Zobrazeno 1 - 10
of 350
pro vyhledávání: '"Federico, Ricci"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Is Stochastic Gradient Descent (SGD) substantially different from Metropolis Monte Carlo dynamics? This is a fundamental question at the time of understanding the most used training algorithm in the field of Machine Learning, but it received
Externí odkaz:
https://doaj.org/article/11b517cd5601424d83335783f294c3f0
Autor:
Tito Brambullo, MD, Roberta Carpenito, MD, Federico Ricci, MD, Vincenzo Vindigni, Prof, Franco Bassetto, Prof
Publikováno v:
Plastic and Reconstructive Surgery, Global Open, Vol 12, Iss 7, p e5970 (2024)
Background:. Currently, the evaluation of pigmented skin lesions relies on dermoscopy, which has become the standard of care. As melanoma is one of the principal areas of expertise in plastic surgery, it is essential that diagnostic skills be acquire
Externí odkaz:
https://doaj.org/article/8f6ebd6e26a945a2a476a72cecad5aa8
Publikováno v:
Regenerative Therapy, Vol 25, Iss , Pp 302-307 (2024)
Introduction: Addressing post traumatic lower limb neuropathic pain is challenging across medical specialties. To address this potentially devastating condition, several invasive and non-invasive approaches have been proposed with inconsistent result
Externí odkaz:
https://doaj.org/article/bddf474c05b54170a4b977a471f12d67
Publikováno v:
JPRAS Open, Vol 39, Iss , Pp 32-41 (2024)
Summary: Background: Temporal migraines (TM) present with throbbing, pulsating headaches in the temporal area. Different surgical techniques ranging from resecting the auriculotemporal nerve (ATN) and or ligating the superficial temporal artery (STA)
Externí odkaz:
https://doaj.org/article/a6dea38441ad4e6f8e91922268348333
Publikováno v:
Applied Sciences, Vol 14, Iss 21, p 9707 (2024)
The increasing demand for vehicles is leading to a rise in pollutant emissions across the world. This decline in air quality is significantly impacting public health, with internal combustion engines being a major contributor to this concerning trend
Externí odkaz:
https://doaj.org/article/93f799d37938415d911eee99c8198a4b
Autor:
Marco Lombardo, Federico Ricci, Andrea Cusumano, Benedetto Falsini, Carlo Nucci, Massimo Cesareo
Publikováno v:
Diagnostics, Vol 14, Iss 20, p 2289 (2024)
Objectives: The main objective of this study was to report and investigate the characteristics and longitudinal changes in dark-without-pressure (DWP) fundus lesions in patients with autoimmune diseases using multimodal imaging techniques. Methods: I
Externí odkaz:
https://doaj.org/article/232c98cc588449b4b27eccd0efc7b558
Publikováno v:
Energies, Vol 17, Iss 16, p 3932 (2024)
The automotive industry is increasingly challenged to develop cleaner, more efficient solutions to comply with stringent emission standards. Hydrogen (H2)-powered internal combustion engines (ICEs) offer a promising alternative, with the potential to
Externí odkaz:
https://doaj.org/article/1ea3c22936f64d07b075ff158783fd07
Publikováno v:
Vehicles, Vol 5, Iss 3, Pp 1104-1117 (2023)
Innovative solutions are now being researched to manage the ever-increasing amount of data required to optimize the performance of internal combustion engines. Machine learning approaches have shown to be a valuable tool for signal prediction due to
Externí odkaz:
https://doaj.org/article/8f4b117119854f17a0968ba9deb03f7e
Analysis of Hydrogen Combustion in a Spark Ignition Research Engine with a Barrier Discharge Igniter
Autor:
Federico Ricci, Jacopo Zembi, Massimiliano Avana, Carlo Nazareno Grimaldi, Michele Battistoni, Stefano Papi
Publikováno v:
Energies, Vol 17, Iss 7, p 1739 (2024)
Hydrogen fuel is gaining particular attention in internal combustion engines. In addition to zero-carbon emissions, major advantages relate to its combustion characteristics, which allow a significant increase in thermal efficiency under ultra-lean o
Externí odkaz:
https://doaj.org/article/b4f51470d7074ba7b79d831b0295167c
Autor:
Federico Ricci, Francesco Mariani
Publikováno v:
Energies, Vol 17, Iss 7, p 1759 (2024)
This research explores the detection of flame front evolution in spark-ignition engines using an innovative neural network, the autoencoder. High-speed camera images from an optical access engine were analyzed under different air excess coefficient
Externí odkaz:
https://doaj.org/article/7b0467ef33aa4289bed8714f5b610850