Total porosity versus permeability of the rocks of different lithologies in the study area. حسب Dong Feng (5375471)

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Changes in mineral composition of unweathered and weathered sandstones. حسب Dong Feng (5375471)

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Porosity and permeability of different types of rocks. حسب Dong Feng (5375471)

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Fitted curves of D_T and the percentages of different sizes of pores in unweathered sandstones and weathered sandstones. حسب Dong Feng (5375471)

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Data. حسب Dong Feng (5375471)

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Fractal dimensions of different rock samples. حسب Dong Feng (5375471)

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Fitted curves of D_T and the total porosity of rocks of different lithologies. حسب Dong Feng (5375471)

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Fractal dimensions of different sandstones. حسب Dong Feng (5375471)

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Classification of rock pores and methods of study. حسب Dong Feng (5375471)

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Baseline characteristics of patients. حسب Su Hwan Park (15158181)

"…Participants in the progression group were younger (60.7 vs. 65.7 years, P = 0.015) and showed a larger BCVA change (0.20 vs. 0.04, P < 0.001) and greater ERM area decrease (34.2% vs. 11.7%, P < 0.001) during the follow-up period. …"

Changes in best-corrected visual acuity and total area reduction ratio of the epiretinal membrane (ERM) during follow-up. حسب Su Hwan Park (15158181)

Clinical and demographic characteristics of the participants. حسب María Benito-de-Pedro (22057468)

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Layup muscle synergies. حسب María Benito-de-Pedro (22057468)

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Maximum voluntary contraction assessment. حسب María Benito-de-Pedro (22057468)

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A) Right-hand layup gesture. B) Left-hand layup gesture. حسب María Benito-de-Pedro (22057468)

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Ultrafine Particulate Matter Exacerbates the Risk of Delayed Neural Differentiation: Modulation Role of METTL3-Mediated m⁶A Modification حسب Rui Wang (52434)

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Placement of surface electrodes according to SENIAM protocol. حسب María Benito-de-Pedro (22057468)

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Testing set error. 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. Finally, the Beluga Whale Optimization (BWO)-tuned STL-PCA-BWO-BiLSTM hybrid model delivered optimal performance on test sets (RMSE = 0.22, MAE = 0.16, MAPE = 0.99%, ), exhibiting 40.7% higher accuracy than unoptimized BiLSTM (MAE = 0.27). …"

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. Finally, the Beluga Whale Optimization (BWO)-tuned STL-PCA-BWO-BiLSTM hybrid model delivered optimal performance on test sets (RMSE = 0.22, MAE = 0.16, MAPE = 0.99%, ), exhibiting 40.7% higher accuracy than unoptimized BiLSTM (MAE = 0.27). …"

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. Finally, the Beluga Whale Optimization (BWO)-tuned STL-PCA-BWO-BiLSTM hybrid model delivered optimal performance on test sets (RMSE = 0.22, MAE = 0.16, MAPE = 0.99%, ), exhibiting 40.7% higher accuracy than unoptimized BiLSTM (MAE = 0.27). …"