RUBIO PROAÑO, Andrès

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RUBIO PROAÑO, Andrès
Official Name
RUBIO PROAÑO, Andrès
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57212081622

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    Design of a backup system powered by renewable energy sources for the operation of a textile industry in Quito
    (2025)
    Segura, Joel 
    ;
    RUBIO PROAÑO, Andrès  
    ;
    Chapi, David  
    This research proposes the design of an energy backup system using renewable energy sources to ensure the continuity of electrical service in a textile industry operating under a 24-hour work regime. Using various engineering methodologies such as energy load surveys and efficiency indicators, photovoltaic solar panels were selected as the optimal renewable energy source. The results show improved energy efficiency and economic benefits, with surplus energy being sold to the national grid, reducing production costs. This work contributes to the company’s sustainability and environmental goals.
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    ROIX-Comp: Optimizing X-ray Computed Tomography Imaging Strategy for Data Reduction and Reconstruction
    (2026)
    Amarjit Singh
    ;
    Kento Sato
    ;
    Kohei Yoshida
    ;
    Kentaro Uesugi
    ;
    Yasumasa Joti
    In high-performance computing (HPC) environments, particularly in synchrotron radiation facilities, vast amounts of X-ray images are generated. Processing large-scale X-ray Computed Tomography (X-CT) datasets presents significant computational and storage challenges due to their high dimensionality and data volume. Traditional approaches often require extensive storage capacity and high transmission bandwidth, limiting real-time processing capabilities and workflow efficiency. To address these constraints, we introduce a region-of-interest (ROI)-driven extraction framework (ROIX-Comp) that intelligently compresses X-CT data by identifying and retaining only essential features. Our work reduces data volume while preserving critical information for downstream processing tasks. At pre-processing stage, we utilize error-bounded quantization to reduce the amount of data to be processed and therefore improve computational efficencies. At the compression stage, our methodology combines object extraction with multiple state-of-the-art lossless and lossy compressors, resulting in significantly improved compression ratios. We evaluated this framework against seven X-CT datasets and observed a relative compression ratio improvement of 12.34× compared to the standard compression. © 2026 Copyright held by the owner/author(s).
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