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

الموضوعات:

LASSO regression visualization plot. حسب Wentao Yang (205781)

الموضوعات:

SHAP dependence plots. حسب Wentao Yang (205781)

الموضوعات:

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

الموضوعات:

Tertile stratified subgroup analysis. حسب Wentao Yang (205781)

الموضوعات:

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

الموضوعات:

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

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Minimal data set. حسب 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. حسب Amalia S. Parra (4173004)

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

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

الموضوعات:

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

الموضوعات:

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). حسب 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. حسب Jacob P. Rastas (20642173)

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Internal structure of an LSTM cell. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. …"