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  • Open Access

    ARTICLE

    CFD Investigation of Diffusion Law and Harmful Boundary of Buried Natural Gas Pipeline in the Mountainous Environment

    Liqiong Chen1, Kui Zhao1, Kai Zhang1,*, Duo Xv1, Hongxvan Hu2, Guoguang Ma1, Wenwen Zhan3

    Energy Engineering, Vol.121, No.8, pp. 2143-2165, 2024, DOI:10.32604/ee.2024.049362

    Abstract The leakage gas from a buried natural gas pipelines has the great potential to cause economic losses and environmental pollution owing to the complexity of the mountainous environment. In this study, computational fluid dynamics (CFD) method was applied to investigate the diffusion law and hazard range of buried natural gas pipeline leakage in mountainous environment. Based on cloud chart, concentration at the monitoring site and hazard range of lower explosion limit (LEL) and upper explosion limit (UEL), the influences of leakage hole direction and shape, soil property, burial depth, obstacle type on the diffusion law… More >

  • Open Access

    ARTICLE

    Energy Blockchain in Smart Communities: Towards Affordable Clean Energy Supply for the Built Environment

    Mingguan Zhao1,4, Lida Liao2,*, Penglong Liang1, Meng Li1, Xinsheng Dong1, Yang Yang1, Hongxia Wang1, Zhenhao Zhang3

    Energy Engineering, Vol.121, No.8, pp. 2313-2330, 2024, DOI:10.32604/ee.2024.048261

    Abstract The rapid growth of distributed renewable energy penetration is promoting the evolution of the energy system toward decentralization and decentralized and digitized smart grids. This study was based on energy blockchain, and developed a dual-biding mechanism based on the real-time energy surplus and demand in the local smart grid, which is expected to enable reliable, affordable, and clean energy supply in smart communities. In the proposed system, economic benefits could be achieved by replacing fossil-fuel-based electricity with the high penetration of affordable solar PV electricity. The reduction of energy surplus realized by distributed energy production More >

  • Open Access

    ARTICLE

    Fibroblast activation protein (FAP) as a prognostic biomarker in multiple tumors and its therapeutic potential in head and neck squamous cell carcinoma

    RUIFANG LI1, XINRONG NAN2,*, MING LI3,*, OMAR RAHHAL3

    Oncology Research, Vol.32, No.8, pp. 1323-1334, 2024, DOI:10.32604/or.2024.046965

    Abstract Background: Fibroblast activation protein (FAP), a cell surface serine protease, plays roles in tumor invasion and immune regulation. However, there is currently no pan-cancer analysis of FAP. Objective: We aimed to assess the pan-cancer expression profile of FAP, its molecular function, and its potential role in head and neck squamous cell carcinoma (HNSC). Methods: We analyzed gene expression, survival status, immune infiltration, and molecular functional pathways of FAP in The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) tumors. Furthermore, to elucidate the role of FAP in HNSC, we performed proliferation, migration, and invasion assays… More >

  • Open Access

    REVIEW

    Biometric Authentication System on Mobile Environment: A Review

    Qasem Abu Al-Haija1,*, Sara Othman Al-Salameen2

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 897-914, 2024, DOI:10.32604/csse.2024.050846

    Abstract The paper discusses the importance of biometric verification systems in mobile environments and highlights the challenges and strategies used to overcome them in order to ensure the security of mobile devices. Emphasis is placed on evaluating the impact of illumination on the performance of biometric verification techniques and how to address this challenge using image processing techniques. The importance of accurate and reliable data collection to ensure the accuracy of verification processes is also discussed. The paper also highlights the importance of improving biometric verification techniques and directing research toward developing models aimed at reducing More >

  • Open Access

    ARTICLE

    A Novel Optimization Approach for Energy-Efficient Multiple Workflow Scheduling in Cloud Environment

    Ambika Aggarwal1, Sunil Kumar2,3, Ashok Bhansali4, Deema Mohammed Alsekait5,*, Diaa Salama AbdElminaam6,7,8

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 953-967, 2024, DOI:10.32604/csse.2024.050406

