Data. by Dong Feng (5375471)

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

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

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

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

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Table 1_Microbial diversity and composition in the gut microbiome of patients during systemic inflammatory response syndrome: can we use gut bacteria as potential biomarkers to cha... by Rafaela Ramalho Guerra (22106579)

Actin-Targeted Magnetic Nanomotors Mechanically Modulate the Tumor Mechanical Microenvironment for Cancer Treatment by Xing Fan (88299)

“…Cancer-associated fibroblasts (CAFs) and tumor cells, which internalize ∼69.3% of ABP-MNs, are significantly tuned under MF with signs of a 7-fold decrease in tumor matrix stiffness, increased immune cell infiltration, and 95.8% tumor growth inhibition. …”

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

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

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

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

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

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Testing set error. 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. 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. 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. 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). …”