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  • Open Access


    Spatio-temporal pattern detection in spatio-temporal graphs

    Use case of invasive team sports and urban road traffic

    Kamaldeep Singh Oberoi1, Géraldine Del Mondo2

    Revue Internationale de Géomatique, Vol.31, No.2, pp. 377-399, 2022, DOI:10.3166/RIG.31.377-399 c 2022

    Abstract Spatio-temporal (ST) graphs have been used in many application domains to model evolving ST phenomenon. Such models represent the underlying structure of the phenomenon in terms of its entities and different types of spatial interactions between them. The reason behind using graph-based models to represent ST phenomenon is due to the existing well-established graph analysis tools and algorithms which can be directly applied to analyze the phenomenon under consideration. In this paper, considering the use case of two distinct, highly dynamic phenomena - invasive team sports, with a focus on handball and urban road traffic, we propose a spatio-temporal graph… More >

  • Open Access


    Robust and Trustworthy Data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

    Jinyang Yu1,2, Xiao Zhang1,2,3,*, Jinjiang Wang1,2, Yuchen Zhang1,2, Yulong Shi1,2, Linxuan Su1,2, Leijie Zeng1,2,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2159-2179, 2024, DOI:10.32604/cmc.2024.047340

    Abstract The proliferation of Internet of Things (IoT) systems has resulted in the generation of substantial data, presenting new challenges in reliable storage and trustworthy sharing. Conventional distributed storage systems are hindered by centralized management and lack traceability, while blockchain systems are limited by low capacity and high latency. To address these challenges, the present study investigates the reliable storage and trustworthy sharing of IoT data, and presents a novel system architecture that integrates on-chain and off-chain data manage systems. This architecture, integrating blockchain and distributed storage technologies, provides high-capacity, high-performance, traceable, and verifiable data storage and access. The on-chain system,… More >

  • Open Access


    Einstein Hybrid Structure of q-Rung Orthopair Fuzzy Soft Set and Its Application for Diagnosis of Waterborne Infectious Disease

    Rana Muhammad Zulqarnain1, Hafiz Khalil ur Rehman2, Imran Siddique3, Hijaz Ahmad4,5, Sameh Askar6, Shahid Hussain Gurmani1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1863-1892, 2024, DOI:10.32604/cmes.2023.031480

    Abstract This research is devoted to diagnosing water-borne infectious diseases caused by floods employing a novel diagnosis approach, the Einstein hybrid structure of q-rung orthopair fuzzy soft set. This approach integrates parts of fuzzy logic and soft set theory to develop a robust alternative for disease detection in stressful situations, especially in areas affected by floods. Compared to the traditional intuitionistic fuzzy soft set and Pythagorean fuzzy soft set, the q-rung orthopair fuzzy soft set (q-ROFSS) adequately incorporates unclear and indeterminate facts. The major objective of this investigation is to formulate the q-rung orthopair fuzzy soft Einstein hybrid weighted average (q-ROFSEHWA)… More >

  • Open Access


    An Intelligent MCGDM Model in Green Suppliers Selection Using Interactional Aggregation Operators for Interval-Valued Pythagorean Fuzzy Soft Sets

    Rana Muhammad Zulqarnain1, Wen-Xiu Ma1,2,3,*, Imran Siddique4, Hijaz Ahmad5,6, Sameh Askar7

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1829-1862, 2024, DOI:10.32604/cmes.2023.030687

    Abstract Green supplier selection is an important debate in green supply chain management (GSCM), attracting global attention from scholars, especially companies and policymakers. Companies frequently search for new ideas and strategies to assist them in realizing sustainable development. Because of the speculative character of human opinions, supplier selection frequently includes unreliable data, and the interval-valued Pythagorean fuzzy soft set (IVPFSS) provides an exceptional capacity to cope with excessive fuzziness, inconsistency, and inexactness through the decision-making procedure. The main goal of this study is to come up with new operational laws for interval-valued Pythagorean fuzzy soft numbers (IVPFSNs) and create two interaction… More >

  • Open Access


    Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM

    Jiajie He1,2, Fuzheng Liu3, Xiangyi Geng3, Xifeng Liang1, Faye Zhang3,*, Mingshun Jiang3

    Structural Durability & Health Monitoring, Vol.18, No.1, pp. 37-54, 2024, DOI:10.32604/sdhm.2023.029428

    Abstract Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods, making it challenging to ensure the fault diagnosis accuracy and reliability. A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator (ESGMD-CC) and artificial fish swarm algorithm (AFSA) optimized extreme learning machine (ELM) is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis. Firstly, SGMD decomposes the raw vibration signal into multiple Symplectic geometry components (SGCs). Secondly, the iterations are reset by the… More >

