Multi-Version PYZ Builder Script: A Universal Python Module Creation Tool by Pavel Izosimov (20096259)

“…</li></ul><p dir="ltr"><b>Application Areas</b></p><p dir="ltr">The <b>Multi-Version PYZ Builder Script</b> can be effectively applied in various areas:</p><ul><li><b>Commercial Distribution</b>: Distribute protected Python code to clients or customers, implementing advanced <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank"><b>source code protection</b></a> for sales or rentals.…”

System Hardware ID Generator Script: A Cross-Platform Hardware Identification Tool by Pavel Izosimov (20096259)

“…System Hardware ID Generator Script: Unique Device Identification for Enhanced Security and Licensing<p dir="ltr">The <b>System Hardware ID Generator Script</b> is a powerful Python tool developed to generate a unique Hardware ID (HWID) for the device it runs on. …”

Python sript. by Kan Jia (6438599)

Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , w... by Ziyan Jin (21021976)

Subjects:

PYTHON by Kan Jia (21666161)

Representative examples of scanned intake forms. by Yojiro Umezaki (5916593)

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

“…</li><li><b>Commercial Distribution</b>: Safely share Python code with clients or customers, implementing advanced <b>Python code protection tools</b> for sales or rentals.…”

A Representative example of organ surface data before and after editing. by Takashi Kimura (178058)

BSTPP: a python package for Bayesian spatiotemporal point processes by Isaac Manring (20705955)

“…<p>Spatiotemporal point process models have a rich history of effectively modeling event data in space and time. However, they are sometimes neglected due to the difficulty of implementing them. …”

Data features examined for potential biases. by Harry Hochheiser (3413396)

“…<div><p><b>Objective:</b> To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.…”

Analysis topics. by Harry Hochheiser (3413396)

“…<div><p><b>Objective:</b> To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.…”

(a) System dynamics: schematic representation of all the reactions in the Tree model, which are aging, reproduction, color cycle, and competition. by Fabricio Cravo (21387854)

Simulation results for the phage communication circuit from Pathania <i>et al</i>. [5]. by Fabricio Cravo (21387854)

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

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

Subjects:

Correlation (a) and Mean-Absolute-Error (b) between frequencies at a reference cutoff, δ₀, and different frequencies, , at different cutoff-values, δ_scan ∊ [3... by Guillermo Pérez-Hernández (21156182)

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

“…However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. …”

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. …”

Exploring the integration of metaverse technologies in engineering education through the SAMR model by Snezhana Dineva (22444471)

“…It demonstrates the successful implementation of the model in practice and provides examples of effective practices in the context of the CAVE (Cave Automatic Virtual Environment) metaverse. …”

Fitting a feast/famine cycle to a glycolysis model. by Paul van Lent (16876977)