Optimization flow of the co-simulation method. by Shangyang Jin (21772466)

“…This strategy combines the design of experiments (DOE), genetic algorithm (GA), and NLPQL algorithm, which is referred to as the DGN method. …”

The TL of the perforated membrane metamaterial. by Shangyang Jin (21772466)

“…This strategy combines the design of experiments (DOE), genetic algorithm (GA), and NLPQL algorithm, which is referred to as the DGN method. …”

The process of the optimization (MIGA). by Shangyang Jin (21772466)

“…This strategy combines the design of experiments (DOE), genetic algorithm (GA), and NLPQL algorithm, which is referred to as the DGN method. …”

The effect of each parameter on the peak value. by Shangyang Jin (21772466)

“…This strategy combines the design of experiments (DOE), genetic algorithm (GA), and NLPQL algorithm, which is referred to as the DGN method. …”

Comparative performance evaluation of 3Mont with other feature selection algorithms over 10 iterations. by Miray Unlu Yazici (14792680)

Metapopulation model notation. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Estimates of for each problem instance. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Approximation factors for each problem instance. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Example of how the Resnet34UNet model functions. by Henrik Mustonen (20378529)

The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…In particular, we examine Autoregressive AR(1) vector autoregressive processes, which are commonly found in time-series applications. …”

Attack and defense behaviors with the sensory–motor algorithm between multiple predators and prey. by Hiroyuki Ichijo (13891153)

Flowchart of simple ant colony algorithm. by Yang Yu (4292)

“…Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. Utilizing this model, an enhanced Risk-Time Ant Colony Optimization (RT-ACO) routing algorithm is proposed, which builds upon the traditional ant colony algorithm. …”

Greedy Man Optimization Algorithm (GMOA) by Hamed Nozari (20601020)

“…GMOA draws inspiration from human behavior, specifically the metaphor of individuals who tenaciously hold on to their positions, modeled as resistant solutions influenced by metaphorical parasites (MMOs). The algorithm introduces two unique mechanisms: MMO resistance, which prevents premature replacement of solutions, ensuring stability and diversity, and periodic parasite removal, which promotes mutation and prevents stagnation. …”

What constitutes an obstructive ventilatory impairment in a pediatric population? A comparative study of five definitions by scholarly societies by Mariem Abdesalem (22677712)

Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

“…<b>(B)</b> The priority of the top 10 genes was evaluated through MNC, which identifies clusters of protein nodes that are more functionally connected to each other and selects the central proteins within the cluster. …”

Using synthetic data to test group-searching algorithms in a context where the correct grouping of species is known and uniquely defined. by Yuanchen Zhao (12905580)

“…(C) We use the synthetic data as input for three families of regression-based algorithms: the EQO of Ref. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.ref026" target="_blank">26</a>] (which groups species into two groups), and two families we call K-means and Metropolis (see text), which can return any specified number of groups. …”

Results of the comparative experiment. by Jia Li (160557)

“…Furthermore, the Focal_EIOU loss function, which balances high- and low-quality anchors to increase detection and localization accuracy, replaces the CIOU loss function. …”

Comparison of attention mechanisms. by Jia Li (160557)

“…Furthermore, the Focal_EIOU loss function, which balances high- and low-quality anchors to increase detection and localization accuracy, replaces the CIOU loss function. …”

Results of ablation experiments. by Jia Li (160557)

“…Furthermore, the Focal_EIOU loss function, which balances high- and low-quality anchors to increase detection and localization accuracy, replaces the CIOU loss function. …”

Comparison of attention mechanisms. by Jia Li (160557)

“…Furthermore, the Focal_EIOU loss function, which balances high- and low-quality anchors to increase detection and localization accuracy, replaces the CIOU loss function. …”