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  • Open Access

    ARTICLE

    DUAL SOLUTIONS FOR HEAT AND MASS TRANSFER IN MHD JEFFREY FLUID IN THE PRESENCE OF HOMOGENEOUSHETEROGENEOUS REACTIONS

    C. S. K. Rajua , N. Sandeepa, J. Prakashb,1

    Frontiers in Heat and Mass Transfer, Vol.7, pp. 1-8, 2016, DOI:10.5098/hmt.7.14

    Abstract In this study, we analyzed the effects of nonlinear thermal radiation and induced magnetic field on steady two-dimensional incompressible flow of Jeffrey fluid flow past a stretching/shrinking surface in the presence of homogeneous-heterogeneous reactions. For physical relevance in this study we analyzed the behavior of homogeneous and heterogeneous profiles individually. The transformed governing equations with the help of similarity variables are solved numerically via Runge-Kutta and Newton’s method. We obtained better accuracy of the present results by differentiating with the existed published literature. The effect of pertinent parameters on velocity, induced magnetic field, temperature and concentration profiles along with the… More >

  • Open Access

    ARTICLE

    A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing

    Zhaohui Xia1,3, Baichuan Gao3, Chen Yu2,*, Haotian Han3, Haobo Zhang3, Shuting Wang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1103-1137, 2024, DOI:10.32604/cmes.2023.029177

    Abstract This paper aims to solve large-scale and complex isogeometric topology optimization problems that consume significant computational resources. A novel isogeometric topology optimization method with a hybrid parallel strategy of CPU/GPU is proposed, while the hybrid parallel strategies for stiffness matrix assembly, equation solving, sensitivity analysis, and design variable update are discussed in detail. To ensure the high efficiency of CPU/GPU computing, a workload balancing strategy is presented for optimally distributing the workload between CPU and GPU. To illustrate the advantages of the proposed method, three benchmark examples are tested to verify the hybrid parallel strategy in this paper. The results… More > Graphic Abstract

    A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing

  • Open Access

    ARTICLE

    Analysis of CH4 and H2 Adsorption on Heterogeneous Shale Surfaces Using a Molecular Dynamics Approach

    Surajudeen Sikiru1,*, Hassan Soleimani2, Amir Rostami1, Mohammed Falalu Hamza1,3, Lukmon Owolabi Afolabi4

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.1, pp. 31-44, 2024, DOI:10.32604/fdmp.2023.029281

    Abstract Determining the adsorption of shale gas on complex surfaces remains a challenge in molecular simulation studies. Difficulties essentially stem from the need to create a realistic shale structure model in terms of mineral heterogeneity and multiplicity. Moreover, precise characterization of the competitive adsorption of hydrogen and methane in shale generally requires the experimental determination of the related adsorptive capacity. In this study, the adsorption of adsorbates, methane (CH4), and hydrogen (H2) on heterogeneous shale surface models of Kaolinite, Orthoclase, Muscovite, Mica, C60, and Butane has been simulated in the frame of a molecular dynamic’s numerical technique. The results show that… More >

  • Open Access

    ARTICLE

    Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

    Premanand S., Sathiya Narayanan*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 25-45, 2023, DOI:10.32604/cmc.2023.042590

    Abstract Machine Learning (ML) and Deep Learning (DL) technologies are revolutionizing the medical domain, especially with Electrocardiogram (ECG), by providing new tools and techniques for diagnosing, treating, and preventing diseases. However, DL architectures are computationally more demanding. In recent years, researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches, say for example, combining the convolutional layer blocks of Convolution Neural Networks (CNNs) into ML algorithms such as Extreme Gradient Boosting (XGBoost) and K-Nearest Neighbor (KNN) resulting in CNN-XGBoost and CNN-KNN, respectively. However, these approaches are homogenous in the sense that they use a… More >

  • Open Access

    ARTICLE

    Acidic Magnetic Biocarbon-Enabled Upgrading of Biomass-Based Hexanedione into Pyrroles

    Zhimei Li1, Kuan Tian2, Keping Wang2, Zhengyi Li2, Haoli Qin1,*, Hu Li2,*

    Journal of Renewable Materials, Vol.11, No.11, pp. 3847-3865, 2023, DOI:10.32604/jrm.2023.030122

    Abstract Sustainable acquisition of bioactive compounds from biomass-based platform molecules is a green alternative for existing CO2-emitting fossil-fuel technologies. Herein, a core–shell magnetic biocarbon catalyst functionalized with sulfonic acid (Fe3O4@SiO2@chitosan-SO3H, MBC-SO3H) was prepared to be efficient for the synthesis of various N-substituted pyrroles (up to 99% yield) from bio-based hexanedione and amines under mild conditions. The abundance of Brønsted acid sites in the MBC-SO3H ensured smooth condensation of 2,5-hexanedione with a variety of amines to produce N-substituted pyrroles. The reaction was illustrated to follow the conventional PallKnorr coupling pathway, which includes three cascade reaction steps: amination, loop closure and dehydration. The… More > Graphic Abstract

