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20篇 您的检索式：作者名="Grigolini"

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	题名	作者	年代	出处	被引量
1	Scaling in non-stationary time series (Ⅱ):Teen birth phenomenon显示文摘	Ignaccolo M Allegrini P Grigolini P	2004	Physica A:Statistical Mechanics and its Applications2004，336，：	1
2	Fractional diffusion and Levy stable processes显示文摘	West B J Grigolini P Metzler R	1997	Physical Review E1997，55，1：	1
3	Maximizing information exchange between complex networks显示文摘	Bruce J. West Elvis L. Geneston Paolo Grigolini	2008	Physics Reports2008，，1：	1
4	Bistable flow driven by coloured gaussian noise: A critical study显示文摘	P. H?nggi F. Marchesoni P. Grigolini	1984	Zeitschrift für Physik B Condensed Matter1984，，4：	1
5	Time behaviour of non-linear stochastic processes in the presence of multiplicative noise: From Kramers’ to Suzuki’s decay显示文摘	Sandro Faetti Paolo Grigolini Fabio Marchesoni	1982	Zeitschrift für Physik B Condensed Matter1982，，4：	1
6	Scaling detection in time series: Diffusion entropy analysis 显示文摘	Scafetta N Grigolini P	2002	Phys Rev E2002，66，：	1
7	Collective behavior and evolutionary games= An introduction显示文摘	Perc M Grigolini P	2013	Chaos Solitons ~ Fractals2013，56，11：	1
8	Scaling detection in time series: Diffusion entropy analysis显示文摘	Seafetta N Grigolini P	2002	Physical Review E2002，66，03：	1
9	Scaling detection in time series: Diffusion entropy analysis显示文摘	Scafetta N Grigolini P	2002	Phys Rev E2002，66，：	1
10	Levy scaling: The diffusion entropy analysis applied to DNA sequences显示文摘	Scafetta N Latora V Grigolini P	2002	Phys Rev E2002，，：	1
11	Levy scaling: The diffusion entropy analysis applied to DNA sequences显示文摘	Scafetta N Latora V Grigolini P	2002	PhysicalReview E2002，66，03：	1
12	Classical and quantum complexity and non-extensive thermodynamics 显示文摘	Grigolini P Tsallis C West B	2002	Chaos Solitons and Fractals2002，13，3：	1
13	Anomalous diffusion associated with nonlinear fractional derivative Fokker- Planck-like equation: exact time-dependent solutions 显示文摘	Bologna M Tsallis C Grigolini P	2000	Physical Review E2000，62，2：	1
14	Fractional diffusion and Levy stable processes显示文摘	West B J Grigolini P Metzler R	1997	Physical Review E1997，55，1：	1
15	Scaling detection in time series:diffusion entropy analysis显示文摘	Scafetta N Grigolini P	2002	Physical Review E2002，66，03：	1
16	Scaling detection in time series: Diffusion entropy analysis显示文摘	Scafetta N Grigolini P	2002	Phys Rev E2002，66，：	1
17	Anomalous diffusion associated with nonlinear fractional derivativepar Fokker-Flanck-Like equation:Exact time-dependent solutions显示文摘	M Bologna C Tsallis P Grigolini	2000	Physical Review E2000，62，2：	1
18	Maximizing Information Exchange Between Complex Networks 显示文摘	West B J Geneston E L Grigolini P	2008	Physics Reports2008，468，1：	1
19	Power-law time distribution of large earthquakes显示文摘	Mega M S Allegrini P Grigolini P	2003	Physical Review Letters2003，90，18：	1
20	The allometric propagation of COVID-19 is explained by human travel显示文摘We analyzed the number of cumulative positive cases of COVID-19 as a function of time in countries around the World.We tracked the increase in cases from the onset of the pandemic in each region for up to 150 days.We found that in 81 out of 146 regions the trajectory was described with a power-law function for up to 30 days.We also detected scale-free properties in the majority of sub-regions in Australia,Canada,China,and the United States(US).We developed an allometric model that was capable of fitting the initial phase of the pandemic and was the best predictor for the propagation of the illness for up to 100 days.We then determined that the power-law COVID-19 exponent correlated with measurements of human mobility.The COVID-19 exponent correlated with the magnitude of air passengers per country.This correlation persisted when we analyzed the number of air passengers per US states,and even per US metropolitan areas.Furthermore,the COVID19 exponent correlated with the number of vehicle miles traveled in the US.Together,air and vehicular travel explained 70%of the variability of the COVID-19 exponent.Taken together,our results suggest that the scale-free propagation of the virus is present at multiple geographical scales and is correlated with human mobility.We conclude that models of disease transmission should integrate scale-free dynamics as part of the modeling strategy and not only as an emergent phenomenological property.	Rohisha Tuladhar Paolo Grigolini Fidel Santamaria	2022	Infectious Disease Modelling2022，7，1：	0

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