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Structured Markovian model

Erlang- and phase-type distributions provide a versatile class of distributions, and are shown to fit naturally into a Markovian compartmental system, where particles move between a series of compartments, so that phase-type compartmental retention-time distributions can be incorporated simply by increasing the size of the system. This class of distributions is sufficiently rich to allow for a wide range of behaviors, and at the same time offers computational convenience for data analysis. Such distributions have been used extensively in theoretical studies (e.g., [366]), because of their range of behavior, as well as in experimental work (e.g., [367]). Especially for compartmental models, the phase-type distributions were used by Faddy [364] and Matis [301,306] as examples of long-tailed distributions with high coefficients of variation. [Pg.231]

We propose to use as single-passage retention-time distributions the Ai Exp(fc) for the central compartment and the A2 Gam(A, p.) distribution for the peripheral compartment and we assume that all molecules are present in compartment 1 at initial time. According to (9.12), [Pg.232]


Figure 9.8 Structured Markovian model. Diffusion is expressed by means of h+, h, and compartments 1 to is. Erlang-type elimination is represented by means of ho and compartments is to m. The drug is given in compartment is and cleared from compartment m. Figure 9.8 Structured Markovian model. Diffusion is expressed by means of h+, h, and compartments 1 to is. Erlang-type elimination is represented by means of ho and compartments is to m. The drug is given in compartment is and cleared from compartment m.
The quantum theory of spectral collapse presented in Chapter 4 aims at even lower gas densities where the Stark or Zeeman multiplets of atomic spectra as well as the rotational structure of all the branches of absorption or Raman spectra are well resolved. The evolution of basic ideas of line broadening and interference (spectral exchange) is reviewed. Adiabatic and non-adiabatic spectral broadening are described in the frame of binary non-Markovian theory and compared with the impact approximation. The conditions for spectral collapse and subsequent narrowing of the spectra are analysed for the simplest examples, which model typical situations in atomic and molecular spectroscopy. Special attention is paid to collapse of the isotropic Raman spectrum. Quantum theory, based on first principles, attempts to predict the. /-dependence of the widths of the rotational component as well as the envelope of the unresolved and then collapsed spectrum (Fig. 0.4). [Pg.7]

It should be emphasized that for Markovian copolymers a knowledge of the values of structural parameters of such a kind will suffice to find the probability of any sequence Uk, i.e. for an exhaustive description of the microstructure of the chains of these copolymers with a given average composition. As for the composition distribution of Markovian copolymers, this obeys for any fraction of Z-mers the Gaussian formula whose covariance matrix elements are Dap/l where Dap depend solely on the values of structural parameters [2]. The calculation of their dependence on time, and the stoichiometric and kinetic parameters of the reaction system permits a complete statistical description of the chemical structure of Markovian copolymers to be accomplished. The above reasoning reveals to which extent the mathematical modeling of the processes of the copolymer synthesis is easier to perform provided the alternation of units in macromolecules is known to obey Markovian statistics. [Pg.167]

More complex systems which model real systems cannot be solved using purely analytical methods. For this reason we want to introduce in this Chapter a novel formalism which is able to handle complex systems using analytical and numerical techniques and which takes explicitly structural aspects into account. The ansatz can be formulated following the theory described below. In the present stochastic ansatz we make use of the assumption that the systems we will handle are of the Markovian type. Therefore these systems are well suited for the description in terms of master equations. [Pg.516]

Carbon-13 nuclear magnetic resonance was used to determine the molecular structure of four copolymers of vinyl chloride and vinylidene chloride. The spectra were used to determine both monomer composition and sequence distribution. Good agreement was found between the chlorine analysis determined from wet analysis and the chlorine analysis determined by the C nmr method. The number average sequence length for vinylidene chloride measured from the spectra fit first order Markovian statistics rather than Bernoullian. The chemical shifts in these copolymers as well as their changes in areas as a function of monomer composition enable these copolymers to serve as model... [Pg.90]

It should be emphasized that for the Markovian copolymers, the knowledge of these structure parameters will suffice for finding the probabilities of any sequences LZ, i.e., for a comprehensive description of the structure of the chains of such copolymers at their given average composition. As for the CD of the Markovian copolymers, for any fraction of Z-mers it is described at Z 1 by the normal Gaussian distribution with covariance matrix, which is controlled along with Z only by the values of structure parameters (Lowry, 1970). The calculation of their dependence on time and on the kinetic parameters of a reaction system enables a complete statistical description of the chemical structure of a Markovian copolymer. It is obvious therewith to which extent a mathematical modeling of the processes of the synthesis of linear copolymers becomes simpler when the sequence of units in their macromolecules is known to obey Markov statistics. [Pg.172]

