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

الموضوعات: "…evolutionary genetic algorithm…"

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

الموضوعات: "…evolutionary genetic algorithm…"

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

الموضوعات: "…evolutionary genetic algorithm…"

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

الموضوعات: "…evolutionary genetic algorithm…"

The topology of HN. حسب Yi Tao (178829)

الموضوعات: "…evolutionary genetic algorithm…"

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

الموضوعات: "…evolutionary genetic algorithm…"

Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms. حسب Tehnan I. A. Mohamed (16846175)

RMSE results. حسب YueSheng Jiang (19267984)

"…Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Results of the Kherson Area Visual Assessment. حسب YueSheng Jiang (19267984)

"…Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Work flow chart. حسب YueSheng Jiang (19267984)

"…Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Experimental data. حسب YueSheng Jiang (19267984)

"…Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Results of the PY area visual assessment. حسب YueSheng Jiang (19267984)

"…Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages حسب Vicente Martí-Centelles (1422415)

"…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …"

CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages حسب Vicente Martí-Centelles (1422415)

"…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …"

CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages حسب Vicente Martí-Centelles (1422415)

"…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …"

Comparison of the improved LRM and RM results. حسب YueSheng Jiang (19267984)

Result charts for different window sizes. حسب YueSheng Jiang (19267984)

Schematic diagram of the TEAPS algorithm. حسب Yoshiaki Kariya (13767985)

Algorithm results based on FE simulated likelihood functions. حسب Nicolas Herzig (9230425)

"…<p>(A) Nodule depth estimation by the algorithm with the likelihood functions obtained by FEM simulation. …"

Distribution of objective function values solved by three algorithms. حسب Meilin Zhu (688698)