    Acidic Magnetic Biocarbon-Enabled Upgrading of Biomass-Based Hexanedione into Pyrroles

  • Open Access

    ARTICLE

    An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

    Weizheng Wang1,3, Xiangqi Wang2,*, Xianmin Pan1, Xingxing Gong3, Jian Liang3, Pradip Kumar Sharma4, Osama Alfarraj5, Wael Said6

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3859-3876, 2023, DOI:10.32604/cmc.2023.041346

    Abstract Image-denoising techniques are widely used to defend against Adversarial Examples (AEs). However, denoising alone cannot completely eliminate adversarial perturbations. The remaining perturbations tend to amplify as they propagate through deeper layers of the network, leading to misclassifications. Moreover, image denoising compromises the classification accuracy of original examples. To address these challenges in AE defense through image denoising, this paper proposes a novel AE detection technique. The proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network (CNN) network structures. The used detector model integrates the classification results of different models as the input to the detector and calculates the… More >

  • Open Access

    ARTICLE

    Traffic Flow Prediction with Heterogenous Data Using a Hybrid CNN-LSTM Model

    Jing-Doo Wang1, Chayadi Oktomy Noto Susanto1,2,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3097-3112, 2023, DOI:10.32604/cmc.2023.040914

    Abstract Predicting traffic flow is a crucial component of an intelligent transportation system. Precisely monitoring and predicting traffic flow remains a challenging endeavor. However, existing methods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes, resulting in the loss of essential information and lower forecast performance. On the other hand, the availability of spatiotemporal data is limited. This research offers alternative spatiotemporal data with three specific features as input, vehicle type (5 types), holidays (3 types), and weather (10 conditions). In this study, the proposed model combines the advantages of the… More >

  • Open Access

    ARTICLE

    Decentralized Heterogeneous Federal Distillation Learning Based on Blockchain

    Hong Zhu*, Lisha Gao, Yitian Sha, Nan Xiang, Yue Wu, Shuo Han

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3363-3377, 2023, DOI:10.32604/cmc.2023.040731

    Abstract Load forecasting is a crucial aspect of intelligent Virtual Power Plant (VPP) management and a means of balancing the relationship between distributed power grids and traditional power grids. However, due to the continuous emergence of power consumption peaks, the power supply quality of the power grid cannot be guaranteed. Therefore, an intelligent calculation method is required to effectively predict the load, enabling better power grid dispatching and ensuring the stable operation of the power grid. This paper proposes a decentralized heterogeneous federated distillation learning algorithm (DHFDL) to promote trusted federated learning (FL) between different federates in the blockchain. The algorithm… More >

  • Open Access

    ARTICLE

    Topic-Aware Abstractive Summarization Based on Heterogeneous Graph Attention Networks for Chinese Complaint Reports

    Yan Li1, Xiaoguang Zhang1,*, Tianyu Gong1, Qi Dong1, Hailong Zhu1, Tianqiang Zhang1, Yanji Jiang2,3

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3691-3705, 2023, DOI:10.32604/cmc.2023.040492

    Abstract Automatic text summarization (ATS) plays a significant role in Natural Language Processing (NLP). Abstractive summarization produces summaries by identifying and compressing the most important information in a document. However, there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics. In particular, Chinese complaint reports, generated by urban complainers and collected by government employees, describe existing resident problems in daily life. Meanwhile, the reflected problems are required to respond speedily. Therefore, automatic summarization tasks for these reports have been developed. However, similar to traditional… More >

  • Open Access

    ARTICLE

    A Multilevel Hierarchical Parallel Algorithm for Large-Scale Finite Element Modal Analysis

    Gaoyuan Yu1, Yunfeng Lou2, Hang Dong3, Junjie Li1, Xianlong Jin1,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2795-2816, 2023, DOI:10.32604/cmc.2023.037375

    Abstract The strict and high-standard requirements for the safety and stability of major engineering systems make it a tough challenge for large-scale finite element modal analysis. At the same time, realizing the systematic analysis of the entire large structure of these engineering systems is extremely meaningful in practice. This article proposes a multilevel hierarchical parallel algorithm for large-scale finite element modal analysis to reduce the parallel computational efficiency loss when using heterogeneous multicore distributed storage computers in solving large-scale finite element modal analysis. Based on two-level partitioning and four-transformation strategies, the proposed algorithm not only improves the memory access rate through… More >

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