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  • Open Access

    ARTICLE

    Outage Probability Analysis for D2D-Enabled Heterogeneous Cellular Networks with Exclusion Zone: A Stochastic Geometry Approach

    Yulei Wang1, Li Feng1,*, Shumin Yao1,2, Hong Liang1, Haoxu Shi1, Yuqiang Chen3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 639-661, 2024, DOI:10.32604/cmes.2023.029565 - 22 September 2023

    Abstract Interference management is one of the most important issues in the device-to-device (D2D)-enabled heterogeneous cellular networks (HetCNets) due to the coexistence of massive cellular and D2D devices in which D2D devices reuse the cellular spectrum. To alleviate the interference, an efficient interference management way is to set exclusion zones around the cellular receivers. In this paper, we adopt a stochastic geometry approach to analyze the outage probabilities of cellular and D2D users in the D2D-enabled HetCNets. The main difficulties contain three aspects: 1) how to model the location randomness of base stations, cellular and D2D More >

  • Open Access

    REVIEW

    Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network: A Comprehensive Survey

    Md. Shohidul Islam1,*, Md. Arafatur Rahman2, Mohamed Ariff Bin Ameedeen1, Husnul Ajra1, Zahian Binti Ismail1, Jasni Mohamad Zain3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 43-123, 2024, DOI:10.32604/cmes.2023.028687 - 22 September 2023

    Abstract Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance, transportation, healthcare, education, and supply chain management. Blockchain interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application exchanges. However, the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain nodes. There is the biggest challenge of data integrity and scalability, including significant computing complexity and inapplicable latency on… More > Graphic Abstract

    Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network: A Comprehensive Survey

  • Open Access

    ARTICLE

    LIM1863 is useful to explore collective cancer cell migration, and the group of heterogeneous cells undergoing collective migration behaves like a supracellular unit

    JINSONG WU1,2, ZHENG ZHI1, WENZHONG XU1, DIANCGENG LI1, QIUBO LI1, YAN HAN1, JIANMING HE1,3,*, XI LIANG1,*

    BIOCELL, Vol.47, No.12, pp. 2671-2680, 2023, DOI:10.32604/biocell.2023.043494 - 27 December 2023

    Abstract Introduction: Collective cancer cell migration (CCCM) and epithelial-to-mesenchymal transition (EMT) play key roles in metastasis. This study reports that the colorectal carcinoma cell line LIM1863 is useful for the study of CCCM and EMT. Methods: Hematoxylin and eosin staining, scanning electron microscopy, transmission electron microscopy, and western blot analysis were performed. Results: LIM1863 automatically grew as spheroids in suspension and had important typical epithelial properties, including several layers of cells arranged around a central lumen, apical-basal polarity, and types of cell-cell junctions. Treatment with a combination of both TGF beta 1 and TNF alpha induced definite and… 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 - 31 October 2023

    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… 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 - 31 October 2023

    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… 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 - 08 October 2023

    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… 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 - 08 October 2023

    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… 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 - 08 October 2023

    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… 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 - 08 October 2023

    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 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 - 08 October 2023

    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… More >

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