Expression profile heatmaps of key genes in gene sets. by Jiyong Zhang (2498740)

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Influence of UHMWPE length on free swelling ratio. by Yonggang Huang (223155)

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Construction of the WGCNA Co-expression Network. by Jiyong Zhang (2498740)

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Influence of UHMWPE content on free swelling ratio. by Yonggang Huang (223155)

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Soil partice-size distribution. by Yonggang Huang (223155)

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Differential Gene Expression Associated with Non-Alcoholic Fatty Liver Disease. by Jiyong Zhang (2498740)

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Interaction analysis of hub genes. by Jiyong Zhang (2498740)

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Change in temperature from baseline. by Katharyn L. Flickinger (4759323)

Hemodynamic and respiratory changes over time. by Katharyn L. Flickinger (4759323)

Linear association Between Vitamin D Concentration and Visceral Adiposity Index (VAI) and Lipid Accumulation Product (LAP). by Hyelim Lee (11286822)

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Comparison of Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), and Related Variables by Vitamin D Status (A) VAI, (B) LAP, (C) TG, (D) WC, (E) BMI, (F) HDL-C. by Hyelim Lee (11286822)

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Distribution of HWP by Criteria and Vitamin D Status (Three Groups). by Hyelim Lee (11286822)

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Association Between Vitamin D Status and Hypertriglyceridemic Waist Phenotype (HWP). by Hyelim Lee (11286822)

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Distribution of Hypertriglyceridemic Waist Phenotype, abdominal obesity, and high triglyceride by serum vitamin D Status. by Hyelim Lee (11286822)

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Characteristics of Study Participants. by Hyelim Lee (11286822)

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Distribution of HWP by Criteria and Vitamin D Status (Two Groups). by Hyelim Lee (11286822)

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The Efficacy of Taurolidine Lock Solution in Preventing Catheter-Related Bloodstream Infections in Pediatrics: A Systematic Review and Meta-Analysis by Congyu Qin (22386781)

Testing set error. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

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

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

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

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”