Introduction Autism spectrum disorder (ASD) is among the most common and pervasive neurodevelopmental disorders.Yet, despite decades of research, the neurobiology of ASD is still poorly understood, as inconsistent findings preclude the identification of robust and interpretable neurobiological markers and predictors of clinical symptoms.Objectives
The present study seeks to improve the accuracy and reliability of disease identification in apple fruits and leaves through the use of state-of-the-art deep learning techniques.The research investigates several state-of-the-art architectures, such as Xception, InceptionV3, InceptionResNetV2, EfficientNetV2M, MobileNetV3Large, ResNet152V2, DenseNet
Abstract Molecular dynamics (MD) simulations are performed to derive an equation of state (EOS) for helium (He) bubbles in tungsten (W) and to study the growth of He bubbles under a W(100) surface until they burst.We study the growth as a function of the initial nucleation depth of the bubbles.During growth, successive loop-punching events are obse