Integrating Scientific GUI Applications into Autoscaled Science Gateway Environments by Julien Chastang (640650)

Designing an online Introduction to Programming w/GenAI (EDUC90970_2025_S2) by Jean Lee (20590241)

Presentations of the Summer School - Satellite-Based Hydrological Data Assimilation by Maike Schumacher (8023076)

“…appeared very motivated In the first day, we learned about global hydrological modelling described by <a href="https://www.linkedin.com/in/henrik-madsen-a1841936/" target="_blank">Henrik Madsen</a> (DHI); a great introduction of satellite gravity and its application in hydrology and climate science was given by <a href="https://www.linkedin.com/in/guillaume-ramillien-3292b42a/" target="_blank">Guillaume Ramillien</a> Centre National de la Recherche Scientifique, CNRS); our third guest lecturer <a href="https://www.linkedin.com/in/anke-fluhrer-77b6862a3/" target="_blank">Anke Fluhrer</a> (German Aerospace Center, DLR) presented soil moisture remote sensing and excellent tools to retrieve the data for hydrological applications; <a href="https://www.linkedin.com/in/maike-schumacher-814307122/" target="_blank">Maike Schumacher</a> (Aalborg University, AAU) gave a presentation about physical data assimilation through the introduction of a simple hydrological model and connected it to distributed hydrological models. …”

Code and data for reproducing the results in the original paper of DML-Geo by Pengfei CHEN (8059976)

“…<p dir="ltr">This asset provides all the code and data for reproducing the results (figures and statistics) in the original paper of DML-Geo</p><h2>Main Files:</h2><p dir="ltr"><b>main.ipynb</b>: the main notebook to generate all the figures and data presented in the paper</p><p dir="ltr"><b>data_generator.py</b>: used for generating synthetic datasets to validate the performance of different models</p><p dir="ltr"><b>dml_models.py</b>: Contains implementations of different Double Machine Learning variants used in this study.…”

Efficient, Hierarchical, and Object-Oriented Electronic Structure Interfaces for Direct Nonadiabatic Dynamics Simulations by Sascha Mausenberger (22225772)

“…We present a novel, flexible framework for electronic structure interfaces designed for nonadiabatic dynamics simulations, implemented in Python 3 using concepts of object-oriented programming. …”

<b>AutoMated tool for Antimicrobial resistance Surveillance System version 3.1 (AMASS3.1)</b> by Chalida Rangsiwutisak (10501496)

“…;</li><li><i>Enterococcus</i> <i>faecalis</i> and <i>E. faecium</i> have been explicitly included in the pathogens under the survey (while <i>Enterococcus</i> spp. are used in the AMASS version 2.0);</li><li>We have added a few antibiotics in the list of antibiotics for a few pathogens under the survey;</li></ol><p dir="ltr">Technical aspects</p><ol><li>We have added a configuration for “Annex C: Cluster signals” in Configuration.xlsx;</li><li>We have improved the algorithm to support more several date formats;</li><li>We have improved the algorithm to translate data files;</li><li>We have improved Data_verification_logfile report to present local languages of the variable names and values (according to how they were recorded in the data files) in the report;</li><li>We have improved Annex B: Data indicators to support a larger data set;</li><li>We have used only Python rather than R + Python (as used in the AMASSv2.0);</li><li>We have set a default config for infection origin stratification by allowing a specimen collected two calendar days before the hospital admission date and one day after the hospital discharge date into consideration. …”

Predicting coding regions on unassembled reads, how hard can it be? - Genome Informatics 2024 by Amanda Clare (98717)

“…Predictions are made on the set of reads, using several prediction tools. The locations and directions of the predictions on the reads are then combined with the information about locations and directions of the reads on the genome using Python code to produce detailed results regarding the correct, incorrect and alternative starts and stops with respect to the genome-level annotation.…”

Graphical abstract of HCAP. by Mohanad Faeq Ali (21354273)

“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”

Recall analysis. by Mohanad Faeq Ali (21354273)

“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”

Convergence rate analysis. by Mohanad Faeq Ali (21354273)

“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”

Computational efficiency. by Mohanad Faeq Ali (21354273)

“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”

Analysis of IoMT data sources. by Mohanad Faeq Ali (21354273)

“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”

Prediction accuracy on varying attack types. by Mohanad Faeq Ali (21354273)

“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”

<b> </b> Precision analysis. by Mohanad Faeq Ali (21354273)

“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”

Impact of cyberattack types on IoMT devices. by Mohanad Faeq Ali (21354273)

“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”

DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF by Shivani V. Pawar (20355171)

“…</p>Methods<p>Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. …”

DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF by Shivani V. Pawar (20355171)

“…</p>Methods<p>Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. …”

Error reduction over time by the HOFA-SMC. by Asra Sarwat (22453794)

Code and data for evaluating oil spill amount from text-form incident information by Yiming Liu (18823387)

“…The code is written in Python and operated using Jupyter Lab and Anaconda. …”

Workflow of T cell activation analysis. by Yitong Ma (8144697)