  • Open Access


    Ice-Induced Vibrational Response of Single-Pile Offshore Wind-Turbine Foundations

    Zhoujie Zhu1, Gang Wang1, Qingquan Liu1, Guojun Wang2, Rui Dong2, Dayong Zhang2,3,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.3, pp. 625-639, 2024, DOI:10.32604/fdmp.2023.042128

    Abstract Important challenges must be addressed to make wind turbines sustainable renewable energy sources. A typical problem concerns the design of the foundation. If the pile diameter is larger than that of the jacket platform, traditional mechanical models cannot be used. In this study, relying on the seabed soil data of an offshore wind farm, the m-method and the equivalent embedded method are used to address the single-pile wind turbine foundation problem for different pile diameters. An approach to determine the equivalent pile length is also proposed accordingly. The results provide evidence for the effectiveness and reliability of the model based… More >

  • Open Access


    Field Observation and Numerical Simulation of Extreme Met-Ocean Conditions: A Case Study of Typhoon Events in South China Sea

    Chen Gu1,*, Caiyu Wang1, Mengjiao Du2, Kan Yi2, Bihong Zhu1, Hao Wang2, Shu Dai1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.09776

    Abstract Site measurement is essential to the meteorological and oceanographic parameters of offshore wind farms. A floating lidar measurement buoy was deployed at a Qingzhou VI wind farm where is 45-80 km away from Guangdong coast. The field observation including wind and wave data start from March, 2021.The lidar wind data is compared and calibrated with the fixed wind tower data for three months, the accuracy meets the standard of stadge3 carbon trust. In this study, all these data are used to recalibrate for the met-ocean model to relies extreme conditions, such as Typhoon Kompasu(2118) and Typhoon Chaba(2203) in recent years.… More >

  • Open Access


    Exploratory Study of Intolerance of Uncertainty and Its Cognitive Processes by the Stroop Task among Parents of a Childhood Cancer Survivor

    Etude exploratoire de l’intolérance à l’incertitude et de ses processus cognitifs par la tâche du Stroop chez les parents d’un enfant en rémission de cancer

    Marie Vander Haegen*, Anne-Marie Etienne

    Psycho-Oncologie, Vol.17, No.4, pp. 257-266, 2023, DOI:10.32604/po.2023.043340

    Abstract Introduction. No study has examined the relationship between the factor of intolerance of uncertainty and its effects on the cognitive processes among parents of a childhood cancer survivor. Intolerance of uncertainty is a central factor in generalized anxiety disorder; an anxiety disorder in which impaired processing of cognitive information in the form of cognitive biases is also observed. The aim of the study was to measure, via a classical Stroop test and an emotional Stroop test, the orientation of selective attention in relation to the uncertainty intolerance factor. Methods. 61 parents of a childhood cancer survivor (aged 4 to 6… More >

  • Open Access


    RB-DEM Modeling and Simulation of Non-Persisting Rough Open Joints Based on the IFS-Enhanced Method

    Hangtian Song1,2, Xudong Chen1,2, Chun Zhu3, Qian Yin4, Wei Wang1,2, Qingxiang Meng1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 337-359, 2024, DOI:10.32604/cmes.2023.031496

    Abstract When the geological environment of rock masses is disturbed, numerous non-persisting open joints can appear within it. It is crucial to investigate the effect of open joints on the mechanical properties of rock mass. However, it has been challenging to generate realistic open joints in traditional experimental tests and numerical simulations. This paper presents a novel solution to solve the problem. By utilizing the stochastic distribution of joints and an enhanced-fractal interpolation system (IFS) method, rough curves with any orientation can be generated. The Douglas-Peucker algorithm is then applied to simplify these curves by removing unnecessary points while preserving their… More > Graphic Abstract

    RB-DEM Modeling and Simulation of Non-Persisting Rough Open Joints Based on the IFS-Enhanced Method

  • Open Access


    CFSA-Net: Efficient Large-Scale Point Cloud Semantic Segmentation Based on Cross-Fusion Self-Attention

    Jun Shu1,2, Shuai Wang1,2, Shiqi Yu1,2, Jie Zhang3,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2677-2697, 2023, DOI:10.32604/cmc.2023.045818

    Abstract Traditional models for semantic segmentation in point clouds primarily focus on smaller scales. However, in real-world applications, point clouds often exhibit larger scales, leading to heavy computational and memory requirements. The key to handling large-scale point clouds lies in leveraging random sampling, which offers higher computational efficiency and lower memory consumption compared to other sampling methods. Nevertheless, the use of random sampling can potentially result in the loss of crucial points during the encoding stage. To address these issues, this paper proposes cross-fusion self-attention network (CFSA-Net), a lightweight and efficient network architecture specifically designed for directly processing large-scale point clouds.… More >

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