Claudio Agostinelli

TL;DR: By providing direct information on the levels and trends of key pollutants, this study enables some general considerations about air pollution in an important hotspot of Southern Europe, the eastern Po Valley, where the levels of some key pollutants are still far from meeting the EC limit and target values.

TL;DR: The need for a new generation of robust estimators that can efficiently deal with cellwise outliers and at the same time show good performance under casewise outliers is highlighted.

TL;DR: In this paper, a deep learning architecture for the classification of metagenomics data based on the convolutional neural networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure, is presented.

TL;DR: In this paper, a robust F-test and a robust stepwise regression procedure based on weighted likelihood were introduced to achieve robustness against the presence of outliers. But the approach is not asymptotically equivalent to the classical one when no contamination is present.

TL;DR: The results show that OC, EC and SOC exhibited higher concentration during winter months in all measurement sites, suggesting that the stable atmosphere and lower mixing play important role for the accumulation of air pollutant and hasten the condensation or adsorption of volatile organic compounds over the Veneto region.