Fig 15 - by Zhezhe Zhang (19704587)

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Fig 23 - by Zhezhe Zhang (19704587)

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Stress path diagram of Ⅱ, Ⅲ and Ⅳ groups of specimens. by Zhezhe Zhang (19704587)

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CONSORT 2025 Flow Diagram [70]. by Robert Trybulski (10998702)

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Basic mechanical parameters of concrete specimens. by Zhezhe Zhang (19704587)

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Fig 19 - by Zhezhe Zhang (19704587)

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Fig 8 - by Zhezhe Zhang (19704587)

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Mean values and 95% confidence intervals for recovery time, Average NN Interval (AVNN); Standard Deviation of NN Intervals (SDNN), Low Frequency/ High Frequency Ratio (LF/HF); and... by Robert Trybulski (10998702)

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Mean values and 95% confidence intervals for resting flow, arterial occlusion pressure (AOP), biological zero, reactive hyperemia (RHmax), and time to peak across the three conditi... by Robert Trybulski (10998702)

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Percentage reduction of shear strength parameters. by Zhezhe Zhang (19704587)

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Relationship between τ_<i>i</i> and σ_<i>i</i>. by Zhezhe Zhang (19704587)

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Convergence trends for IEEE 33-bus test system. by Ahmed A. Zaki Diab (12757190)

Temperature-dependent parameter values under stable temperatures conditions. by Zadoki Tabo (12415261)

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Temperature-invariant parameters, symbols, ranges and baseline values, and sources. by Zadoki Tabo (12415261)

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Temperature-dependent parameters, symbols, derived curves, ranges, and their sources. by Zadoki Tabo (12415261)

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Temperature effects on populations of <i>S. haematobium</i> and <i>S. mansoni</i> infections. by Zadoki Tabo (12415261)

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Temperature-dependent model parameters in the life history of <i>Schistosoma</i> species between human and intermediate host genera. by Zadoki Tabo (12415261)

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Impacts of individual input parameters on the output variables (a) by Zadoki Tabo (12415261)

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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. …”