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  • Open Access

    ARTICLE

    Bioconvective Hybrid Flow with Microorganisms Migration and Buongiorno’s Model under Convective Condition

    Azad Hussain1, Saira Raiz1, Ali Hassan1,2,*, Mohamed R. Ali3, Abdulkafi Mohammed Saeed4

    Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 433-453, 2024, DOI:10.32604/fhmt.2024.044121

    Abstract Heat transfer improves significantly when the working fluid has high thermal conductivity. Heat transfer can be found in fields such as food processing, solar through collectors, and drug delivery. Considering this notable fact, this work is focused on investigating the bio-convection-enhanced heat transfer in the existence of convective boundary conditions in the flow of hybrid nanofluid across a stretching surface. Buongiorno fluid model with hybrid nanoparticles has been employed along the swimming microorganisms to investigate the mixture base working fluid. The developed nonlinear flow governing equations have been tackled numerically with the help of the… More > Graphic Abstract

    Bioconvective Hybrid Flow with Microorganisms Migration and Buongiorno’s Model under Convective Condition

  • Open Access

    ARTICLE

    The Turbulent Schmidt Number for Transient Contaminant Dispersion in a Large Ventilated Room Using a Realizable k-ε Model

    Fei Wang, Qinpeng Meng, Jinchi Zhao, Xin Wang, Yuhong Liu, Qianru Zhang*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.4, pp. 829-846, 2024, DOI:10.32604/fdmp.2023.026917

    Abstract Buildings with large open spaces in which chemicals are handled are often exposed to the risk of explosions. Computational fluid dynamics is a useful and convenient way to investigate contaminant dispersion in such large spaces. The turbulent Schmidt number (Sct) concept has typically been used in this regard, and most studies have adopted a default value. We studied the concentration distribution for sulfur hexafluoride (SF6) assuming different emission rates and considering the effect of Sct. Then we examined the same problem for a light gas by assuming hydrogen gas (H2) as the contaminant. When SF6 was considered as More >

  • Open Access

    ARTICLE

    Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

    Yunchang Liu1,*, Fei Wan1, Chengwu Liang2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4343-4361, 2024, DOI:10.32604/cmc.2024.047211

    Abstract Traffic flow prediction plays a key role in the construction of intelligent transportation system. However, due to its complex spatio-temporal dependence and its uncertainty, the research becomes very challenging. Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes. However, due to the time-varying spatial correlation of the traffic network, there is no fixed node relationship, and these methods cannot effectively integrate the temporal and spatial features. This paper proposes a novel temporal-spatial dynamic graph More >

  • Open Access

    ARTICLE

    Prediction of Bandwidth of Metamaterial Antenna Using Pearson Kernel-Based Techniques

    Sherly Alphonse1,*, S. Abinaya1, Sourabh Paul2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3449-3467, 2024, DOI:10.32604/cmc.2024.046403

    Abstract The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas. The radiation cost and quality factor of the antenna are influenced by the size of the antenna. Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas. Antenna parameters have recently been predicted using machine learning algorithms in existing literature. Machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters. The accuracy of the prediction will be primarily dependent on the model that is used. In… More >

  • Open Access

    ARTICLE

    Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

    Xianfeng Cao1, Meihua Yao2, Yahui Zhang3,*, Xiaofeng Hu4, Chuanxun Wu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3233-3253, 2024, DOI:10.32604/cmes.2023.030773

    Abstract As the take-off of China’s macro economy, as well as the rapid development of infrastructure construction, real estate industry, and highway logistics transportation industry, the demand for heavy vehicles is increasing rapidly, the competition is becoming increasingly fierce, and the digital transformation of the production line is imminent. As one of the most important components of heavy vehicles, the transmission front and middle case assembly lines have a high degree of automation, which can be used as a pilot for the digital transformation of production. To ensure the visualization of digital twins (DT), consistent control More > Graphic Abstract

    Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

  • Open Access

    ARTICLE

    CircTHSD4 promotes the malignancy and docetaxel (DTX) resistance in prostate cancer by regulating miR-203/HMGA2 axis

