Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Francesco Immorlano"'
Publikováno v:
Earth and Space Science, Vol 10, Iss 11, Pp n/a-n/a (2023)
Abstract Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger and more destructive. The
Externí odkaz:
https://doaj.org/article/1479b7bf82014b199af701098c123aa8
Autor:
Andrea Gatto, Valeria Aloisi, Gabriele Accarino, Francesco Immorlano, Marco Chiarelli, Giovanni Aloisio
Publikováno v:
AI, Vol 3, Iss 1, Pp 146-163 (2022)
Since December 2019, the novel coronavirus disease (COVID-19) has had a considerable impact on the health and socio-economic fabric of Italy. The effective reproduction number Rt is one of the most representative indicators of the contagion status as
Externí odkaz:
https://doaj.org/article/d1b356ca2ac44262b886efed57e47ba1
Autor:
Gabriele Accarino, Marco Chiarelli, Francesco Immorlano, Valeria Aloisi, Andrea Gatto, Giovanni Aloisio
Publikováno v:
AI, Vol 2, Iss 4, Pp 600-620 (2021)
One of the most important open challenges in climate science is downscaling. It is a procedure that allows making predictions at local scales, starting from climatic field information available at large scale. Recent advances in deep learning provide
Externí odkaz:
https://doaj.org/article/c012a57e6bcc4ff79f35923fb0b92b2a
Autor:
Andrea Gatto, Gabriele Accarino, Valeria Aloisi, Francesco Immorlano, Francesco Donato, Giovanni Aloisio
Publikováno v:
Informatics, Vol 8, Iss 3, p 57 (2021)
Compartmental models have long been used in epidemiological studies for predicting disease spread. However, a major issue when using compartmental mathematical models concerns the time-invariant formulation of hyper-parameters that prevent the model
Externí odkaz:
https://doaj.org/article/4a95c4e10eff4925a1abd969783550ed
Autor:
Donatello Elia, Sonia Scardigno, Alessandro D'Anca, Gabriele Accarino, Jorge Ejarque, Francesco Immorlano, Daniele Peano, Enrico Scoccimarro, Rosa M. Badia, Giovanni Aloisio
Typical end-to-end Earth System Modelling (ESM) workflows rely on different steps including data pre-processing, numerical simulation, output post-processing, as well as data analytics and visualization. The approaches currently available for impleme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85c7cc310aaf0a45da63bbba64b1bb9b
https://doi.org/10.5194/egusphere-egu23-6857
https://doi.org/10.5194/egusphere-egu23-6857
In recent years, Climate Change has been leading to an exacerbation of Extreme Weather Events (EWEs), such as storms and wildfires, raising major concerns in terms of their increase of their intensity, frequency and duration. Detecting and predicting
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::052d1710bad51d521f7577ad74c7caff
https://doi.org/10.5194/egusphere-egu23-6060
https://doi.org/10.5194/egusphere-egu23-6060
Autor:
Andrea Gatto, Marco Chiarelli, Giovanni Aloisio, Valeria Aloisi, Gabriele Accarino, Francesco Immorlano
Publikováno v:
AI, Vol 2, Iss 36, Pp 600-620 (2021)
AI; Volume 2; Issue 4; Pages: 600-620
AI; Volume 2; Issue 4; Pages: 600-620
One of the most important open challenges in climate science is downscaling. It is a procedure that allows making predictions at local scales, starting from climatic field information available at large scale. Recent advances in deep learning provide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c0ff9c9fb2fb6f1af4e147be8f8f517
https://www.mdpi.com/2673-2688/2/4/36
https://www.mdpi.com/2673-2688/2/4/36
Autor:
Francesco Donato, Giovanni Aloisio, Francesco Immorlano, Valeria Aloisi, Gabriele Accarino, Andrea Gatto
Publikováno v:
Informatics, Vol 8, Iss 57, p 57 (2021)
Informatics
Volume 8
Issue 3
Informatics
Volume 8
Issue 3
Compartmental models have long been used in epidemiological studies for predicting disease spread. However, a major issue when using compartmental mathematical models concerns the time-invariant formulation of hyper-parameters that prevent the model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::412497222046c3d5e8375b5be3848e68
https://www.mdpi.com/2227-9709/8/3/57
https://www.mdpi.com/2227-9709/8/3/57