Results of normal and wide step width (cm). by Fateme Khorramroo (18086501)

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Picture representing real-time visual feedback for wide step width. by Fateme Khorramroo (18086501)

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Step and trend changes in the percentage of unmet needs for healthcare during and after the COVID-19 lockdown, compared to the pre-lockdown period (n = 259,501). by Rodrigo Vargas-Fernández (10164344)

Step and trend changes in the percentage of people reporting a health problem during and after the COVID-19 lockdown, compared to the pre-lockdown period (n = 259,501). by Rodrigo Vargas-Fernández (10164344)

AI KRegression by CHIN-CHENG CHU (20718575)

Raw data 16–20. by Fateme Khorramroo (18086501)

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Demographics, SD= Standard Deviation. by Fateme Khorramroo (18086501)

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Raw data 6–9 and 15. by Fateme Khorramroo (18086501)

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Results of coordination variability during running. by Fateme Khorramroo (18086501)

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Raw data 1–5. by Fateme Khorramroo (18086501)

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Results of coordination variability during walking. by Fateme Khorramroo (18086501)

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Raw data 10–14. by Fateme Khorramroo (18086501)

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a; The connection between the phase angle in each quadrant and the relative movements of ankle inversion/eversion and knee internal/external rotation is outlined as follows: A vect... by Fateme Khorramroo (18086501)

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Coordination angle during running. by Fateme Khorramroo (18086501)

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Gait retraining with biofeedback. by Fateme Khorramroo (18086501)

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Coordination angle during walking. by Fateme Khorramroo (18086501)

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The correlation between the phase angle in each quadrant and the relative motion of distal and proximal joints is as follows: A coupling angle of 0° or 180° signifies movement in t... by Fateme Khorramroo (18086501)

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A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. This research proposes a new RNN architecture that decreases the prediction error up to 50% for cargo vessels when compared to the OU model. …”

Physics-Assisted Machine Learning for the Simulation of the Slurry Drying in the Manufacturing Process of Battery Electrodes: A Hybrid Time-Dependent VGG16-DEM Model by Diego E. Galvez-Aranda (9436672)

“…The hybrid approach consists in performing a certain amount of DEM simulation steps, <i>n</i>_DEM, after every DL prediction, mitigating the risk of unphysical predictions, like overlapping particles. …”

Presents the bibliometric search results. by Yan She (8639796)