The flowchart of Algorithm 2. حسب Jing Xu (15337)

"…For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. …"

Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. حسب Tristan Mary-Huard (3864)

Train stopping plan. حسب Jing Xu (15337)

"…For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. …"

Major notations. حسب Jing Xu (15337)

"…For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. …"

S1 File - حسب Jing Xu (15337)

"…For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. …"

Table_1_Probabilistic Optimal Power Flow Calculation Method Based on Adaptive Diffusion Kernel Density Estimation.docx حسب Guoqing Li (23674)

"…Second, the Kendall rank correlation coefficient and the least Euclidean distance are used as correlation measure and index of fitting to select the optimal Copula function, and the joint probability distribution model of PV output and load is constructed. …"

ANFIS MODELING IN PROJECTION WELDING OF NUTS TO SHEETS حسب bircan albak (13647625)

"…<p>  </p> <p>Projection welding Adaptive neuro-fuzzy inference system, Genetic algorithm.ojection welding Adaptive neuro-fuzzy inference system, Genetic algorithm. this study, the maximum weld strength in projection welding of nuts to sheets (DD13 sheet metal part and AISI 1010 nut) was investigated using optimum input parameters (weld current, weld time and hold time) in order not to damage weld joint during the car assembly. …"

Table4_Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.xlsx حسب Melina Prado (19931685)

"…To test the trials’ optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. …"

Table3_Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.xlsx حسب Melina Prado (19931685)

"…To test the trials’ optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. …"

Image2_Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.jpeg حسب Melina Prado (19931685)

"…To test the trials’ optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. …"

Table1_Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.xlsx حسب Melina Prado (19931685)

"…To test the trials’ optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. …"

Image1_Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.jpeg حسب Melina Prado (19931685)

"…To test the trials’ optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. …"

Table2_Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.xlsx حسب Melina Prado (19931685)

"…To test the trials’ optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. …"

DataSheet1_Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo.docx حسب Kaixian Yu (2836709)

"…A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. …"

DataSheet_1_Identification and validation of a novel CD8+ T cell-associated prognostic model based on ferroptosis in acute myeloid leukemia.docx حسب Ge Jiang (2792095)

"…GO, KEGG analysis and PPI network were conducted based on these 46 DEGs. By jointly using LASSO algorithm and Cox univariate regression, we generated a 6-gene prognostic signature comprising VEGFA, KLHL24, ATG3, EIF2AK4, IDH1 and HSPB1. …"

DataSheet_2_Identification and validation of a novel CD8+ T cell-associated prognostic model based on ferroptosis in acute myeloid leukemia.zip حسب Ge Jiang (2792095)

"…GO, KEGG analysis and PPI network were conducted based on these 46 DEGs. By jointly using LASSO algorithm and Cox univariate regression, we generated a 6-gene prognostic signature comprising VEGFA, KLHL24, ATG3, EIF2AK4, IDH1 and HSPB1. …"

Image_2_Identification and validation of a novel CD8+ T cell-associated prognostic model based on ferroptosis in acute myeloid leukemia.tif حسب Ge Jiang (2792095)

"…GO, KEGG analysis and PPI network were conducted based on these 46 DEGs. By jointly using LASSO algorithm and Cox univariate regression, we generated a 6-gene prognostic signature comprising VEGFA, KLHL24, ATG3, EIF2AK4, IDH1 and HSPB1. …"

Image_3_Identification and validation of a novel CD8+ T cell-associated prognostic model based on ferroptosis in acute myeloid leukemia.tif حسب Ge Jiang (2792095)

"…GO, KEGG analysis and PPI network were conducted based on these 46 DEGs. By jointly using LASSO algorithm and Cox univariate regression, we generated a 6-gene prognostic signature comprising VEGFA, KLHL24, ATG3, EIF2AK4, IDH1 and HSPB1. …"

Image_1_Identification and validation of a novel CD8+ T cell-associated prognostic model based on ferroptosis in acute myeloid leukemia.tif حسب Ge Jiang (2792095)

"…GO, KEGG analysis and PPI network were conducted based on these 46 DEGs. By jointly using LASSO algorithm and Cox univariate regression, we generated a 6-gene prognostic signature comprising VEGFA, KLHL24, ATG3, EIF2AK4, IDH1 and HSPB1. …"

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles حسب Soham Savarkar (21811825)

"…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…"