    JIANYUN XIE1,*, LINJIE LU1, JIALI ZHANG1, QIRUI LI2, WEIDONG CHEN1,*

    Oncology Research, Vol.32, No.3, pp. 529-544, 2024, DOI:10.32604/or.2023.031511

    Abstract Objective: Circular ribose nucleic acids (circRNAs) are implicated in tumor progression and drug resistance of prostate cancer (PCa). The current work explored the function of circ_0005203 (circTHSD4) in the malignancy and docetaxel (DTX) resistance of PCa. Methods: circTHSD4 expression within PCa as well as matched non-carcinoma samples was measured through real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR). In addition, a subcellular fraction assay was conducted to determine circTHSD4 subcellular localization within PCa cells. In addition, we performed a Western blot (WB) assay to detect high-mobility-group A2 protein (HMGA2) levels. Besides, functional associations of two molecules… More >

  • Open Access

    ARTICLE

    A Novel Fractional Dengue Transmission Model in the Presence of Wolbachia Using Stochastic Based Artificial Neural Network

    Zeshan Faiz1, Iftikhar Ahmed1, Dumitru Baleanu2,3,4, Shumaila Javeed1,5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1217-1238, 2024, DOI:10.32604/cmes.2023.029879

    Abstract The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model (FDTM) in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network (LM-NN) technique. The fractional dengue transmission model (FDTM) consists of 12 compartments. The human population is divided into four compartments; susceptible humans (Sh), exposed humans (Eh), infectious humans (Ih), and recovered humans (Rh). Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments: aquatic (eggs, larvae, pupae), susceptible, exposed, and infectious. We investigated three different cases of vertical transmission probability (η), namely when Wolbachia-free mosquitoes persist only… More >

  • Open Access

    ARTICLE

    CHDTEPDB: Transcriptome Expression Profile Database and Interactive Analysis Platform for Congenital Heart Disease

    Ziguang Song1,2, Jiangbo Yu1, Mengmeng Wang3, Weitao Shen4, Chengcheng Wang1, Tianyi Lu1, Gaojun Shan1, Guo Dong1, Yiru Wang1, Jiyi Zhao1,*

    Congenital Heart Disease, Vol.18, No.6, pp. 693-701, 2023, DOI:10.32604/chd.2024.048081

    Abstract CHDTEPDB (URL: ) is a manually integrated database for congenital heart disease (CHD) that stores the expression profiling data of CHD derived from published papers, aiming to provide rich resources for investigating a deeper correlation between human CHD and aberrant transcriptome expression. The development of human diseases involves important regulatory roles of RNAs, and expression profiling data can reflect the underlying etiology of inherited diseases. Hence, collecting and compiling expression profiling data is of critical significance for a comprehensive understanding of the mechanisms and functions that underpin genetic diseases. CHDTEPDB stores the expression profiles of… More >

  • Open Access

    ARTICLE

    A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

    Naeem Ullah1, Javed Ali Khan2,*, Sultan Almakdi3, Mohammed S. Alshehri3, Mimonah Al Qathrady4, Eman Abdullah Aldakheel5,*, Doaa Sami Khafaga5

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3969-3992, 2023, DOI:10.32604/cmc.2023.041819

    Abstract Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for… More >

  • Open Access

    ARTICLE

    Modeling Geometrically Nonlinear FG Plates: A Fast and Accurate Alternative to IGA Method Based on Deep Learning

    Se Li1, Tiantang Yu1,*, Tinh Quoc Bui2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2793-2808, 2024, DOI:10.32604/cmes.2023.030278

    Abstract Isogeometric analysis (IGA) is known to show advanced features compared to traditional finite element approaches. Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functional grading (FG). However, the procedure is usually complex and often is time-consuming. We thus put forward a deep learning method to model the geometrically nonlinear bending behavior of FG plates, bypassing the complex IGA simulation process. A long bidirectional short-term memory (BLSTM) recurrent neural network is trained using the load and gradient index as inputs and the displacement responses as outputs. The nonlinear relationship between More >

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