scholarly journals Crossover Scaling Functions in One Dimensional Dynamic Growth Models

1995 ◽  
Vol 74 (5) ◽  
pp. 730-733 ◽  
Author(s):  
John Neergaard ◽  
Marcel den Nijs
1998 ◽  
Vol 57 (5) ◽  
pp. 4997-5012 ◽  
Author(s):  
U. Alon ◽  
M. R. Evans ◽  
H. Hinrichsen ◽  
D. Mukamel

2020 ◽  
Vol 75 (2) ◽  
pp. 175-182
Author(s):  
Magdy E. Amin ◽  
Mohamed Moubark ◽  
Yasmin Amin

AbstractThe one-dimensional Ising model with various boundary conditions is considered. Exact expressions for the thermodynamic and magnetic properties of the model using different kinds of boundary conditions [Dirichlet (D), Neumann (N), and a combination of Neumann–Dirichlet (ND)] are presented in the absence (presence) of a magnetic field. The finite-size scaling functions for internal energy, heat capacity, entropy, magnetisation, and magnetic susceptibility are derived and analysed as function of the temperature and the field. We show that the properties of the one-dimensional Ising model is affected by the finite size of the system and the imposed boundary conditions. The thermodynamic limit in which the finite-size functions approach the bulk case is also discussed.


2002 ◽  
Vol 25 (4) ◽  
pp. 519-529 ◽  
Author(s):  
Alessandro Torcini ◽  
Paolo Politi

BIOMATH ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 1904167 ◽  
Author(s):  
Svetoslav Marinov Markov

New reaction network realizations of the Gompertz and logistic growth models are proposed. The proposed reaction networks involve an additional species interpreted as environmental resource. Some natural generalizations and modifications of the Gompertz and the logistic models, induced by the proposed networks, are formulated and discussed. In particular, it is shown that the induced dynamical systems can be reduced to one dimensional differential equations for the growth (resp. decay) species. The reaction network formulation of the proposed models suggest hints for the intrinsic mechanism of the modeled growth process and can be used for analyzing evolutionary measured data when testing various appropriate models, especially when studying growth processes in life sciences.


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