Python file of the computational framework. by Benoit Haut (9753029)

Secure Python Code Manager: A Tool for Protected Python Code Distribution and Management by Pavel Izosimov (20096259)

“…A license key is generated, allowing authorized users to access the code.bash<pre><pre>python secure_python_code_manager.py --upload -f your_script.py<br></pre></pre></li><li><b>Updating Previously Uploaded Code</b>: With the <code>--update</code> function, update your code in the cloud without needing to redistribute new files to clients, ensuring seamless code maintenance and updates.bash<pre><pre>python secure_python_code_manager.py --update -f your_script.py -l your_license_key<br></pre></pre></li><li><b>Retrieving License Information</b>: The <code>--license-info</code> function lets you retrieve detailed information about your licenses, including status, usage data, and limits.bash<pre><pre>python secure_python_code_manager.py --license-info -l your_license_key<br></pre></pre></li><li><b>Service Usage Monitoring</b>: Use the <code>--service-usage</code> function to monitor your service usage, including uploaded scripts and associated licenses, helping you keep track of your code deployment.bash<pre><pre>python secure_python_code_manager.py --service-usage<br></pre></pre></li></ol><h2>Use Cases</h2><ul><li><b>Protect Python Code</b>: If you're looking to <b>protect Python code</b> from unauthorized use, this tool provides robust protection mechanisms.…”

Comparison of performance between our next reaction implementation and the Python library from Ref. [3]. by Samuel Cure (22250922)

“…<p>We simulate SIR epidemic processes on Watts-Strogatz networks with parameters <i>k</i>₀ = 5, <i>p</i> = 0.1 <b>(a)</b> and Barabási-Albert networks with parameter <i>m</i> = 5 <b>(b)</b> using the Python wrapper of our C++ implementation and compare its performance with the Python library from Ref. …”

Left schematic of the XOR gate implementation [11]. by Fabricio Cravo (21387854)

Python implementation of a wildfire propagation example using m:n-CAk over Z and R. by Pau Fonseca i Casas (9507338)

An example Python input file for the CA4 binder design. by Jakub Lála (17136205)

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

Subjects:

Example change statistic implementations. by Alex Stivala (8356257)

“…ALAAMEE implements both the stochastic approximation and equilibrium expectation (EE) algorithms for ALAAM parameter estimation, including estimation from snowball sampled network data. …”

Simple implementation examples of agent AI on free energy calculation and phase-field simulation by Toshiyuki Koyama (22828581)

“…The key points are summarized as follows. (1) Using Gibbs energy calculations as an example, we demonstrated a simple method for constructing agent AI, including explanation and distribution of the python code. (2) We applied this method to a scratch code development of phase-field simulation, and the template python code was also distributed. (3) Using the simple agent AI technique, we were able to easily verify that the Jarzynski equality is applicable for the diffusion behavior in diffusion couple, which provides a new approach for directly evaluating free energy change from diffusion flux information.…”

PYTHON by Kan Jia (21666161)

Reproducible implementation for article: Reproducibility study of the modular and reusable model of epithelial transport in the proximal convoluted tubule by Leyla Noroozbabaee (14301173)

Python sript. by Kan Jia (6438599)

Overview of the implemented simulation tool and training framework in Diffrax. by Paul van Lent (16876977)

Parameter estimates and estimated standard errors from the EE algorithm. by Alex Stivala (8356257)

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

“…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 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 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 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 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 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. …”