Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen... by Ross J. Burton (10941910)

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

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FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz by Faizan Ali (21689861)

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Reward function related parameters. by Honglei Pang (22693724)

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Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”

Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

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Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

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Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

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Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

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

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). …”

The Blood–Brain Barrier (BBB) Score by Mayuri Gupta (1886839)

“…Understanding the BBB interaction with drug molecules based on physicochemical property space can guide effective and efficient drug design. An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …”

The Blood–Brain Barrier (BBB) Score by Mayuri Gupta (1886839)

“…Understanding the BBB interaction with drug molecules based on physicochemical property space can guide effective and efficient drug design. An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …”

The Blood–Brain Barrier (BBB) Score by Mayuri Gupta (1886839)

“…Understanding the BBB interaction with drug molecules based on physicochemical property space can guide effective and efficient drug design. An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …”

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

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Study Design. by Mai Shi (756927)

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Performance as a function of the number of algorithm executions for the full-sized matrix design. by Jörg Felder (5848541)

“…<p>Performance as a function of the number of algorithm executions for the full-sized matrix design.…”

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)