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  1. 3081

    Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization by Andrés Escobedo-Gordillo, Jorge Brieva, Ernesto Moya-Albor

    Published 2025-07-01
    “…Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO<sub>2</sub> estimation.…”
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    YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

    Published 2025-07-01
    “…Experimental evaluations on the HIT-UAV dataset corroborate that YOLO-SRMX achieves an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>mAP</mi><mn>50</mn></msub></semantics></math></inline-formula> of 82.8%, representing a 7.81% improvement over the baseline YOLOv8s model; an F1 score of 80%, marking a 3.9% increase; and a substantial 65.3% reduction in computational cost (GFLOPs). …”
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  10. 3090

    Influence of Substrate Concentrations on the Performance of Fed-Batch and Perfusion Bioreactors: Insights from Mathematical Modelling by John J. Fitzpatrick, Fionn O'Leary, Ali Hill, James Daly, Fergal Lalor, Edmond P. Byrne

    Published 2025-05-01
    “…This study applies mathematical models to investigate the influence of substrate concentration in the media added (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>S</mi></mrow><mrow><mi>m</mi></mrow></msub></mrow></semantics></math></inline-formula>), operating substrate concentration in the bioreactor (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi></mrow></semantics></math></inline-formula>), and bioreaction time on the performance of both bioreactors. …”
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