Development of an automated SILAR method for the sustainable fabrication of BiOI/TiO photoanodes.

BiOI is a promising material for use in photoelectrocatalytic water oxidation, renowned for its chemical inertness and safety in aqueous media. For device integration, BiOI must be fabricated into films. Considering future industrial applications, automated production is essential.

Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures.

The potential and promise of deep learning systems to provide an independent assessment and relieve radiologists' burden in screening mammography have been recognized in several studies. However, the low cancer prevalence, the need to process high-resolution images, and the need to combine information from multiple views and scales still pose technical challenges. Multi-view architectures that combine information from the four mammographic views to produce an exam-level classification score are a promising approach to the automated processing of screening mammography.

A Testbed for Studying Cybersickness and its Mitigation in Immersive Virtual Reality.

Cybersickness (CS) represents one of the oldest problems affecting Virtual Reality (VR) technology. In an attempt to resolve or at least limit this form of discomfort, an increasing number of mitigation techniques have been proposed by academic and industrial researchers. However, the validation of such techniques is often carried out without grounding on a common methodology, making the comparison between the various works in the state of the art difficult.

Self-supervised and semi-supervised learning for road condition estimation from distributed road-side cameras.

Monitoring road conditions, e.g., water build-up due to intense rainfall, plays a fundamental role in ensuring road safety while increasing resilience to the effects of climate change.

Data augmentation for medical imaging: A systematic literature review.

Recent advances in Deep Learning have largely benefited from larger and more diverse training sets. However, collecting large datasets for medical imaging is still a challenge due to privacy concerns and labeling costs. Data augmentation makes it possible to greatly expand the amount and variety of data available for training without actually collecting new samples.