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  • Open Access

    ARTICLE

    Adaptability Analysis of Dual Clearing Systems in Spot Electricity Markets Based on Fuzzy Evaluation Metrics: An Inner Mongolia Case Study

    Kai Xie1, Shaoqing Yuan2, Dayun Zou1, Jinran Wang1,*, Genjun Chen1, Ciwei Gao3, Yinghao Cao1

    Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070733 - 27 January 2026

    Abstract The construction of spot electricity markets plays a pivotal role in power system reforms, where market clearing systems profoundly influence market efficiency and security. Current clearing systems predominantly adopt a single-system architecture, with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models. Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems in contingency scenarios—a critical gap given redundant systems’ expanding applications in operational environments. This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability, demonstrated through an in-depth case… More >

  • Open Access

    ARTICLE

    An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

    Walid El-Shafai1,2, Noha A. El-Hag3, Ahmed Sedik4, Ghada Elbanby5, Fathi E. Abd El-Samie1, Naglaa F. Soliman6, Hussah Nasser AlEisa7,*, Mohammed E. Abdel Samea8

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2905-2925, 2023, DOI:10.32604/cmc.2023.031936 - 31 October 2022

    Abstract Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. More >

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