Characterization of the impact of glucose/galactose on infection outcomes. by Tyron Chang (9922533)

Glucose/galactose selectively alters ISG protein not RNA levels in interferon-primed cells. by Tyron Chang (9922533)

Glucose but not galactose culturing of IFN-γ primed cells reduces VACV and HSV-1 replication. by Tyron Chang (9922533)

<i>IRF1</i> impacts VACV replication in glucose/IFN-γ primed but not galactose conditions. by Tyron Chang (9922533)

Medicare clozapine data analysis. by Luke R. Cavanah (19022435)

“…We observed a steady decrease in clozapine use adjusted for population (−18.0%) and spending (−24.9%) over time. …”

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

iKIR score decreases protection associated with protective class II genotypes in T1D. by Laura Mora-Bitria (20477525)

Microhardness of various samples. by Huashen Guan (20454677)

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Shot peening process parameters. by Huashen Guan (20454677)

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Wear morphology: (a) UT, (b) SP1, (c) SP2, and (d) SP3. by Huashen Guan (20454677)

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Schematic diagram of pin-disk wear. by Huashen Guan (20454677)

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Surface 3D topography (a) UT, (b) SP1, (c) SP2, (d) SP3. by Huashen Guan (20454677)

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Parameters of the sliding wear test. by Huashen Guan (20454677)

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Finite element model of shot peening. by Huashen Guan (20454677)

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Chemical compositions of the 25CrNi2MoV steel (wt. %). by Huashen Guan (20454677)

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Wear morphology of the SP samples: (a) (b) SP1, (c) (d) SP2, and (e)(f) SP3. by Huashen Guan (20454677)

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