Overview of mdciao’s API submodule and their functionalities. by Guillermo Pérez-Hernández (21156182)

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Fig 4. - mdciao: Accessible Analysis and Visualization of Molecular Dynamics Simulation Data by Guillermo Pérez-Hernández (21156182)

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Overview of the MD datasets used in S2 Fig. by Guillermo Pérez-Hernández (21156182)

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Things to do after installing mdciao. by Guillermo Pérez-Hernández (21156182)

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Distribution of closest heavy-atom—heavy-atom distances over five different MD datasets (S1 Table). by Guillermo Pérez-Hernández (21156182)

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Complete text output printed to the terminal by the CLT (shown in Fig 2A) of the main text. by Guillermo Pérez-Hernández (21156182)

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Example Jupyter notebook illustrating how mdciao can be used in API mode. by Guillermo Pérez-Hernández (21156182)

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Table 1 - mdciao: Accessible Analysis and Visualization of Molecular Dynamics Simulation Data by Guillermo Pérez-Hernández (21156182)

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Longer-range peaks of S2 Fig (using arrows and the *, ⁑, †, ‡, and ⸸ symbols) not corresponding to short-range contacts, but rather secondary structure elements like beta-sheets or... by Guillermo Pérez-Hernández (21156182)

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Example Jupyter notebook illustrating how mdciao is used in API mode. by Guillermo Pérez-Hernández (21156182)

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Overview of one by Guillermo Pérez-Hernández (21156182)

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PyGMT – Accessing and Integrating GMT with Python and the Scientific Python Ecosystem (AGU24, U12B-05) by Yvonne Fröhlich (20442683)

“…</p><p dir="ltr">PyGMT (<a href="https://www.pygmt.org/" target="_blank">https://www.pygmt.org/</a>) wraps around the very fast GMT C code to make it accessible through the Python programming language. …”

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”

Table 1_Entropy-adaptive differential privacy federated learning for student performance prediction and privacy protection: a case study in Python programming.docx by Shanwei Chen (12031760)

“…This study proposes an Entropy-Adaptive Differential Privacy Federated Learning method (EADP-FedAvg) to enhance the accuracy of student performance prediction while ensuring data privacy. Based on online test records from Python programming courses for Electronic Engineering students (grade 2021–2023) at the School of Physics and Optoelectronic Technology, Baoji University of Arts and Sciences, China, the study uses a Multilayer Perceptron (MLP) model and 10 distributed clients for training. …”

Optimal radiomics feature of MRI. by Jiangfa Li (18345033)

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Optimal radiomics feature of CT. by Jiangfa Li (18345033)

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Extracted CT and MRI images and outlined ROI. by Jiangfa Li (18345033)

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Statistically relevant information. by Jiangfa Li (18345033)

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Cross-validation of Lasso regression of enhanced MRI images. by Jiangfa Li (18345033)

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