Reward function related parameters. by Honglei Pang (22693724)

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

Hyperparameter settings of the algorithm 1. by Jin Xu (31283)

“…Subsequently, a state space is selected, and a dense reward function is designed, considering the multivariable characteristics of the recirculating cooling water system. …”

Comparison of different algorithms. by Jiandong Qiu (20389944)

Route for bays29 output by ABSQL algorithm. by Jin Zhang (53297)

“…DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive <i>ε-beam</i> search strategy, and a new Q-value update formula. …”

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. …”

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.…”

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

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

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The <i>min</i> obtained by DSRABSQL, S2V-DQN and 2-opt-DRL algorithms for 25 instances. by Jin Zhang (53297)

Comparison of average reward values in various simulation scenarios. by Honglei Pang (22693724)

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<i>S</i>_<i>c</i> of ch150, rat575 and fl1400 for algorithms of each group. by Jin Zhang (53297)

<i>S</i>_<i>c</i> and rank of DSRABSQL and VDWOA algorithms in <i>G</i>_<i>1</i>. by Jin Zhang (53297)

<i>S</i>_<i>c</i> and rank of DSRABSQL and VDWOA algorithm in <i>G</i>_<i>2</i>. by Jin Zhang (53297)

The value of <i>N</i>_<i>min</i> for ch150, rat575 and fl1400 obtained by three algorithms. by Jin Zhang (53297)

<i>min</i>, <i>avg</i>, <i>max</i> and <i>S</i>_<i>td</i> for rat575 obtained by three algorithms. by Jin Zhang (53297)

<i>min</i>, <i>avg</i>, <i>max</i> and <i>S</i>_<i>td</i> of ch150 obtained by three algorithms. by Jin Zhang (53297)

<i>min</i>, <i>avg</i>, <i>max</i> and <i>S</i>_<i>td</i> of fl1400 obtained by three algorithms. by Jin Zhang (53297)

Blood glucose risk index function. by Phuwadol Viroonluecha (13783106)

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