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  • Open Access


    Combo Packet: An Encryption Traffic Classification Method Based on Contextual Information

    Yuancong Chai, Yuefei Zhu*, Wei Lin, Ding Li

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1223-1243, 2024, DOI:10.32604/cmc.2024.049904

    Abstract With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has become a core key technology in network supervision. In recent years, many different solutions have emerged in this field. Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-level features of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites, temporal features can exhibit significant variations due to changes in communication links and transmission quality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission. Faced with these challenges, identifying… More >

  • Open Access


    A Study on Enhancing Chip Detection Efficiency Using the Lightweight Van-YOLOv8 Network

    Meng Huang, Honglei Wei*, Xianyi Zhai

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 531-547, 2024, DOI:10.32604/cmc.2024.048510

    Abstract In pursuit of cost-effective manufacturing, enterprises are increasingly adopting the practice of utilizing recycled semiconductor chips. To ensure consistent chip orientation during packaging, a circular marker on the front side is employed for pin alignment following successful functional testing. However, recycled chips often exhibit substantial surface wear, and the identification of the relatively small marker proves challenging. Moreover, the complexity of generic target detection algorithms hampers seamless deployment. Addressing these issues, this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips, termed Van-YOLOv8. Initially, to alleviate the influence of diminutive, low-resolution markings on the precision of… More >

  • Open Access


    Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

    Kai Wei1, Song Yu2, Qingxian Pan1,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 607-622, 2024, DOI:10.32604/cmc.2024.048240

    Abstract Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation. While traditional methods for task allocation can help reduce costs and improve efficiency, they may encounter challenges when dealing with abnormal data flow nodes, leading to decreased allocation accuracy and efficiency. To address these issues, this study proposes a novel two-part invalid detection task allocation framework. In the first step, an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data. Compared to the baseline method, the model achieves an approximately 4% increase in the F1 value on the public dataset. In… More >

  • Open Access


    Deciphering resistance mechanisms and novel strategies to overcome drug resistance in ovarian cancer: a comprehensive review


    Oncology Research, Vol.32, No.5, pp. 831-847, 2024, DOI:10.32604/or.2024.031006

    Abstract Ovarian cancer is among the most lethal gynecological cancers, primarily due to the lack of specific symptoms leading to an advanced-stage diagnosis and resistance to chemotherapy. Drug resistance (DR) poses the most significant challenge in treating patients with existing drugs. The Food and Drug Administration (FDA) has recently approved three new therapeutic drugs, including two poly (ADP-ribose) polymerase (PARP) inhibitors (olaparib and niraparib) and one vascular endothelial growth factor (VEGF) inhibitor (bevacizumab) for maintenance therapy. However, resistance to these new drugs has emerged. Therefore, understanding the mechanisms of DR and exploring new approaches to overcome them is crucial for effective… More >

  • Open Access


    Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

    Lyuchao Liao1,2, Hankun Xiao2,*, Pengqi Xing2, Zhenhua Gan1,2, Youpeng He2, Jiajun Wang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 557-576, 2024, DOI:10.32604/cmes.2024.047452

    Abstract Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception Enhanced Deep Deterministic Policy Gradient (PE-DDPG) for Autonomous Driving in the Roundabouts. Specifically, the model incorporates an enhanced variational… More >

  • Open Access



    Shihong Zhang#, Ning Li, Zhihua Wang

    Frontiers in Heat and Mass Transfer, Vol.2, No.3, pp. 1-5, 2011, DOI:10.5098/hmt.v2.3.3004

    Abstract This article discussed the thermal efficiency, stability and pollutant emissions characteristics of the combustion of lean natural gas-air mixtures in Pd metal based honeycomb monoliths by means of experiments on a practical burner V. The chemistry at work in the monoliths was then investigated by the stagnation point flow reactor( SPFR), a fundamental experimental reactor. It was found that catalytic combustion inhibited the extent of gas-phase oxidation and increased the surface temperature of homogeneous ignition. According to the applications of catalytic combustion in the condenser boiler, the data of catalytic combustion condenser boiler V were measured at atmospheric temperature and… More >

  • Open Access


    Exploring the molecular mechanisms and potential therapeutic strategies of ferroptosis in ovarian cancer


    BIOCELL, Vol.48, No.3, pp. 379-386, 2024, DOI:10.32604/biocell.2024.047812

    Abstract The morbidity rate of ovarian cancer, a malignant tumour in gynaecological tumours, is rising, and it is considered to be the most lethal cancer. The majority of patients are typically diagnosed during the advanced stages of the illness due to the elusive characteristics of ovarian cancer and an absence of highly sensitive and specific diagnostic indicators. Surgical excision of the lesions, along with chemotherapy, is the conventional treatment for ovarian cancer; however, resistance to platinum-based chemotherapeutic drugs and molecular targeted therapies frequently arises. Improving the survival rate and prognosis of patients with end-stage or recurring ovarian cancer requires the identification… More >

  • Open Access


    MicroRNAs modulation in lung cancer: exploring dual mechanisms and clinical prospects


    BIOCELL, Vol.48, No.3, pp. 403-413, 2024, DOI:10.32604/biocell.2024.044801

    Abstract The global incidence of lung cancer is marked by a considerably elevated mortality rate. MicroRNAs (miRNAs) exert pivotal influence in the intricate orchestration of gene regulation, and their dysregulation can precipitate dire consequences, notably cancer. Within this context, miRNAs encapsulated in exosomes manifest a diversified impact on the landscape of lung cancer, wherein their actions may either foster angiogenesis, cell proliferation, and metastasis, or counteract these processes. This comprehensive review article discerns potential targets for the prospective development of therapeutic agents tailored for lung cancer. Tumor-suppressive miRNAs, such as miR-204, miR-192, miR-30a, miR-34a, miR-34b, miR-203, and miR-212, exhibit heightened expression… More >

  • Open Access


    Enhancing Image Description Generation through Deep Reinforcement Learning: Fusing Multiple Visual Features and Reward Mechanisms

    Yan Li, Qiyuan Wang*, Kaidi Jia

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2469-2489, 2024, DOI:10.32604/cmc.2024.047822

    Abstract Image description task is the intersection of computer vision and natural language processing, and it has important prospects, including helping computers understand images and obtaining information for the visually impaired. This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images. Our method focuses on refining the reward function in deep reinforcement learning, facilitating the generation of precise descriptions by aligning visual and textual features more closely. Our approach comprises three key architectures. Firstly, it utilizes Residual Network 101 (ResNet-101) and Faster Region-based Convolutional Neural Network (Faster R-CNN) to extract average… More >

  • Open Access


    Physiological and Transcriptome Analysis Illuminates the Molecular Mechanisms of the Drought Resistance Improved by Alginate Oligosaccharides in Triticum aestivum L.

    Yunhong Zhang1,2,*, Yonghui Yang1,2, Jiawei Mao1,2

    Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 185-212, 2024, DOI:10.32604/phyton.2023.046811

    Abstract Alginate oligosaccharides (AOS) enhance drought resistance in wheat (Triticum aestivum L.), but the definite mechanisms remain largely unknown. The physiological and transcriptome responses of wheat seedlings treated with AOS were analyzed under drought stress simulated with polyethylene glycol-6000. The results showed that AOS promoted the growth of wheat seedlings and reduced oxidative damage by improving peroxidase and superoxide dismutase activities under drought stress. A total of 10,064 and 15,208 differentially expressed unigenes (DEGs) obtained from the AOS treatment and control samples at 24 and 72 h after dehydration, respectively, were mainly enriched in the biosynthesis of secondary metabolites (phenylpropanoid biosynthesis,… More >

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