activity
20022024
most citedConstraining Van der Waals quintessence by observations

63 citations · 89 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2024★ 1 cited

Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces

Giovanni Messuti, ortensia Amoroso, Ferdinando Napolitano +3

Deep learning models have demonstrated remarkable success in various fields, including seismology. However, one major challenge in deep learning is the presence of mislabeled examp…

q-bio.TO2013

Experimental investigation of the trachea oscillation and its role in the pitch production

G. Buccheri, E. De Lauro, S. De Martino +1

Several experiments have been performed to investigate the mechanical vibrations associated with trachea and larynx when Italian vowels are emitted. The mechanical measurements hav…

physics.plasm-ph2004★ 8 cited

Whistleron Gas in Magnetized Plasmas

Salvatore De Martino, Mariarosaria Falanga, Stephan I. Tzenov

We have studied the nonlinear dynamics of whistler waves in magnetized plasmas. Since plasmas and beam-plasma systems considered here are assumed to be weakly collisional, the poin…

astro-ph2004★ 63 cited

Constraining Van der Waals quintessence by observations

S. Capozziello, V. F. Cardone, S. Carloni +4

We discuss an alternative approach to quintessence modifying the usual equation of state of the cosmological fluid in order to see if going further than the approximation of perfec…

physics.flu-dyn2004★ 2 cited

Kinetic Derivation of the Hydrodynamic Equations for Capillary Fluids

S. De Martino, M. Falanga, S. I. Tzenov

Based on the generalized kinetic equation for the one-particle distribution function with a small source, the transition from the kinetic to the hydrodynamic description of many-pa…

physics.ao-ph2002★ 15 cited

The stochastic resonance mechanism in the Aerosol Index dynamics

S. De Martino, M. Falanga, L. Mona

We consider Aerosol Index (AI) time-series extracted from TOMS archive for an area covering Italy . The missing of convergence in estimating the embedding d…