The comparison results of different algorithms on 23 benchmark functions with D=30. by Yu Li (183836)

The comparison results of different algorithms on CEC2017 functions with D=30. by Yu Li (183836)

The structure of the proposed algorithm. by Mingbao Yang (14008799)

Parameters of trained algorithms. by Swathi Anil (17382903)

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Predicted body weights (kg) as a function of time (age in weeks) obtained from MARS algorithm and Gompertz model. by Turgay Şengül (12801990)

“…<p>Predicted body weights (kg) as a function of time (age in weeks) obtained from MARS algorithm and Gompertz model.…”

Function test results. by Xiaowen Zhang (83669)

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Functions of the software. by Chen Cheng (76970)

Event time instants of heterogeneous with saturation function controller (29). by Linling Wang (679981)

Event time instants of heterogeneous with sign function controller (17). by Linling Wang (679981)

Comparison chart of computation time. by Manoj Kumar V. (18196645)

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Radius of gyration (RG) of the backbone atoms of the protein as a function of time. by L. F. S. Bonet (11502082)

Solvent-accessible surface area (SASA) of the protein as a function of time. by L. F. S. Bonet (11502082)

Performances of ImpAO, AO, SMA, MFO and ABC algorithms for minimization of different error-based cost functions. by Davut Izci (15927452)

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Iterative convergence curves of each algorithm for the CEC2019 benchmark functions. by Guangwei Liu (181992)

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Variation of function values by ADM with different tolerable waiting times. by Yuxuan Long (17720796)

Data Sheet 1_Understanding acceptance and resistance toward generative AI technologies: a multi-theoretical framework integrating functional, risk, and sociolegal factors.docx by Priyanka Shrivastava (472059)

“…Additionally, the manuscript now highlights demographic diversity, including variations in age, gender, and academic discipline, as relevant to AI adoption patterns. Ethical concerns, including algorithmic bias, data ownership, and the labor market impact of AI, are addressed to offer a more holistic understanding of resistance behavior. …”

Genetic algorithm flowchart. by Dingming Yang (8505990)

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Parameters of the test function. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

DMTD algorithm. by Yunhu Huang (21402795)

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Detailed information of benchmark functions. by Guangwei Liu (181992)

“…In Case 1, the GJO-GWO algorithm addressed eight complex benchmark functions. …”