Enantiomorphic Site with Chain-End Control. In the case of less stereoselective Cz-symmetric metallocene catalysts, the magnitude of chain-end control can be comparable to that of site control. In this case, obviously, the former has to be added to the model using Markovian statistics. The probability parameters are the same found for pure chain-end control p si re), i.e., the probability of insertion of a si monomer enantioface after a monomer inserted with the re face, p re si), p si si), and p re re). In this case, the metallocene chirality prevents the equiprob-ability of the si olefin insertion after a re inserted monomer (see structure on the left in Scheme 36) and re olefin insertion after a si inserted monomer (see structure on the right in Scheme 36). [Pg.414]

The memory kernel in (2.59), recall that v] represents a nonlocal-in-time integral operator, is a clear indication that subdiffusive transport is non-Markovian. Incorporating kinetic terms into a non-Markovian transport equation requires great care and is best carried out at the mesoscopic level. We show in Sect. 3.4 how to proceed directly at the level of the mesoscopic balance equations for non-Markovian CTRWs. Here we pursue a different approach. As stated above, if all processes are Markovian, then contributions from different processes are indeed separable and simply additive. As is well known, processes often become Markovian if a sufficiently large and appropriate state space is chosen. For the case of reactions and subdiffusion, the goal of a Markovian description can be achieved by taking the age structure of the system explicitly into account as done by Vlad and Ross [460,461]. This approach is equivalent to Model B, see Sect. 3.4. [Pg.48]

Both deterministic and stochastic models can be defined to describe the kinetics of chemical reactions macroscopically. (Microscopic models are out of the scope of this book.) The usual deterministic model is a subclass of systems of polynomial differential equations. Qualitative dynamic behaviour of the model can be analysed knowing the structure of the reaction network. Exotic phenomena such as oscillatory, multistationary and chaotic behaviour in chemical systems have been studied very extensively in the last fifteen years. These studies certainly have modified the attitude of chemists, and exotic begins to become common . Stochastic models describe both internal and external fluctuations. In general, they are a subclass of Markovian jump processes. Two main areas are particularly emphasised, which prove the importance of stochastic aspects. First, kinetic information may be extracted from noise measurements based upon the fluctuation-dissipation theorem of chemical kinetics second, noise may change the qualitative behaviour of systems, particularly in the vicinity of instability points. [Pg.273]

Littlewood (1979) explicitly takes into account the structure (i.e., the modules) of the software, and models the exchange of control between modules (time of sojourn in a module and target of exchange) using a semi-Markovian process. The failure rates of a given module can be obtained from the basic reliability models applied to the module interface failures are being modeled explicitly. This model can be used to study the integration process. [Pg.2306]

The configurational structure (stereoregularity) of 1-butene and of the higher polyolefins up to 1-nonene has been studied by NMR spectroscopy in solution [38, 39], interpreted with the aid of chemical shift calculations, consideration of the y effect and of the rotational isomeric state model of Flory. The evaluation of the results favors the bicatalytic sites model of polymerization [40] over simple Markovian statistics. In contrast to polypropylene, side-chain conformation also has to be considered. Comparison with alkane model compounds indicates that in meso-units of poly-1-butene, trans conformation of backbone is less favored than in isotactic polypropylene because of contiguous ethyl group interactions. Introduction of racemic units in both... [Pg.169]

Statistical copolymers refer to a class of copolymers in which the distribution of the monomer counits follows Markovian statistics [1,2]. In these polymeric materials, since the different chemical units are joined at random, the resulting polymer chains would be expected to encounter difficulties in packing into crystaUine structures with long-range order however, numerous experiments have shown that crystallites can form in statistical copolymers under suitable conditions [2], In this section, we will discuss the effects of counit incorporation on the solid-state structure and the crystallization kinetics in statistical copolymers. A number of thermodynamic models, which have been proposed to describe the equilibrium crystallization/melting behavior in copolymers, vill also be highlighted, and their applicability to describing experimental observations will be discussed. [Pg.328]


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