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  • Open Access


    Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

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

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1711-1728, 2024, DOI:10.32604/cmes.2024.048955

    Abstract A significant obstacle in intelligent transportation systems (ITS) is the capacity to predict traffic flow. Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately. However, accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors. This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory (Conv-BiLSTM) with attention mechanisms. Prior studies neglected to include data pertaining to factors such as holidays, weather conditions, and More >

  • Open Access


    SPATULA as a Versatile Tool in Plant: The Progress and Perspectives of SPATULA (SPT) Transcriptional Factor

    Lei Liang, Xiangyang Hu*

    Phyton-International Journal of Experimental Botany, Vol.93, No.3, pp. 517-531, 2024, DOI:10.32604/phyton.2024.049277

    Abstract With the rapid development of modern molecular biology and bioinformatics, many studies have proved that transcription factors play an important role in regulating the growth and development of plants. SPATULA (SPT) belongs to the bHLH transcription family and participates in many processes of regulating plant growth and development. This review systemically summarizes the multiple roles of SPT in plant growth, development, and stress response, including seed germination, flowering, leaf size, carpel development, and root elongation, which is helpful for us to better understand the functions of SPT. More >

  • Open Access


    The Analysis of the Correlation between SPT and CPT Based on CNN-GA and Liquefaction Discrimination Research

    Ruihan Bai1, Feng Shen2,*, Zihao Zhao3, Zhiping Zhang4, Qisi Yu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1159-1182, 2024, DOI:10.32604/cmes.2023.029562

    Abstract The objective of this study is to investigate the methods for soil liquefaction discrimination. Typically, predicting soil liquefaction potential involves conducting the standard penetration test (SPT), which requires field testing and can be time-consuming and labor-intensive. In contrast, the cone penetration test (CPT) provides a more convenient method and offers detailed and continuous information about soil layers. In this study, the feature matrix based on CPT data is proposed to predict the standard penetration test blow count N. The feature matrix comprises the CPT characteristic parameters at specific depths, such as tip resistance qc, sleeve resistance fs,… More >

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