Comparative discussion of TDWO. حسب Fengren Lin (22075465)

"…Thus, a resource allocation algorithm named Tasmanian devil whale optimization (TDWO) is introduced for secure video transmission in 5G networks. …"

Pseudocode of TDWO. حسب Fengren Lin (22075465)

"…Thus, a resource allocation algorithm named Tasmanian devil whale optimization (TDWO) is introduced for secure video transmission in 5G networks. …"

tP-Same.mp4 from Amorphous bicontinuous minimal surface models and the superior Gaussian curvature uniformity of Diamond, Primitive and Gyroid surfaces حسب M. Himmelmann (22743018)

"…It contains the following sections: 1 Deformation families of triply-periodic minimal surfaces; 2 Wooten-Winer-Weaire algorithm; 3 Amorphous diamond minimal surface; 4 Pinch-off effect; 5 Topology-stabilised minimisation scheme for minimal surface generation using the "Surface Evolver"; 6 Relaxation algorithm to enhance isotropy; 7 Minimal surfaces from parallel layers with catenoidal connections; 8 Heterogeneity of minimal surfaces from parallel layers with catenoidal connections; 9 Balance between the two labyrinths of the amorphous Diamond surfaces; 10 Use of a labelled Voronoi diagram in <i>pomelo</i> package for creating initial polyhedral interfaces between intertwined graphs; 11 Distance Function on the Flat Torus; 12 Weierstrass parameterisations of TPMS; 13 Discretization data for the Minimal Surfaces; 14 Gauss Curvature Data and distributions for the Minimal Surfaces; 15 Gauss curvature of minimal surfaces as function of the in-surface distance to the nearest flatpoint; 16 Further supplementary items;We provide a detailed list of ESM files at the end of the supporting information, but before the references. …"

tG-Larger.mp4 from Amorphous bicontinuous minimal surface models and the superior Gaussian curvature uniformity of Diamond, Primitive and Gyroid surfaces حسب M. Himmelmann (22743018)

"…It contains the following sections: 1 Deformation families of triply-periodic minimal surfaces; 2 Wooten-Winer-Weaire algorithm; 3 Amorphous diamond minimal surface; 4 Pinch-off effect; 5 Topology-stabilised minimisation scheme for minimal surface generation using the "Surface Evolver"; 6 Relaxation algorithm to enhance isotropy; 7 Minimal surfaces from parallel layers with catenoidal connections; 8 Heterogeneity of minimal surfaces from parallel layers with catenoidal connections; 9 Balance between the two labyrinths of the amorphous Diamond surfaces; 10 Use of a labelled Voronoi diagram in <i>pomelo</i> package for creating initial polyhedral interfaces between intertwined graphs; 11 Distance Function on the Flat Torus; 12 Weierstrass parameterisations of TPMS; 13 Discretization data for the Minimal Surfaces; 14 Gauss Curvature Data and distributions for the Minimal Surfaces; 15 Gauss curvature of minimal surfaces as function of the in-surface distance to the nearest flatpoint; 16 Further supplementary items;We provide a detailed list of ESM files at the end of the supporting information, but before the references. …"

Amorphous-diamond-50b.fe from Amorphous bicontinuous minimal surface models and the superior Gaussian curvature uniformity of Diamond, Primitive and Gyroid surfaces حسب M. Himmelmann (22743018)

"…It contains the following sections: 1 Deformation families of triply-periodic minimal surfaces; 2 Wooten-Winer-Weaire algorithm; 3 Amorphous diamond minimal surface; 4 Pinch-off effect; 5 Topology-stabilised minimisation scheme for minimal surface generation using the "Surface Evolver"; 6 Relaxation algorithm to enhance isotropy; 7 Minimal surfaces from parallel layers with catenoidal connections; 8 Heterogeneity of minimal surfaces from parallel layers with catenoidal connections; 9 Balance between the two labyrinths of the amorphous Diamond surfaces; 10 Use of a labelled Voronoi diagram in <i>pomelo</i> package for creating initial polyhedral interfaces between intertwined graphs; 11 Distance Function on the Flat Torus; 12 Weierstrass parameterisations of TPMS; 13 Discretization data for the Minimal Surfaces; 14 Gauss Curvature Data and distributions for the Minimal Surfaces; 15 Gauss curvature of minimal surfaces as function of the in-surface distance to the nearest flatpoint; 16 Further supplementary items;We provide a detailed list of ESM files at the end of the supporting information, but before the references. …"

System model of the educational video network. حسب Fengren Lin (22075465)

"…Thus, a resource allocation algorithm named Tasmanian devil whale optimization (TDWO) is introduced for secure video transmission in 5G networks. …"

Supplementary Material حسب Aris Shahbazian (22464325)

"…The supplementary material includes the full Python-based implementation of the AI-driven optimization framework described in the manuscript. It consists of three modules: 1.COMSOL Data Generator: A script that automates 500 high-fidelity COMSOL simulations using Latin Hypercube Sampling and extracts key plasma parameters such as electron density, uniformity, and absorbed power. 2.DNN Surrogate Model: A complete training pipeline using TensorFlow/Keras, including data preprocessing, model architecture, training, evaluation, and visualization of prediction accuracy. 3.Genetic Algorithm Optimization: A DEAP-based evolutionary optimization script that identifies optimal RF power and gas pressure values to maximize electron density while maintaining plasma uniformity above 90%. …"

Structural diagram of PPCS. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

Comparison between NSGA-II and RPGA. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

Parameter value. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

Summary of BBSDP-related studies. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

Symbol description. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

Nominal model solution results. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

May 1st −7st Metro Line 2 OD Statistics Table. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

Mode choice under rail transit disruption. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

Xi’an metro line 2 disruption stations. حسب Yicheng Liu (2179626)

"…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …"

Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model حسب Ramya Chinnasamy (21633527)

"…This framework integrates a novel biologically inspired optimization algorithm, the Indian Millipede Optimization Algorithm (IMOA), for effective feature selection. …"

Confusion matrix of model diagnosis result. حسب Yang Guo (399435)

"…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …"

Least squares support vector machine model. حسب Yang Guo (399435)

"…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …"

AdaBoost training flowchart. حسب Yang Guo (399435)

"…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …"