Strength test results of semi-circular specimens. by Yining Wang (432154)

“…Displacement evolution analysis indicates that the modifier enhances inter-aggregate bonding, increases failure displacement, prolongs the stable phase, and significantly improves overall durability. …”

Bending test results of beam specimens. by Yining Wang (432154)

“…Displacement evolution analysis indicates that the modifier enhances inter-aggregate bonding, increases failure displacement, prolongs the stable phase, and significantly improves overall durability. …”

VFSS results. by Rie Asayama (693361)

S1 Raw data - by Dominic Michael Rasp (19959412)

“…Compared to pre-match values, hamstring strength was significantly decreased after 15 and 30 minutes of simulated soccer match for the non-dominant and dominant leg, respectively. …”

S2 Raw data - by Dominic Michael Rasp (19959412)

“…Compared to pre-match values, hamstring strength was significantly decreased after 15 and 30 minutes of simulated soccer match for the non-dominant and dominant leg, respectively. …”

Summary of HSS and LSI during the LIST. by Dominic Michael Rasp (19959412)

“…Compared to pre-match values, hamstring strength was significantly decreased after 15 and 30 minutes of simulated soccer match for the non-dominant and dominant leg, respectively. …”

Types of drought trends in the Santon River Irrigation District and interannual trends of the Sen slope during 2005–2023. by Yuxin Wei (11510974)

Fig 4 - by Kirralise J. Hansford (20670869)

Quantification of cartilage thickness in all cases. by Yusuke Fuchioka (22806178)

Combined illusion score index across conditions (NI: Non-Illusion; NIT: Non-Illusion Tactile; MS: Multisensory; UV: Unimodal Visual). by Kirralise J. Hansford (20670869)

Simulation datasets. by Xiao Mo (2430355)

“…These results offer significant theoretical guidance for the design and improvement of needle-free injection.…”

Topological properties of soil bacteria in different disturbed areas of CG. by Bianhua Zhang (22430652)

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. …”

PCAECG_GAN. 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 dataset expansion quantities and Proportions. 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. …”

Experimental hardware and software environment. 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 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. …”

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. 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. …”