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    Dynamic Surgical Prioritization: A Machine Learning and XAI-Based Strategy by Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan, Muhammad Ehsan Rana, Jimmy H. Gutiérrez-Bahamondes

    Published 2025-02-01
    “…Specifically, we employ the Light Gradient Boosting Machine (LightGBM) for predictive modeling, stochastic simulations to account for dynamic variables and competitive interactions, and SHapley Additive Explanations (SHAPs) to interpret model outputs at both the global and patient-specific levels. …”
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    Impact of Wind Pressure Coefficients on the Natural Ventilation Effectiveness of Buildings through Simulations by Nayara Rodrigues Marques Sakiyama, Joyce Correna Carlo, Felipe Isamu Harger Sakiyama, Nadir Abdessemed, Jürgen Frick, Harald Garrecht

    Published 2024-09-01
    “…For that, an experimental house and a measurement period were used to develop and calibrate the initial BES model. Four <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>C</mi></mrow><mrow><mi>p</mi></mrow></msub></mrow></semantics></math></inline-formula> sources are considered: an analytical model from the BES software (i), surface-averaged <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>C</mi></mrow><mrow><mi>p</mi></mrow></msub></mrow></semantics></math></inline-formula> values for building windows that were calculated with Computational Fluid Dynamics (CFD) simulations using OpenFOAM through a cloud-based platform (ii<sub>a,b,c</sub>), and two databases—AIVC (iii) and Tokyo Polytechnic University (TPU) (iv). …”
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