Dietary breadth in kangaroos facilitated resilience to Quaternary climatic variations.

Identifying what drove the late Pleistocene megafaunal extinctions on the continents remains one of the most contested topics in historical science. This is especially so in Australia, which lost 90% of its large species by 40,000 years ago, more than half of them kangaroos. Determining causation has been obstructed by a poor understanding of their ecology.

Leveraging generative neural networks for accurate, diverse, and robust nanoparticle design.

Tandem neural networks for inverse design can only make single predictions, which limits the diversity of predicted structures. Here, we use conditional variational autoencoder (cVAE) for the inverse design of core-shell nanoparticles. cVAE is a type of generative neural network that generates multiple valid solutions for the same input condition.

Deep learning for mango leaf disease identification: A vision transformer perspective.

Over the last decade, the use of machine learning in smart agriculture has surged in popularity. Deep learning, particularly Convolutional Neural Networks (CNNs), has been useful in identifying diseases in plants at an early stage. Recently, Vision Transformers (ViTs) have proven to be effective in image classification tasks.

Deep learning-driven forward and inverse design of nanophotonic nanohole arrays: streamlining design for tailored optical functionalities and enhancing accessibility.

In nanophotonics, nanohole arrays (NHAs) are periodic arrangements of nanoscale apertures in thin films that provide diverse optical functionalities essential for various applications. Fully studying NHAs' optical properties and optimizing performance demands understanding both materials and geometric parameters, which presents a computational challenge due to numerous potential combinations. Efficient computational modeling is critical for overcoming this challenge and optimizing NHA-based device performance.

Prevalence of Isthmi and Root Canal Configurations in Mandibular Permanent Teeth Using Cone-Beam Computed Tomography.

While root canal anatomy in Middle Eastern populations is well-studied, research on isthmi in mandibular permanent teeth from this region is limited. This retrospective study used cone-beam computed tomography (CBCT) to examine isthmi prevalence and location, as well as root canal morphologies (per Vertucci's classification) in mandibular permanent teeth from a subpopulation in Ardabil, Iran. The study is aimed at enhancing our understanding of dental anatomical variations in Middle Eastern populations.