Results 181 to 190 of about 56,994 (264)

Inter‐Material Transfer Learning for Accelerated Nanofluid Heat Transfer Prediction: A Machine Learning Approach for Energy Systems

open access: yesEnergy Science &Engineering, EarlyView.
This study presents an inter‐material transfer learning framework for nanofluid heat transfer prediction in energy systems. By leveraging knowledge from Al2O3‐water data, the model accurately predicts hybrid Al2O3‐TiO2 nanofluid performance with only 20 simulations, achieving R2 = 0.985 and reducing computational requirements by 78. ABSTRACT This paper
Soumaya Hadj Salah   +2 more
wiley   +1 more source

Calcitonin gene‐related peptide concentration in cerebrospinal fluid and serum in horses affected by trigeminal‐mediated headshaking

open access: yesEquine Veterinary Journal, EarlyView.
Abstract Background Trigeminal‐mediated headshaking (TMHS) in horses shares clinical features with human trigeminal neuralgia (HTN). Increased levels of the neuropeptide calcitonin gene‐related peptide (CGRP) have been found in the blood and cerebrospinal fluid (CSF) of HTN patients. Inhibition of CGRP in humans has shown promise for pain relief.
Lisa Annabel Weber   +7 more
wiley   +1 more source

Mono‐dimensional, two‐dimensional and Doppler echocardiographic measurements in healthy Standardbred neonatal foals in the first 5 days of life

open access: yesEquine Veterinary Journal, EarlyView.
Abstract Background Bodyweight, age and breed influence the echocardiographic assessment of foals. There are no echocardiographic studies in Standardbred neonatal foals. Objectives To describe echocardiographic values for selected variables, evaluate intra‐ and inter‐observer variability and assess cardiac changes in the first 5 days of life in healthy
Fernanda Timbó D'el Rey Dantas   +8 more
wiley   +1 more source

Intraday Functional PCA Forecasting of Cryptocurrency Returns

open access: yesJournal of Forecasting, EarlyView.
ABSTRACT We study the functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account.
Joann Jasiak, Cheng Zhong
wiley   +1 more source

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