Classification model parameter settings. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

MIT-BIH expanded dataset proportion chart. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

AUROC Graphs of RF Model and ResNet. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

PCA-CGAN Model Workflow Diagram. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

Structural Diagrams of RF Model and ResNet Model. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

PCA-CGAN model convergence curve. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

PCA-CGAN Structure Diagram. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

Comparison of Model Five-classification Results. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

PCAECG-GAN K-fold experiment table. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

PCA-CGAN Pseudocode Table. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

PCA-CGAN Ablation Experiment Results. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

Data from: Temporal niche partitioning by nocturnal arboreal mammals increases the modularity of plant-frugivore networks in a fragmented subtropical landscape by Wande Li (12480883)

“…Island area positively predicted nocturnal interaction richness and its proportional contribution, whereas isolation had no significant effect. Incorporating nocturnal interactions increased network modularity and decreased nestedness, indicating temporal niche partitioning, while connectance remained unchanged. …”

PCAIs inhibit NCI-H23 cell line viability. by Matthew D. Gregory (19929096)

“…Polyisoprenylated Cysteinyl amide Inhibitors (PCAIs) constitute a group of potential cancer therapy agents that we designed to specifically disrupt and suppress hyperactive G-protein signaling, such as that caused by mutated RAS proteins. …”

PCAIs disrupt actin filaments in NCI-H23 cells. by Matthew D. Gregory (19929096)

“…Polyisoprenylated Cysteinyl amide Inhibitors (PCAIs) constitute a group of potential cancer therapy agents that we designed to specifically disrupt and suppress hyperactive G-protein signaling, such as that caused by mutated RAS proteins. …”

PCAIs suppress 3D NCI-H23 cell invasion. by Matthew D. Gregory (19929096)

“…Polyisoprenylated Cysteinyl amide Inhibitors (PCAIs) constitute a group of potential cancer therapy agents that we designed to specifically disrupt and suppress hyperactive G-protein signaling, such as that caused by mutated RAS proteins. …”

PCAIs suppress NCI-H23 cancer cell migration. by Matthew D. Gregory (19929096)

“…Polyisoprenylated Cysteinyl amide Inhibitors (PCAIs) constitute a group of potential cancer therapy agents that we designed to specifically disrupt and suppress hyperactive G-protein signaling, such as that caused by mutated RAS proteins. …”

PCAIs induce apoptosis in NCI-H23 cells. by Matthew D. Gregory (19929096)

“…Polyisoprenylated Cysteinyl amide Inhibitors (PCAIs) constitute a group of potential cancer therapy agents that we designed to specifically disrupt and suppress hyperactive G-protein signaling, such as that caused by mutated RAS proteins. …”

<b>Data from: </b><b>Experimentally increased food availability allows for earlier departure in a long-distance migratory shorebird</b> by Thomas Lameris (20888213)

“…When none of the slope coefficients were greater than 0.5 g/day, we did not determine a start of body mass increase (27 out of 94 birds). …”

Summary of related studies. by Most. Fatematuz Zohora (19429152)

“…Traditional methods like the Banker’s algorithm provide solutions but suffer from limitations such as static process handling, high time complexity, and a lack of real-time adaptability. To address these challenges, we propose the Dynamic Banker’s Deadlock Avoidance Algorithm (DBDAA). …”

Time complexity of different safety algorithms. by Most. Fatematuz Zohora (19429152)

“…Traditional methods like the Banker’s algorithm provide solutions but suffer from limitations such as static process handling, high time complexity, and a lack of real-time adaptability. To address these challenges, we propose the Dynamic Banker’s Deadlock Avoidance Algorithm (DBDAA). …”