. Deep learning models have shown promise in assisting early detection, yet their performance is often limited by the severe class imbalance present in dermoscopic datasets. This paper proposes CANNSkin, a skin cancer classification framework that integrates a convolutional autoencoder with latent-space oversampling to address this imbalance. The autoencoder is trained to reconstruct lesion images, and its latent embeddings are used as features for classification. To enhance minority-class representation, the Synthetic Minority Oversampling… More >

  • Open Access

    ARTICLE

    Transformation of Verbal Descriptions of Process Flows into Business Process Modelling and Notation Models Using Multimodal Artificial Intelligence: Application in Justice

    Silvia Alayón1,*, Carlos Martín1, Jesús Torres1, Manuel Bacallado1, Rosa Aguilar1, Guzmán Savirón2

    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073488

    Abstract Business Process Modelling (BPM) is essential for analyzing, improving, and automating the flow of information within organizations, but traditional approaches based on manual interpretation are slow, error-prone, and require a high level of expertise. This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation (BPMN) diagrams solely from verbal descriptions of the processes to be modeled, utilizing Large Language Models (LLMs) and multimodal Artificial Intelligence (AI). Experimental results, based on video recordings of process explanations provided by an expert from an organization (in this case,… More >

  • Open Access

    ARTICLE

    Human Activity Recognition Using Weighted Average Ensemble by Selected Deep Learning Models

    Waseem Akhtar1,2, Mahwish Ilyas3, Romana Aziz4,*, Ghadah Aldehim4, Tassawar Iqbal5, Muhammad Ramzan6

    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.071669

    Abstract Human Activity Recognition (HAR) is a novel area for computer vision. It has a great impact on healthcare, smart environments, and surveillance while is able to automatically detect human behavior. It plays a vital role in many applications, such as smart home, healthcare, human computer interaction, sports analysis, and especially, intelligent surveillance. In this paper, we propose a robust and efficient HAR system by leveraging deep learning paradigms, including pre-trained models, CNN architectures, and their average-weighted fusion. However, due to the diversity of human actions and various environmental influences, as well as a lack of… More >

  • Open Access

    ARTICLE

    Computational Modeling for Mortality Prediction in Medical Sciences Based on a Proto-Digital Twin Framework

    Victor Leiva1,2,*, Carlos Martin-Barreiro3,*, Viviana Giampaoli4

    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.074800

    Abstract Mortality prediction in respiratory health is challenging, especially when using large-scale clinical datasets composed primarily of categorical variables. Traditional digital twin (DT) frameworks often rely on longitudinal or sensor-based data, which are not always available in public health contexts. In this article, we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset. This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System, including both COVID-19 and non-COVID-19 patients. Four classification models—extreme gradient boosting (XGBoost), logistic regression, random forest, and a deep neural… More >

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