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)

Subjects:

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)

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

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

Subjects:

Application of the smooth-index to bandwidth-limited matrices (A,B) and to block-diagonal matrices (C,D). by Alexander Borst (32619)

Subjects:

List of recurrent connections from the single column connectome of Drosophila. by Alexander Borst (32619)

Subjects:

Steps of data preprocessing. by Bernhard Vennemann (7434353)

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

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

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

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

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:

Task structure diagram. by Honglan Huang (341951)

Subjects: “…Cell Biology…”

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

Subjects: “…Cell Biology…”

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

Subjects:

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…”

Convergence curves for multimodal function test. by Qinghe Guo (21519322)