The Date. by Chengyuan Hao (21615653)

“…The relevant low temperature index showed proper decreasing trend while the diurnal range of annual extreme temperature showed fluctuating<b>—</b>decreasing first and then increasing. …”

Mann-Kendall test for the mean temperature index. by Chengyuan Hao (21615653)

“…The relevant low temperature index showed proper decreasing trend while the diurnal range of annual extreme temperature showed fluctuating<b>—</b>decreasing first and then increasing. …”

Variation curve of the extreme temperature index. by Chengyuan Hao (21615653)

“…The relevant low temperature index showed proper decreasing trend while the diurnal range of annual extreme temperature showed fluctuating<b>—</b>decreasing first and then increasing. …”

Fluctuation trend of the mean temperature index. by Chengyuan Hao (21615653)

“…The relevant low temperature index showed proper decreasing trend while the diurnal range of annual extreme temperature showed fluctuating<b>—</b>decreasing first and then increasing. …”

Variation curve of the mean temperature index. by Chengyuan Hao (21615653)

“…The relevant low temperature index showed proper decreasing trend while the diurnal range of annual extreme temperature showed fluctuating<b>—</b>decreasing first and then increasing. …”

Detail of the personalized-enhanced GCN. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Enhanced multi-component module. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

The architecture of the TCBiL. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Detail of the encoder. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Detail of the Fourier transform. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Detail of the decoder. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Encoder-decoder architecture. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Dataset description. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Blood Pressure and LDL-C During Follow-up. by Karl Ingard (22582091)

“…The risk of cardiovascular death was significantly decreased (HR 0.64, 95% CI 0.41–0.998, <i>P</i> = 0.049) and the risk of fracture non-significantly increased (HR 1.47, 95% CI 0.95–2.27, <i>P</i> = 0.08) in the intervention group compared to the control group.…”

Baseline Characteristics of Included Patients. by Karl Ingard (22582091)

“…The risk of cardiovascular death was significantly decreased (HR 0.64, 95% CI 0.41–0.998, <i>P</i> = 0.049) and the risk of fracture non-significantly increased (HR 1.47, 95% CI 0.95–2.27, <i>P</i> = 0.08) in the intervention group compared to the control group.…”

Attention reduces decision uncertainty under high cognitive demand. by Rahul Garg (3064578)

“…(L) Fraction of glomeruli with significant changes with cued-odor. Yellow: increased, dark grey: decreased, light grey: unchanged. …”

Cued-attention is mediated by a cholinergic HDB to SAC circuit. by Rahul Garg (3064578)

PCA-CGAN 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. 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. …”

MIT-BIH dataset proportion analysis 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. 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. …”

Wavelet transform preprocessing 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. …”