Comparison of ramping contributions of various resources under different schemes. by Tianhe Sun (21145341)

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Principles of flexibility demand in different directions. by Tianhe Sun (21145341)

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Technological Fitness and Coherence explanation. Schematic representation of the workings of the Fitness and Complexity algorithm and Coherence diversification. by Matteo Straccamore (15083799)

Example gauge chart outputs produced by the M.E.D.V.I.S. algorithm. by Hector Torres (11708207)

Comparative Internal and External AUC Performance Across Single-Site Models. by Ioana Duta (18462981)

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Performance of incremental models. by Ioana Duta (18462981)

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Performance of LOSO models. by Ioana Duta (18462981)

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Dataset breakdown. by Ioana Duta (18462981)

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Data preparation. by Ioana Duta (18462981)

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Median and IQR of the model AUC values across each study site for internal and external validation. by Ioana Duta (18462981)

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Missingness in the raw data. by Ioana Duta (18462981)

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Variation of model weights between sites. by Ioana Duta (18462981)

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Effect of changing thresholds on accuracy. by Ioana Duta (18462981)

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Histogram showing propensity scores as calculated from the processed data, coloured by whether there was a diagnosis of epilepsy (blue) or not (red). by Ioana Duta (18462981)

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Covariate values per site. by Ioana Duta (18462981)

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<b>Data Availability</b> by Bonyad Ahmadi (20750327)

“…</p><p dir="ltr">Reproducibility Resources:</p><p dir="ltr">Python scripts for reproducing figures, preprocessing data, and training machine learning models (SVM, MLP, XGB, BRR, KRR).</p><p dir="ltr">python scripts documenting the implementation of the Mixture Density Network (MDN) algorithm, including hyperparameter tuning and uncertainty quantification.…”

Peak-Based Machine Learning for Plastic Type Classification in Time-of-Flight Secondary Ion Mass Spectrometry by Jin Gyeong Son (1544851)

“…In order to take into account the characteristics of the measurement data, the local maxima of the measurement data were first examined in a preprocessing step. Several machine learning methods were then implemented to create a model that could successfully classify the plastics. …”

Peak-Based Machine Learning for Plastic Type Classification in Time-of-Flight Secondary Ion Mass Spectrometry by Jin Gyeong Son (1544851)

“…In order to take into account the characteristics of the measurement data, the local maxima of the measurement data were first examined in a preprocessing step. Several machine learning methods were then implemented to create a model that could successfully classify the plastics. …”

Statistical data for the dataset. by Rizk M. Rizk-Allah (18625751)

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The proposed X-LSTM-EO model. by Rizk M. Rizk-Allah (18625751)

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