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

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Sampling point (Source: by Yonggang Huang (223155)

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Enrichment Analysis Based on Ferroptosis-Related Differentially Expressed Genes. by Jiyong Zhang (2498740)

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Correlation between hub genes and 50 hallmark signaling pathways. by Jiyong Zhang (2498740)

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ROC curves for hub genes. by Jiyong Zhang (2498740)

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Intersection genes obtained from the PPI network and PPI network analysis of intersection genes. colored circles represent genes. by Jiyong Zhang (2498740)

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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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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. …”

The kernel density plot for data of each feature. 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. …”

Analysis of raw data prediction results. 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. …”

Flowchart of the 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. …”

SARIMA predicts season components. 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. …”