scholarly journals Temporal evolution of mesoscopic structure of some non-Euclidean systems using a Monte Carlo model

2011 ◽  
Vol 83 (10) ◽  
Author(s):  
T. Mazumdar ◽  
S. Mazumder ◽  
D. Sen
Keyword(s):  
Monte Carlo ◽  
Temporal Evolution ◽  
Monte Carlo Model ◽  
Mesoscopic Structure

Monte Carlo model for describing charge transfer in irradiated CCDs

1998 ◽  
Author(s):  
Dennis J. Gallagher ◽  
Raymond Demara ◽  
Gary Emerson ◽  
Wayne W. Frame ◽  
Alan W. Delamere
Keyword(s):  
Monte Carlo ◽  
Charge Transfer ◽  
Monte Carlo Model

Monte Carlo model of multi-site adsorption

1985 ◽  
Vol 8 (7) ◽  
pp. 364-365 ◽  
Author(s):  
J. Sedláček ◽  
L. Nondek
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Model

scholarly journals Dynamical model for social distancing in the U.S. during the COVID-19 epidemic

2020 ◽  
Vol 8 (1) ◽  
pp. 141-149
Author(s):  
Shirish M. Chitanvis
Keyword(s):  
New York ◽  
Monte Carlo ◽  
Temporal Evolution ◽  
Social Experiment ◽  
Social Distancing ◽  
Back Testing ◽  
Moderate Growth ◽  
Log Normal ◽  
Very High ◽  
The U.S

AbstractBackground Social distancing has led to a “flattening of the curve” in many states across the U.S. This is part of a novel, massive, global social experiment which has served to mitigate the COVID-19 pandemic in the absence of a vaccine or effective anti-viral drugs. Hence it is important to be able to forecast hospitalizations reasonably accurately.Methods We propose on phenomenological grounds a random walk/generalized diffusion equation which incorporates the effect of social distancing to describe the temporal evolution of the probability of having a given number of hospitalizations. The probability density function is log-normal in the number of hospitalizations, which is useful in describing pandemics where the number of hospitalizations is very high.Findings We used this insight and data to make forecasts for states using Monte Carlo methods. Back testing validates our approach, which yields good results about a week into the future. States are beginning to reopen at the time of submission of this paper and our forecasts indicate possible precursors of increased hospitalizations. However, the trends we forecast for hospitalizations as well as infections thus far show moderate growth.Additionally we studied the reproducibility Ro in New York (Italian strain) and California (Wuhan strain). We find that even if there is a difference in the transmission of the two strains, social distancing has been able to control the progression of COVID 19.

scholarly journals Monte Carlo model for multiparticle production at ultrarelativistic energies

1995 ◽  
Vol 52 (1) ◽  
pp. 362-373 ◽  
Author(s):  
N. S. Amelin ◽  
H. Stöcker ◽  
W. Greiner ◽  
N. Armesto ◽  
M. A. Braun ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Model ◽  
Multiparticle Production

Payment Instruments, Financial Privacy and Online Purchases

2016 ◽  
Vol 15 (3) ◽  
Author(s):  
Yann Balgobin ◽  
David Bounie ◽  
Martin Quinn ◽  
Patrick Waelbroeck
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Monte Carlo Model ◽  
Personal Data ◽  
Purchasing Decisions ◽  
Online Purchases ◽  
Econometric Methods ◽  
Internet Users ◽  
Payment Instruments ◽  
Financial Privacy

AbstractThe protection of financial personal data has become a major concern for Internet users in the digital economy. This paper investigates whether the consumers’ use of non-bank payment instruments that preserve financial privacy from banks and relatives may increase their online purchases. We analyze the purchasing decisions and the use of bank and non-bank payment instruments of a representative sample of French Internet consumers in 2015. Using two econometric methods, namely a two-step regression and a Bayesian Markov Chain Monte Carlo model to account for a potential endogeneity problem, we find evidence that the use of a non-bank payment instrument positively influences consumers’ online purchases.

scholarly journals A Robust Monte Carlo Model for the Extraction of Biological Absorption and ScatteringIn Vivo

2009 ◽  
Vol 56 (4) ◽  
pp. 960-968 ◽  
Author(s):  
J.E. Bender ◽  
K. Vishwanath ◽  
L.K. Moore ◽  
J.Q. Brown ◽  
V. Chang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Model
Sign in / Sign up

Export Citation Format

Share Document