Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Table_1_Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning.DOCX by Gaoyan Zhang (450697)

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Functional profiling of GS height <i>preserved</i> modules with cumulative evidence for genomic risk effect on co-expression; PITT sample. by Eugenia Radulescu (319733)

Functional profiling of GS-SCZ <i>preserved</i> modules with cumulative evidence for genomic risk effect on co-expression- PITT sample. by Eugenia Radulescu (319733)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

Parameters in the neuron-astrocyte model and model training. by Lulu Gong (18707874)

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Learning performance. by Lulu Gong (18707874)

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Fig 3 - by Lulu Gong (18707874)

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Fig 1 - by Lulu Gong (18707874)

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Neuron-astrocyte network motifs and dynamic properties. by Lulu Gong (18707874)

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<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

Fig 1 - by Anant Shinde (3954662)

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The top 2 ROI pairs contributing towards the classification of multi-electrode stimulation. by Anant Shinde (3954662)

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VAS tolerability scores (min = 0, max = 10) of all sessions of all participants. by Anant Shinde (3954662)

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Fig 6 - by Anant Shinde (3954662)

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Accuracy across different ROIs for the classification shown by various models. by Anant Shinde (3954662)

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Data preprocessing flow-chart. by Anant Shinde (3954662)

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Time taken for data processing up to prediction, varying the number of ROIs used. by Anant Shinde (3954662)

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Accuracy of models to classify different stimulations. by Anant Shinde (3954662)

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ROC and MCC across different ROIs for the classification shown by various models. by Anant Shinde (3954662)

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