Mediation model illustrating the relationship between eGDR, diabetes, and frailty. by Wentao Yang (205781)

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LASSO regression visualization plot. by Wentao Yang (205781)

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SHAP dependence plots. by Wentao Yang (205781)

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Weighted ORs (95% CIs) for the associations between betweenc across three models. by Wentao Yang (205781)

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Tertile stratified subgroup analysis. by Wentao Yang (205781)

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Mortality of <i>C. maculatus</i> after 24h exposure to GO and GO-GA NPs. by Huda H. Elbehery (22492996)

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Mean mesenteric, retroperitoneal, pericardial, and total visceral adipose tissue weights (in grams) upon completion of the experiment. by Elsie D. Oduor (22521785)

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Minimal data set. by Danan Zhao (20861666)

“…<div><p>The study of the adsorption characteristics of coal is of great significance to gas prevention and CO₂ geological storage. …”

Mod antibody and RNAi validations. by Amalia S. Parra (4173004)

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Study-related adverse events. by Benjamin R. Lewis (22279166)

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Study flow chart. by Benjamin R. Lewis (22279166)

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Study CONSORT diagram. by Benjamin R. Lewis (22279166)

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Charts revealing A) the significant decrease (<i>p</i> < 0.05) in the membrane integrity and B) the significant increase (<i>p</i> < 0.05) in the membrane permeability after treatment with harmalacidine hydrochloride in a representative <i>S. aureus</i> isolate (n = 3 as technical repeats of the same isolate). by Manal A. Alossaimi (10269852)

“…<p>Charts revealing A) the significant decrease (<i>p</i> < 0.05) in the membrane integrity and B) the significant increase (<i>p</i> < 0.05) in the membrane permeability after treatment with harmalacidine hydrochloride in a representative <i>S. aureus</i> isolate (n = 3 as technical repeats of the same isolate).…”

Change in rectal temperature following pre-medication, induction and inhalant anesthesia. by Jacob P. Rastas (20642173)

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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. …”

Flowchart of the 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. …”

SARIMA predicts season components. 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. …”