Statistical tests of ACC on the random network. حسب Ruochen Zhang (3434996)

"…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …"

Parameters in the experiment. حسب Ruochen Zhang (3434996)

"…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …"

Statistical tests of APL on the random network. حسب Ruochen Zhang (3434996)

"…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …"

Statistical tests of ACC on the regular network. حسب Ruochen Zhang (3434996)

"…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …"

Statistical tests of APL on the regular network. حسب Ruochen Zhang (3434996)

"…The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. …"

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis حسب Metodi V. Metodiev (6089009)

"…It covers data processing from <i>in silico</i> digestion of protein sequences to comprehensive identification of post-translational modifications and solving the protein inference problem. …"

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis حسب Metodi V. Metodiev (6089009)

"…It covers data processing from <i>in silico</i> digestion of protein sequences to comprehensive identification of post-translational modifications and solving the protein inference problem. …"

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis حسب Metodi V. Metodiev (6089009)

"…It covers data processing from <i>in silico</i> digestion of protein sequences to comprehensive identification of post-translational modifications and solving the protein inference problem. …"

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis حسب Metodi V. Metodiev (6089009)

"…It covers data processing from <i>in silico</i> digestion of protein sequences to comprehensive identification of post-translational modifications and solving the protein inference problem. …"

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis حسب Metodi V. Metodiev (6089009)

"…It covers data processing from <i>in silico</i> digestion of protein sequences to comprehensive identification of post-translational modifications and solving the protein inference problem. …"

Reactive Molecular Simulation and Microscopic Origins in the Reaction Kinetics of Binary Polymerization حسب Xinwei Chen (255125)

"…The effect of binary polymerization is assessed by the hybrid function, which quantifies the deviation of binary polymerization from single mechanism polymerization. …"

Data Sheet 2_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.zip حسب Rui Zhang (13940)

"…The model for potential pregnancy loss was also developed using five machine learning algorithms and was based on 7 indicators. According to the results obtained from the testing set, the sensitivity was higher than 92.02%, the specificity was higher than 95.18%, the accuracy was higher than 94.34%, and the AUC was higher than 0.972.…"

Data Sheet 1_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.docx حسب Rui Zhang (13940)

"…The model for potential pregnancy loss was also developed using five machine learning algorithms and was based on 7 indicators. According to the results obtained from the testing set, the sensitivity was higher than 92.02%, the specificity was higher than 95.18%, the accuracy was higher than 94.34%, and the AUC was higher than 0.972.…"

Overnight technician routing and scheduling problem with time windows and balanced workloads: a bi-objective zebra optimization algorithm حسب Abolfazl Gharaei (21803416)

"…The performance evaluation and validation results revealed that the proposed ML-based BOZOA provides very good performance in solving TRSPTWs at a variety of scales with respect to the optimality criteria, including, number of taken iterations, infeasibility, optimality error and complementarity compared with both an exact solver and two inspired algorithms from ZOA.</p> <p><b>Highlights</b></p><p>An ML-based bi-objective zebra optimisation algorithm to treat large-scale TRSPs</p><p>Centroid-based clustering on the population of zebras to avoid bias towards a specific search space</p><p>Making a trade-off between exploration and exploitation of the feasible region in the developed algorithm</p><p>A new MINLP model of a weighted bi-objective TRSP with limited capacity depots</p><p>Workload function, penalty function for lateness, subcontracts, time windows for tasks and breaks</p><p>Experiments using real data to show the performance of the model and solution method</p><p></p> <p>An ML-based bi-objective zebra optimisation algorithm to treat large-scale TRSPs</p> <p>Centroid-based clustering on the population of zebras to avoid bias towards a specific search space</p> <p>Making a trade-off between exploration and exploitation of the feasible region in the developed algorithm</p> <p>A new MINLP model of a weighted bi-objective TRSP with limited capacity depots</p> <p>Workload function, penalty function for lateness, subcontracts, time windows for tasks and breaks</p> <p>Experiments using real data to show the performance of the model and solution method</p>…"

<b>Optimization of the whole life capacity configuration of the hydrogen production system based on improved whale optimization algorithm</b> حسب Fan Jiang (21178691)

"…The improvements notably boost the algorithm's convergence speed and optimization accuracy, as validated by five benchmark function types. …"

Ablation study visualization results. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

Experimental parameter configuration. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

FLMP-YOLOv8 identification results. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

C2f structure. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"

Experimental environment configuration. حسب Xiaozhou Feng (2918222)

"…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …"