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. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

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. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

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. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

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. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

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. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

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. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

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. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

Downregulation of CD36 impaired phagocytic capacity and is associated with a proinflammatory phenotype of <i>Mst1</i> deficiency macrophages. by Jianyang Li (461703)

Schematic diagram of monitoring points and units. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

The analysis procedure. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

Model monitoring points and units coordinates. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

Soil mechanical parameters. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

Parameters of the contact surface. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

The schematic diagram of free-field boundary. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

Residual deformation parameters of soil. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

Soil fluid and liquefaction parameters. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

PL-Finn model development procedure diagram. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

Acoustic startle at 6 dpf. by Morgan Barnes (7876373)

“…<p>(A-B) These graphs represent the area under the curve of the LLC response frequency. There are significant decreases in LLC responses in the 24+ (p < 0.0001) and 48+ (p ≤ 0.0001) fish. …”

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

<i>japonicum</i> infection induces MST1 activation. by Jianyang Li (461703)