The Simulation and optimization process of pipe diameter selection. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Optional pipe diameter and unit price of NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Optional pipe diameter and unit price of HN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The topology of HN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Information of nodes and pipes of HN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Ranking distribution and average ranking on 23 benchmark functions. by Yuqi Xiong (12343771)

Subjects:

Results of DE algorithms with our scheme and original DE algorithms when function dimensionality is set 100. by Haiming Du (6691646)

Results of DE algorithms with our scheme and original DE algorithms when function dimensionality is set 30. by Haiming Du (6691646)

Results of DE algorithms with our scheme and original DE algorithms when function dimensionality is set 50. by Haiming Du (6691646)

The image of the Aekley function. by Zhengyu Xu (8550660)

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics by Paolo Cifani (1575613)

“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics by Paolo Cifani (1575613)

“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”

Ranking distribution and average ranking on CEC2022 benchmark functions. by Yuqi Xiong (12343771)

Subjects:

The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”

The IALO algorithm solution flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”

Comparison of UACI among different algorithms. by Feixian Liu (22477943)

“…Based on the 2D-SQSM, this paper further designs a highly robust color image encryption algorithm. First, by introducing different hash functions multiple times, the correlation between the key and plaintext is enhanced, significantly improving resistance against brute-force attacks; second, cyclic shifting and segmentation-recombination operations are applied separately to the three RGB channels to effectively disrupt pixel distribution and significantly reduce spatial correlation between pixels; finally, the chaotic sequence generated by the 2D-SQSM is utilized for XOR diffusion, further enhancing the randomness and diffusion capability of the ciphertext. …”

Comparison of NPCR among different algorithms. by Feixian Liu (22477943)

“…Based on the 2D-SQSM, this paper further designs a highly robust color image encryption algorithm. First, by introducing different hash functions multiple times, the correlation between the key and plaintext is enhanced, significantly improving resistance against brute-force attacks; second, cyclic shifting and segmentation-recombination operations are applied separately to the three RGB channels to effectively disrupt pixel distribution and significantly reduce spatial correlation between pixels; finally, the chaotic sequence generated by the 2D-SQSM is utilized for XOR diffusion, further enhancing the randomness and diffusion capability of the ciphertext. …”

Values of the objective function and its non-normalized terms , obtained by using different weights in the optimization process. by Roberto Castro-Medina (21484990)

Algorithm of the main experiment targeted to measure the perceptual point spread function (pPSF) treating patients visual system including its optics, physiology and psychology as an integrated imaging system, and patient’s perceptions as its output signal. by Krzysztof Petelczyc (3954203)

“…<p>In the algorithm, the following variables were used: “Ic” denotes the intensity of the central diode (Ic = 40 cd); “DIST(i)” is a randomly sorted list of “D” angular stimuli positions distributed equally as a function of distance from 0.24° to 7.67° from the central point (D = 10), while “i” is an index corresponding to the current distance of a probe diode (“d”); “N” denotes the number of trials for each stimuli position (N = 20); “s” denotes the perceptual brightness value transformed to diode luminous intensity by an array “I(s)” corresponds to the table “scale (level)” determined by the algorithm presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0306331#pone.0306331.g003" target="_blank">Fig 3</a>; “cnt” is a counter of trials for the current probe diode’s distance, array threshold (d), and slope (d), i.e., it denotes the intensity of the single point of the pPSF and its uncertainty. …”

Benchmarking functions. by Guilin Yang (583364)

“…Introducing nonlinear convergence factors based on positive cut functions to changing the convergence of algorithms, the early survey capabilities and later development capabilities of the algorithm are balanced. …”