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

“…For a RDKit molecule, it provides (i) a PNG binary string with an image of the molecule with color-highlighted functional groups; (ii) a list of sets of atom indices (idx), each set corresponding to a functional group; (iii) a list of pseudo-SMILES canonicalized strings for the full functional groups; and (iv) a list of RDKit labeled mol objects, one for each full functional group. …”

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

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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). …”

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

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

“…<p>(A), (B) Algorithm performance, evaluated over 50 simulated datasets generated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.g001" target="_blank">Fig 1</a> with <i>N</i> = 3 true groups, 900 samples and 10% simulated measurement noise. …”

Benchmark functions. by Yun Qi (560401)

A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

“…We benchmark the strategy against one- and two-dimensional nearest-neighbor Heisenberg models, the one-dimensional next-nearest-neighbor Heisenberg model, and <i>selected</i> collinear ground and excited states of the nitrogenase P-cluster, employing intermediate CAS(48,40) active space ab initio Hamiltonians. …”

Type-1 membership function for distance. by Seung-Min Ryu (21463891)

“…<div><p>In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. …”

Type-1 membership function for speed. by Seung-Min Ryu (21463891)

“…<div><p>In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. …”

Function testing and iterative variation diagram. by Yun Qi (560401)

Observed distributions of ARI ×100 from the FRECL partition after consensus clustering; against the number of runs. by Susana Conde (5495675)

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Membership function of output for tank 1. by Tamiru Demelash Kassie (20678515)

Observed adjusted Rand index for one-replicate simulations (with AR(1), <i>ρ</i> = 0.5, <i>σ</i>² = 0.1) against the number of clusters <i>K</i> = 3, 6, 9, 12 (on the ho... by Susana Conde (5495675)

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Line plots with the ARI for the simulations with one replicate against the number of clusters for FRECL and the five alternative methods for simulated data sets. by Susana Conde (5495675)

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Line plots with the RI for the simulations with one replicate against the number of clusters for FRECL and the five alternative methods for simulated data sets. by Susana Conde (5495675)

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Line plots with the TPR for the simulations with one replicate against the number of clusters for FRECL and the five alternative methods for simulated data sets. by Susana Conde (5495675)

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Line plots with the TNR for the simulations with one replicate against the number of clusters for FRECL and the five alternative methods for simulated data sets. by Susana Conde (5495675)

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Membership function of error E1 for tank 1. by Tamiru Demelash Kassie (20678515)

Loss Function Comparison. by Hongwei Yue (574068)

Loss Function Comparison. by Hongwei Yue (574068)