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

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Curve of step response signal of 6 algorithms. by Guangwei Liu (181992)

Working steps and operating algorithm of the developed GSMCS. by Abdallah Elshawadfy Elwakeel (19852263)

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

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Steps of data preprocessing. by Bernhard Vennemann (7434353)

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Objective function trend with static and dynamic step size. by Ludovica Adacher (11238189)

Multidomain, Automated Photopatterning of DNA-functionalized Hydrogels (MAPDH). by Moshe Rubanov (7289156)

“…The algorithm incorporates a wash step between each round of patterning. …”

Accuracy and Absolute true of multi-label therapeutic peptides prediction models using different classical ML and DL algorithms on the training set with 5-fold cross-validation. by Wenhui Yan (825510)

Swarm intelligence algorithms for multi-objective IMP: Step-by-step improvement by Francisco Muñoz (9455441)

“…Algorithms implemented on article "<b>A step-by-step solution for the multi-objective influence spread problem</b>". …”

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

Subjects:

Automated sampling and control system for FACS streamlines high-throughput cell sorting workflows. by Diane M. Wiener (1956583)

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Performance profiles of these algorithms on the basis of the number of function evaluations. by Xianfeng Ding (535904)

Flow chart of the analysis steps in this study. by Junjie Su (368807)

Subjects:

Task structure diagram. by Honglan Huang (341951)

Subjects: “…Cell Biology…”

Second experiment results of the trained model. by Honglan Huang (341951)

Subjects: “…Cell Biology…”

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.…”

Comparison of algorithms in two cases. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Flow of the NSGA-II algorithm. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The scores (objective function values) acquired when generating individual schedules for each of the patients using PPO trained agents with PK/PD and ML environments and 2 dose ste... by Tomasz Gutowski (17200042)