Radionuclide uptake and long term behavior of CS-137, CS-134 and K-40 in tree rings of spruce

1995 ◽  
Vol 194 (2) ◽  
pp. 277-282 ◽  
Author(s):  
G. Haas ◽  
R. Schupfner ◽  
A. Müller
Keyword(s):  
Tree Rings ◽  
Long Term Behavior ◽  
Radionuclide Uptake

Effects of acute seizures on cell proliferation, synaptic plasticity and long-term behavior in adult zebrafish

2021 ◽  
Vol 1756 ◽  
pp. 147334
Author(s):  
Charles Budaszewski Pinto ◽  
Natividade de Sá Couto-Pereira ◽  
Felipe Kawa Odorcyk ◽  
Kamila Cagliari Zenki ◽  
Carla Dalmaz ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Synaptic Plasticity ◽  
Adult Zebrafish ◽  
Acute Seizures ◽  
Long Term Behavior

Using Generalized Cell Mapping to Approximate Invariant Measures on Compact Manifolds

1997 ◽  
Vol 07 (11) ◽  
pp. 2487-2499 ◽  
Author(s):  
Rabbijah Guder ◽  
Edwin Kreuzer
Keyword(s):  
Dynamical Systems ◽  
Invariant Measures ◽  
Nonlinear Dynamical Systems ◽  
Powerful Method ◽  
Cell Mapping ◽  
Generalized Cell Mapping ◽  
Nonlinear Dynamical ◽  
Long Term Behavior ◽  
Mapping Theory

In order to predict the long term behavior of nonlinear dynamical systems the generalized cell mapping is an efficient and powerful method for numerical analysis. For this reason it is of interest to know under what circumstances dynamical quantities of the generalized cell mapping (like persistent groups, stationary densities, …) reflect the dynamics of the system (attractors, invariant measures, …). In this article we develop such connections between the generalized cell mapping theory and the theory of nonlinear dynamical systems. We prove that the generalized cell mapping is a discretization of the Frobenius–Perron operator. By applying the results obtained for the Frobenius–Perron operator to the generalized cell mapping we outline for some classes of transformations that the stationary densities of the generalized cell mapping converges to an invariant measure of the system. Furthermore, we discuss what kind of measures and attractors can be approximated by this method.

Long-Term Behavior of Wood-Concrete Composite Floor/Deck Systems with Shear Key Connection Detail

2007 ◽  
Vol 133 (9) ◽  
pp. 1307-1315 ◽  
Author(s):  
M. Fragiacomo ◽  
R. M. Gutkowski ◽  
J. Balogh ◽  
R. S. Fast
Keyword(s):  
Shear Key ◽  
Long Term Behavior

Attractors for multi-valued lattice dynamical systems with nonlinear diffusion terms

2021 ◽  
Author(s):  
Panpan Zhang ◽  
Anhui Gu
Keyword(s):  
Dynamical Systems ◽  
Nonlinear Diffusion ◽  
Upper Semicontinuity ◽  
Growth Conditions ◽  
Pullback Attractor ◽  
Random Lattice ◽  
Lipschitz Continuous ◽  
Lattice Dynamical Systems ◽  
Long Term Behavior

This paper is devoted to the long-term behavior of nonautonomous random lattice dynamical systems with nonlinear diffusion terms. The nonlinear drift and diffusion terms are not expected to be Lipschitz continuous but satisfy the continuity and growth conditions. We first prove the existence of solutions, and establish the existence of a multi-valued nonautonomous cocycle. We then show the existence and uniqueness of pullback attractors parameterized by sample parameters. Finally, we establish the measurability of this pullback attractor by the method based on the weak upper semicontinuity of the solutions.

scholarly journals The Implications of HIV Treatment on the HIV-Malaria Coinfection Dynamics: A Modeling Perspective

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
F. Nyabadza ◽  
B. T. Bekele ◽  
M. A. Rúa ◽  
D. M. Malonza ◽  
N. Chiduku ◽  
...  
Keyword(s):  
Hiv Treatment ◽  
Pathogen Transmission ◽  
Disease Epidemiology ◽  
Qualitative Dynamics ◽  
Potential Impact ◽  
Negative Impacts ◽  
Long Term Behavior ◽  
The Impact ◽  
Multiple Pathogens

Most hosts harbor multiple pathogens at the same time in disease epidemiology. Multiple pathogens have the potential for interaction resulting in negative impacts on host fitness or alterations in pathogen transmission dynamics. In this paper we develop a mathematical model describing the dynamics of HIV-malaria coinfection. Additionally, we extended our model to examine the role treatment (of malaria and HIV) plays in altering populations’ dynamics. Our model consists of 13 interlinked equations which allow us to explore multiple aspects of HIV-malaria transmission and treatment. We perform qualitative analysis of the model that includes positivity and boundedness of solutions. Furthermore, we evaluate the reproductive numbers corresponding to the submodels and investigate the long term behavior of the submodels. We also consider the qualitative dynamics of the full model. Sensitivity analysis is done to determine the impact of some chosen parameters on the dynamics of malaria. Finally, numerical simulations illustrate the potential impact of the treatment scenarios and confirm our analytical results.

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