Distribution dynamic systems

Variance, 269 of a distribution, 120 significance of, 123 of a Poisson distribution, 122 Variational equations of dynamical systems, 344 of singular points, 344 of systems with n variables, 345 Vector norm, 53 Vector operators, 394 Vector relations in particle collisions, 8 Vectors, characteristic, 67 Vertex, degree of, 258 Vertex, isolated, 256 Vidale, M. L., 265 Villars, P.,488 Von Neumann, J., 424 Von Neumann projection operators, 461... 

Spatially distributed reacting systems can be described by a generalization of MPC dynamics that incorporates stochastic birth-death reactive events in the collision step. For simplicity, consider a single reaction among a set of s species Xa, (a = 1,..., j) ... 

Reactive MPC dynamics should prove most useful when fluctuations in spatially distributed reactive systems are important, as in biochemical networks in the cell, or in situations where fluctuating reactions are coupled to fluid flow. 

The moments of transition time of a dynamical system driven by noise, described by arbitrary potential cp(x) such that cp( oo) = oo, symmetric relatively to some point x = d, with initial delta-shaped distribution, located at the point xo < d [Fig. A 1(a)], coincides with the corresponding moments of the first passage time for the same potential, having an absorbing boundary at the point of symmetry of the original potential profile [Fig. A 1(b)]. 

It is clear that the strong form of the QCT is impossible to obtain from either the isolated or open evolution equations for the density matrix or Wigner function. For a generic dynamical system, a localized initial distribution tends to distribute itself over phase space and either continue to evolve in complicated ways (isolated system) or asymptote to an equilibrium state (open system) - whether classically or quantum mechanically. In the case of conditioned evolution, however, the distribution can be localized due to the information gained from the measurement. In order to quantify how this happ ens, let us first apply a cumulant expansion to the (fine-grained) conditioned classical evolution (5), resulting in the equations for the centroids (x = (t), P= (P ,... 

A spectrum is the distribution of physical characteristics in a system. In this sense, the Power Spectrum Density (PSD) provides information about fundamental frequencies (and their harmonics) in dynamical systems with oscillatory behavior. PSD can be used to study periodic-quasiperiodic-chaotic routes [27]. The filtered temperature measurements y t) were obtained as discrete-time functions, then PSD s were computed from Fast Fourier Transform (FFT) in order to compute the fundamental frequencies. 

In terms of nonlinear dynamical systems, the second waveguide of the junction can be considered as a system that is initially more or less far from its stable point. The global dynamics of the system is directly related to the spatial transfomation of the total field behind the plane of junction. In structure A, the initial linear mode transforms into a nonlinear mode of the waveguide with the same width and refractive index. In the structure B, the initial filed distribution corresponds to a nonlinear mode of the first waveguide it differs from nonlinear mode of the second waveguide, however. The dynamics in both cases is complicated and involves nonlinear modes as well as radiation. Global dynamics of a non-integrable system usually requires numerical simulations. For the junctions, the Cauchy problem also cannot be solved analytically. 

There exists a class of dynamical systems, whose distribution function p obeys the (deterministic) Liouville equation, for which one can prove that, as a result of a... 

The authors then ask the following question Do there exist deterministic dynamical systems that are, in a precise sense, equivalent to a monotonous Markov process The question can be reformulated in a more operational way as follows Does there exist a similarity transformation A which, when applied to a distribution function p, solution of the Liouville equation, transforms the latter into a function p that can also be interpreted as a distribution function (probability density) and whose evolution is governed by a monotonous Markov process An affirmative answer to this question requires the following conditions on A (MFC) ... 

Elizabetli, C.M., Della, P., Oscar Ploeger, B.A. and Voskuyl, R.A. (2007) Mechanism-based pharmacokinetic-pharmacodynamic modeling hiophase distribution, receptor theory, and dynamical systems analysis. Annual Review of Pharmacology and Toxicology, 47, 357-400. 

The fast stage of relaxation of a complex reaction network could be described as mass transfer from nodes to correspondent attractors of auxiliary dynamical system and mass distribution in the attractors. After that, a slower process of mass redistribution between attractors should play a more important role. To study the next stage of relaxation, we should glue cycles of the first auxiliary system (each cycle transforms into a point), define constants of the first derivative network on this new set of nodes, construct for this new network an (first) auxiliary discrete dynamical system, etc. The process terminates when we get a discrete dynamical system with one attractor. Then the inverse process of cycle restoration and cutting starts. As a result, we create an explicit description of the relaxation process in the reaction network, find estimates of eigenvalues and eigenvectors for the kinetic equation, and provide full analysis of steady states for systems with well-separated constants. 

Again we should analyze, whether this new cycle is a sink in the new reaction network, etc. Finally, after a chain of transformations, we should come to an auxiliary discrete dynamical system with one attractor, a cycle, that is the sink of the transformed whole reaction network. After that, we can find stationary distribution by restoring of glued cycles in auxiliary kinetic system and applying formulas (11)-(13) and (15) from Section 2. First, we find the stationary state of the cycle constructed on the last iteration, after that for each vertex Ay that is a glued cycle we know its concentration (the sum of all concentrations) and can find the stationary distribution, then if there remain some vertices that are glued cycles we find distribution of concentrations in these cycles, etc. At the end of this process we find all stationary concentrations with high accuracy, with probability close to one. 

For kinetic systems with well-separated constants the left and right eigenvectors can be explicitly estimated. Their coordinates are close to +1 or 0. We analyzed these estimates first for linear chains and cycles (5) and then for general acyclic auxiliary dynamical systems (34), (36) (35), (37). The distribution of zeros and +1 in the eigenvectors components depends on the rate constant ordering and may be rather surprising. Perhaps, the simplest example gives the asymptotic equivalence (for of the reaction network A,+2 with... 

When one considers the distribution of trace components in dynamic systems, it must also be accepted that the distribution will not necessarily be uniform either in space or time. Nevertheless, most people are attuned to a sense that analytical measurements should be highly repeatable. In consequence, there is far too often a strong tendency to discount or eliminate from consideration, measurements in a sample series which deviate... 

Section 6.3 treats distributed nonreacting systems and specifically packed bed absorption, while Section 6.4 studies a battery of nonisothermal CSTRs and its dynamic behavior. 

Models are either dynamic or steady-state. Steady-state models are most often used to study continuous processes, particularly at the design stage. Dynamic models, which capture time-dependent behavior, are used for batch process design and for control system design. Another classification of models is in terms of lumped parameter or distributed parameter systems. A lumped parameter system... 

Danhof, M., de Jongh, J., De Lange, E. C., Della Pasqua, O., Ploeger, B. A., Voskuyl, R. A. Mechanism-based pharmacokinetic-pharmacodynamic modeling biophase distribution, receptor theory, and dynamical systems analysis. Anna Rev Pharmacol Toxicol 2007,47 357-400. 

---