    Abstract Existing multiple workflow scheduling techniques focus on traditional Quality of Service (QoS) parameters such as cost, deadline, and makespan to find optimal solutions by consuming a large amount of electrical energy. Higher energy consumption decreases system efficiency, increases operational cost, and generates more carbon footprint. These major problems can lead to several problems, such as economic strain, environmental degradation, resource depletion, energy dependence, health impacts, etc. In a cloud computing environment, scheduling multiple workflows is critical in developing a strategy for energy optimization, which is an NP-hard problem. This paper proposes a novel, bi-phase Energy-Efficient… More >

  • Open Access

    ARTICLE

    Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

    Asad Raza1,*, Shahzad Memon1, Muhammad Ali Nizamani1, Mahmood Hussain Shah2

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 545-566, 2024, DOI:10.32604/iasc.2024.051779

    Abstract Smart Industrial environments use the Industrial Internet of Things (IIoT) for their routine operations and transform their industrial operations with intelligent and driven approaches. However, IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet. Traditional signature-based IDS are effective in detecting known attacks, but they are unable to detect unknown emerging attacks. Therefore, there is the need for an IDS which can learn from data and detect new threats. Ensemble Machine Learning (ML) and individual Deep Learning (DL) based IDS have been developed, and these individual models achieved… More >

  • Open Access

    ARTICLE

    Enhancing Critical Path Problem in Neutrosophic Environment Using Python

    M. Navya Pratyusha, Ranjan Kumar*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2957-2976, 2024, DOI:10.32604/cmes.2024.051581

    Abstract In the real world, one of the most common problems in project management is the unpredictability of resources and timelines. An efficient way to resolve uncertainty problems and overcome such obstacles is through an extended fuzzy approach, often known as neutrosophic logic. Our rigorous proposed model has led to the creation of an advanced technique for computing the triangular single-valued neutrosophic number. This innovative approach evaluates the inherent uncertainty in project durations of the planning phase, which enhances the potential significance of the decision-making process in the project. Our proposed method, for the first time… More >

  • Open Access

    ARTICLE

    A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

    Weijian Song1,, Xi Li1,, Peng Chen1,*, Juan Chen1, Jianhua Ren2, Yunni Xia3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3001-3016, 2024, DOI:10.32604/cmes.2024.048563

    Abstract With the rapid development of Internet of Things (IoT) technology, IoT systems have been widely applied in healthcare, transportation, home, and other fields. However, with the continuous expansion of the scale and increasing complexity of IoT systems, the stability and security issues of IoT systems have become increasingly prominent. Thus, it is crucial to detect anomalies in the collected IoT time series from various sensors. Recently, deep learning models have been leveraged for IoT anomaly detection. However, owing to the challenges associated with data labeling, most IoT anomaly detection methods resort to unsupervised learning techniques.… More >

  • Open Access

    ARTICLE

    YOLO-CRD: A Lightweight Model for the Detection of Rice Diseases in Natural Environments

    Rui Zhang1,2, Tonghai Liu1,2,*, Wenzheng Liu1,2, Chaungchuang Yuan1,2, Xiaoyue Seng1,2, Tiantian Guo1,2, Xue Wang1,2

    Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1275-1296, 2024, DOI:10.32604/phyton.2024.052397

    Abstract Rice diseases can adversely affect both the yield and quality of rice crops, leading to the increased use of pesticides and environmental pollution. Accurate detection of rice diseases in natural environments is crucial for both operational efficiency and quality assurance. Deep learning-based disease identification technologies have shown promise in automatically discerning disease types. However, effectively extracting early disease features in natural environments remains a challenging problem. To address this issue, this study proposes the YOLO-CRD method. This research selected images of common rice diseases, primarily bakanae disease, bacterial brown spot, leaf rice fever, and dry… More >

  • Open Access

    ARTICLE

    Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

    Abdulelah Alwabel1,*, Chinmaya Kumar Swain2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4127-4148, 2024, DOI:10.32604/cmc.2024.048833

    Abstract Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources. However, the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes, thus making the application placement problem more complex than that in cloud computing. An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the… More >

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