This appendix presents the flowchart used in the development of the particle swarm optimization algorithm for determining the optimal PID controller gain values. by Rafael Mendes Faria (18886798)

The performance results on benchmark test functions and real-world problems. by Mahmood A. Jumaah (22272371)

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libEnsemble: New capabilities for coordinating dynamic ensembles of calculations by John-Luke Navarro (9024008)

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finit... by Hassan Mehboob (8960273)

State Preparation in Quantum Algorithms for Fragment-Based Quantum Chemistry by Ruhee D’Cunha (8921372)

“…The localized active space–unitary coupled cluster (LAS–UCC) algorithm iteratively loads a fragment-based multireference wave function onto a quantum computer. …”

Illustration of the four steps in constructing the point feature binary mask. by Luka Maisuradze (8791229)

ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…In order to overcome such shortcoming, we develop a generalized algorithm, “ADT” to generate the nonadiabatic equations through symbolic manipulation and to construct highly accurate diabatic surfaces for molecular processes involving excited electronic states. …”

Wavelet transform and scaling function. by Menaa Nawaz (14304920)

Summary of steps in iterative point set registration for scRNA-seq data. by Amir Alavi (9569337)

“…The number of source and/or target cells matched can vary for different matching strategies. 3) Based on the selected pairs, a global transformation function is learned so that source cells in <i>A</i> become closer to their paired cell in <i>B</i>. 4) The learned transformation is next applied to all points in <i>A</i>. 5) This process (steps 2–4) is repeated, iteratively aligning set <i>A</i> onto <i>B</i> until the mean distance between the assigned pairs of cells no longer improves. 6) The final global transformation function is the composition of the functions learned in each iteration at step 3.…”

Penalty functions used in different methods. by Souvik Seal (12437166)

Algorithm parameters. by Ruyi Dong (9038174)

“…In addition, to verify the performance and robustness of LLSKSO, comparison experiments between LLSKSO and 10 well-known algorithms are conducted on 50 benchmark test functions. …”

Algorithm of the brightness scale calibration experiment. by Krzysztof Petelczyc (3954203)

“…The “level” denotes the number of perceptually equal units of brightness, while the scale is an array storing brightness vs. luminous intensity function values.</p>…”

Stepping motor driving circuit. by Shaohua Fu (10573209)

wIRBMO feature selection algorithm improving rate. by Chenyi Zhu (9383370)

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Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

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Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

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Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

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Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

Summary of classical and CEC-based benchmark test functions used in this study. by Mahmood A. Jumaah (22272371)

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PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)