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pro vyhledávání: '"Trezza, A"'
Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystals. From this, we identify over
Externí odkaz:
http://arxiv.org/abs/2411.10125
We assume that a sufficiently large database is available, where a physical property of interest and a number of associated ruling primitive variables or observables are stored. We introduce and test two machine learning approaches to discover possib
Externí odkaz:
http://arxiv.org/abs/2401.05226
Autor:
Trezza, Giovanni, Chiavazzo, Eliodoro
It stands to reason that the amount and the quality of data is of key importance for setting up accurate AI-driven models. Among others, a fundamental aspect to consider is the bias introduced during sample selection in database generation. This is p
Externí odkaz:
http://arxiv.org/abs/2311.09891
Publikováno v:
IEEE Transactions on Vehicular Technology, 2023
The hybridization process has recently touched also the world of agricultural vehicles. Within this context, we develop an Energy Management Strategy (EMS) aiming at optimizing fuel consumption, while maintaining the battery state of charge. A typica
Externí odkaz:
http://arxiv.org/abs/2308.02203
Measurement-adaptive track initiation remains a critical design requirement of many practical multi-target tracking systems. For labeled random finite sets multi-object filters, prior work has been established to construct a labeled multi-object birt
Externí odkaz:
http://arxiv.org/abs/2307.06401
Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled random finite sets literature, Gib
Externí odkaz:
http://arxiv.org/abs/2306.15135
Autor:
Trezza, Giovanni, Chiavazzo, Eliodoro
In this study, we evaluate several classifiers and focus on selecting a minimal set of appropriate material features. Our objective is to propose and discuss general strategies for reducing the number of descriptors required for material classificati
Externí odkaz:
http://arxiv.org/abs/2304.07592
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 6, Iss 3, Pp 1597-1618 (2024)
We assume that a sufficiently large database is available, where a physical property of interest and a number of associated ruling primitive variables or observables are stored. We introduce and test two machine learning approaches to discover possib
Externí odkaz:
https://doaj.org/article/d981eb6008bc4d589cb2f0a76dd849fa
Autor:
Omar Sharif, Afnan Freije, Salwa Al-Thawadi, Dalal Alromaihi, Fida Alsaffar, Essam Juma, Faisal Abubaker, Abdulrahman Barakat, Mariam Alhammadi, Zeyad Mahmood, Suha Hejres, Hanan Matar, Alice Trezza, Mariangela Rondanelli, Simone Perna
Publikováno v:
Gastrointestinal Disorders, Vol 6, Iss 3, Pp 622-633 (2024)
Background/Objectives: Colorectal cancer incidence in Bahrain occurs at a ratio of 13.4–18.8 per 100,000 persons after age standardization. This study aims to explore the relationship between colorectal cancer/abnormalities and different lifestyle
Externí odkaz:
https://doaj.org/article/92e0633da6954b36a865076f0e9977e6
HGA Triggers SAA Aggregation and Accelerates Fibril Formation in the C20/A4 Alkaptonuria Cell Model.
Autor:
Mastroeni, Pierfrancesco1 (AUTHOR) p.mastroeni@student.unisi.it, Trezza, Alfonso1 (AUTHOR) alfonso.trezza2@unisi.it, Geminiani, Michela1 (AUTHOR) geminiani2@unisi.it, Frusciante, Luisa1 (AUTHOR) anna.visibelli2@unisi.it, Visibelli, Anna1 (AUTHOR) annalisa.santucci@unisi.it, Santucci, Annalisa1,2 (AUTHOR)
Publikováno v:
Cells (2073-4409). Sep2024, Vol. 13 Issue 17, p1501. 20p.