Comparison of treatment effects between premolar extraction and mandibular third molar extraction groups. * p < 0.05; ** p < 0.01. by Lijuan Liu (136685)

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Comparison of upper and lower arch crowding between premolar extraction group and the mandibular third molar extraction groups. by Lijuan Liu (136685)

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Intraoperative facial and intraoral photographs of the case with mandibular third molar extraction. by Lijuan Liu (136685)

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Pretreatment facial, intraoral photographs, and radiographs of the case with premolar tooth extraction. by Lijuan Liu (136685)

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Pretreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction. by Lijuan Liu (136685)

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Flow diagram of participant selection. by Prajwal Paudel (7359209)

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Bivariate association of characteristics of mothers (<34 weeks POG) who received and did not receive ACS. by Prajwal Paudel (7359209)

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Supplementary file ACS. by Prajwal Paudel (7359209)

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Multivariate analysis of the outcomes among the newborns who mothers receive or did not receive ACS. by Prajwal Paudel (7359209)

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Bivariate analysis of morbidities among newborns whose mothers received or did not receive ACS. by Prajwal Paudel (7359209)

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Multivariate analysis of the morbidities among the newborns who mothers receive or did not receive ACS. by Prajwal Paudel (7359209)

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Dilated Cardiomyopathy-related mortality trends from 1999 to 2023, stratified by Urbanization in the United States. by Zian Zafar Cheema (22446246)

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Dilated Cardiomyopathy-related mortality trends from 1999 to 2023, stratified by Census Regions in the United States. by Zian Zafar Cheema (22446246)

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Dilated Cardiomyopathy-related mortality trends from 1999 to 2023, stratified by the Overall population and Sex in the United States. by Zian Zafar Cheema (22446246)

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Dilated Cardiomyopathy-related mortality trends from 1999 to 2023, stratified by the Racial groups in the United States. by Zian Zafar Cheema (22446246)

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Testing set error. by Xiangjuan Liu (618000)

“…Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”

Internal structure of an LSTM cell. by Xiangjuan Liu (618000)

“…Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”

Prediction effect of each model after STL. by Xiangjuan Liu (618000)

“…Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”

The kernel density plot for data of each feature. by Xiangjuan Liu (618000)

“…Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”

Analysis of raw data prediction results. by Xiangjuan Liu (618000)

“…Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”