Interaction panel of Treat × Period effect (T × P) for body measurements as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein calves (n =... by Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for eating time (min/8-h) and sorting index (%) on sieve 2.36 mm and bottom pan as influenced by feeding pasteurized waste milk (... by Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for starter intake (g/d), body weight (kg), average daily gain (g/d), and feed efficiency (g/g) as influenced by feeding pasteuri... by Shahryar Kargar (18880600)

Prediction of bacterial BugBase phenotype. by Jun Sun (48981)

Transcript levels of MAPK, NIK, NLR and SA-dependent gene pathways and responsive genes. by Bulelani L. Sizani (21485746)

South African cassava mosaic virus (SACMV) genome structure and the effect of SACMV on cassava leaf symptoms. by Bulelani L. Sizani (21485746)

Supporting information – data for plots. by Maria Nadinskaia (22662419)

Non-enzymatic antioxidants in hemolymph of honey bee workers. by Agnieszka Murawska (11021928)

Enzymes activity in hemolymph of honey bee workers. by Agnieszka Murawska (11021928)

This figure shows the forest plot for the multivariable logistic regression found in Table 1. by Scott J. Halperin (17835402)

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. …”

Renal outcomes of both treatment groups. by Marwan El-Deyarbi (21688492)

“…Participants in the multifactorial group achieved a significant mean difference in low-density lipoprotein cholesterol levels (mean difference = −0.14, 95% CI: −0.27–0.001, P < 0.03), and significant adjusted mean difference of eGFR levels difference (3.93 mL/min/1.73 m², 95% CI: 1.27–6.58, P < 0.01) at study completion compared to those in the control group. …”

Multilevel logistic regression analysis of individual and community level factors. by Mulugeta Wassie (10803155)

Subjects:

Socio- demographic characteristics of study participants. by Mulugeta Wassie (10803155)

Subjects:

Model comparison and random effect. by Mulugeta Wassie (10803155)

Subjects:

Manuscript data. by Khalid A. Al-Gaadi (2826632)

“…Moreover, the total tomato fruit yield also decreased significantly at salinity-3 compared to salinity-1.…”