Zhenyu Zhang¹, Quan Jin¹, Haitao Zhang¹, Zhao Liu¹, Yuyang Wu², Longfei Zhang², Renzhang Yan^2,*

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 167-191, 2025, DOI:10.32604/sdhm.2024.053190 - 15 November 2024

Abstract When the upper chord beam of the beam-string structure (BSS) is made of concrete-filled steel tube (CFST), its overall stiffness will change greatly with the construction of concrete placement, which will have an impact on the design of the tensioning plans and selection of control measures for the BSS. In order to accurately obtain the bending stiffness of CFST beam and clarify its impact on the mechanical properties of composite BSS during construction, the influence of some factors such as height-width ratio, wall thickness of steel tube, elasticity modulus of concrete, and friction coefficient on More >

Huayi Zhang¹, Maobin Song², Lei Shen^1,*, Nizar Faisal Alkayem¹, Maosen Cao³

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 55-75, 2025, DOI:10.32604/sdhm.2024.052869 - 15 November 2024

Abstract The concrete panel of earth-rock dams in cold regions tends to crack due to the combination effect of non-uniform foundation settlement, ice expansion loads, and freeze-thaw damage. In this work, simulations are designed to investigate the effects of freeze-thaw damage degrees on the fracture behavior caused by the partial detachment and ice expansion loads on concrete panels. Results show that the range of detached panels and freeze-thaw damage degree are the dominant factors that affect the overall load-bearing capacity of the panel and the failure cracking modes, whereas the panel slope is a secondary factor. More >

Zhao-Jun Zhang¹, Wen-Wei Wang^1,2,*, Jing-Shui Zhen¹, Bo-Cheng Li¹, De-Cheng Cai¹, Yang-Yang Du¹, Hui Huang²

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 105-123, 2025, DOI:10.32604/sdhm.2024.052506 - 15 November 2024

Abstract This study aimed to investigate the moment redistribution in continuous glass fiber reinforced polymer (GFRP)-concrete composite slabs caused by concrete cracking and steel bar yielding in the negative bending moment zone. An experimental bending moment redistribution test was conducted on continuous GFRP-concrete composite slabs, and a calculation method based on the conjugate beam method was proposed. The composite slabs were formed by combining GFRP profiles with a concrete layer and supported on steel beams to create two-span continuous composite slab specimens. Two methods, epoxy resin bonding, and stud connection, were used to connect the composite… More >

Aruna M. Pugalenthi^*, Khaoula Khlie

Journal of Renewable Materials, Vol.12, No.11, pp. 1879-1893, 2024, DOI:10.32604/jrm.2024.055747 - 22 November 2024

Abstract With growing environmental concerns and the depletion of oil reserves, the need to replace synthetic fibres with sustainable alternatives in composite materials has become increasingly urgent. This study investigates the potential of Leptadenia pyrotechnica fibre as a sustainable reinforcement material in hybrid composites alongside E-glass fibres. The primary objectives are to assess these hybrid composites’ mechanical properties, structural integrity, and performance. To achieve this, Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) were employed to analyze the microstructure and chemical composition of the composites. At the same time, mechanical testing focused on properties such… More >

Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Chi-Chou Kao^1,*, Hung-Yi Lin²

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1581-1594, 2024, DOI:10.32604/csse.2024.055073 - 22 November 2024

Abstract Layout synthesis in quantum computing is crucial due to the physical constraints of quantum devices where quantum bits (qubits) can only interact effectively with their nearest neighbors. This constraint severely impacts the design and efficiency of quantum algorithms, as arranging qubits optimally can significantly reduce circuit depth and improve computational performance. To tackle the layout synthesis challenge, we propose an algorithm based on integer linear programming (ILP). ILP is well-suited for this problem as it can formulate the optimization objective of minimizing circuit depth while adhering to the nearest neighbor interaction constraint. The algorithm aims… More >

Salman Ahmed¹, Fazli Subhan^2,3, Mazliham Mohd Su’ud^3,*, Muhammad Mansoor Alam^3,4, Adil Waheed⁵

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1483-1511, 2024, DOI:10.32604/csse.2024.053997 - 22 November 2024

Abstract In the contemporary era, the death rate is increasing due to lung cancer. However, technology is continuously enhancing the quality of well-being. To improve the survival rate, radiologists rely on Computed Tomography (CT) scans for early detection and diagnosis of lung nodules. This paper presented a detailed, systematic review of several identification and categorization techniques for lung nodules. The analysis of the report explored the challenges, advancements, and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning (DL) algorithm. The findings also highlighted the usefulness of DL… More >

Nurhadi, Rabiah Abdul Kadir^*, Ely Salwana Mat Surin, Mahidur R. Sarker^*

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1405-1435, 2024, DOI:10.32604/csse.2024.051851 - 22 November 2024

Abstract A data lake (DL), abbreviated as DL, denotes a vast reservoir or repository of data. It accumulates substantial volumes of data and employs advanced analytics to correlate data from diverse origins containing various forms of semi-structured, structured, and unstructured information. These systems use a flat architecture and run different types of data analytics. NoSQL databases are nontabular and store data in a different manner than the relational table. NoSQL databases come in various forms, including key-value pairs, documents, wide columns, and graphs, each based on its data model. They offer simpler scalability and generally outperform… More >

Arar Al Tawil^1,*, Laiali Almazaydeh², Doaa Qawasmeh³, Baraah Qawasmeh⁴, Mohammad Alshinwan^1,5, Khaled Elleithy⁶

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3395-3412, 2024, DOI:10.32604/cmc.2024.057279 - 18 November 2024

Abstract Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information, a practice known as phishing. This study utilizes three distinct methodologies, Term Frequency-Inverse Document Frequency, Word2Vec, and Bidirectional Encoder Representations from Transformers, to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks. The study uses feature extraction methods to assess the performance of Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron algorithms. The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron (Precision: 0.98, Recall: 0.98, F1-score: 0.98, Accuracy: 0.98). Word2Vec’s More >

Mohd Asif Hajam¹, Tasleem Arif¹, Akib Mohi Ud Din Khanday², Mudasir Ahmad Wani^3,*, Muhammad Asim^3,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2077-2131, 2024, DOI:10.32604/cmc.2024.057136 - 18 November 2024

Abstract The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and cost-effectiveness compared to modern drugs. Throughout the extensive history of medicinal plant usage, various plant parts, including flowers, leaves, and roots, have been acknowledged for their healing properties and employed in plant identification. Leaf images, however, stand out as the preferred and easily accessible source of information. Manual plant identification by plant taxonomists is intricate, time-consuming, and prone to errors, relying heavily on human perception. Artificial intelligence (AI) techniques offer a solution by automating plant recognition processes. This study thoroughly examines cutting-edge… More >