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Showing 41 - 60 results of 2,997 for search '(( algorithm fc function ) OR ((( algorithm both function ) OR ( algorithm python function ))))', query time: 1.07s Refine Results
  1. 41
    Average quality scores of the models of the analysis set as a function of the <i>w</i><sub><i>SP</i></sub> with which the DOPE potential has been included in the objective function of MODELLER.

    Average quality scores of the models of the analysis set as a function of the <i>w</i><sub><i>SP</i></sub> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

    Published 2019
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  2. 42
    Reward function related parameters.

    Reward function related parameters. by Honglei Pang (22693724)

    Published 2025
    Subjects:
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  3. 43
    Principal component analysis applied to both datasets.

    Principal component analysis applied to both datasets. by Anny K. G. Rodrigues (11689849)

    Published 2021
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  4. 44
    Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration.

    Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration. by Raul Castañeda (13932971)

    Published 2022
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  5. 45
    Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength.

    Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. by María Victoria Waks-Serra (21578611)

    Published 2025
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  6. 46
    3D modeling qualities of the AS single-template models built by including DOPE in the objective function of MODELLER.

    3D modeling qualities of the AS single-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

    Published 2019
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  7. 47
    3D modeling qualities of the AM multiple-template models built by including DOPE in the objective function of MODELLER.

    3D modeling qualities of the AM multiple-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

    Published 2019
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  8. 48
    Information of nodes and pipes of NYN.

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

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  9. 49
    The topology of the NYN.

    The topology of the NYN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  10. 50
    The result of Wilcoxon signed-rand test.

    The result of Wilcoxon signed-rand test. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  11. 51
    The Simulation and optimization process of pipe diameter selection.

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

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  12. 52
    Optional pipe diameter and unit price of NYN.

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

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  13. 53
    Optional pipe diameter and unit price of HN.

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

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  14. 54
    The topology of HN.

    The topology of HN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  15. 55
    Information of nodes and pipes of HN.

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

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  16. 56
    Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic

    Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic by NELSON L. DIAS (8078003)

    Published 2019
    “…With these practical rules, simple summation algorithms provide sufficiently robust results for the calculation of the density function and its definite integrals. …”
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  17. 57
    Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm.

    Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm. by Laurentius O. Osapoetra (9931759)

    Published 2020
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  18. 58
    Venn diagrams showing overlap of the total of overrepresented/underrepresented kinases for both cohorts.

    Venn diagrams showing overlap of the total of overrepresented/underrepresented kinases for both cohorts. by Erica A. K. DePasquale (11851483)

    Published 2021
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  19. 59
    The image of the Aekley function.

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

    Published 2025
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  20. 60
    Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides

    Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides by Bo-Siyuan Jian (14669996)

    Published 2023
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