Model semi-mechanistic

The empirical and fully mechanistic models reflect the extreme of both situations, either a huge reductionism or a complex description of the biological system. As each approach has its strengths and weaknesses, a hybrid of both (the so-called semi-mechanistic approach) was introduced into the field of clinical modeling and simulation. Within the area of M S these models are also often just referred to as mechanistic models . 

Overall, these models are closer to the empirical PD models, but major elements of the biological system are implemented. Semi-mechanistic models are mostly developed using the population approach and consequently they are data-driven and parameters are estimated from the data available. Parameters which cannot be estimated might be either fixed to biologically meaningful values or they are explored by other studies, including in vitro or preclinical in vivo studies. Overall, the number of parameters is still small, compared with mechanistic PD models and the majority of the parameters are estimated. 

Pending the development of appropriate mechanistic theoretical modeling, semi-empirical models derived from dedicated constant amplitude and variable amplitude tests are the most likely to lead to engineering design rules. 

Bulk and solution (and several suspension and precipitation polymerization systems) can be modeled as homogeneous single-phase systems, or using a monomer-rich and a polymer-rich phase. The level of model sophistication and detail depends upon the intended use of the model. By their nature, these mathematical models are semi-mechanistic or semi-empirical , indicating that they are based on polymerization mechanisms with the inevitable use of certain (often many) parameters. 

Guohua A, Morrie ME. 2012. A physiologically based pharmacokinetic model of mitoxantrone in mice and scale-np to humans a semi-mechanistic model on corporating DNA and protein binding. AAPS J 14 352-354. 

In 2005, Debarberis et al. developed a semi-mechanistic model applicable to the WWER RPV steels. The embrittlement mechanisms, matrix damage, Cu-precipitation and phosphorus segregation were considered, resulting in the following equation ... 

L. Debarberis, B. Acosta, F. Sevini, A. Kryukov, F. Gillemot, M. Valo, A. Nikolaev and M. Brumovsky, Role of nickel in a semi-mechanistic analytical model for radiation embrittlement of model alloys, Journal of Nuclear Materials, 336,2005, 210-216. 

Cluster Renewal Models Most mechanistic models for heat transfer in CFBs are extensions of the model of Mickley and Fairbanks (1955). Descending clusters and strands in the vicinity of the wall surface are modeled as homogeneous semi-infinite... 

In the literature there is only one serious attempt to develop a detailed mechanistic model of free radical polymerization at high conversions (l. > ) This model after Cardenas and 0 Driscoll is discussed in some detail pointing out its important limitations. The present authors then describe the development of a semi-empirical model based on the free volume theory and show that this model adequately accounts for chain entanglements and glassy-state transition in bulk and solution polymerization of methyl methacrylate over wide ranges of temperature and solvent concentration. 

A useful model should account for a reduction of kt and kp with increase in polymer molecular weight and concentration and decrease in solvent concentration at polymerization temperatures both below and above the Tg of the polymer produced. For a mechanistic model this would involve many complex steps and a large number of adjustable parameters. It appears that the only realistic solution is to develop a semi-empirical model. In this context the free-volume theory appears to be a good starting point. 

Mechanistic Approaches. Adequate and appropriate river-quality assessment must provide predictive information on the possible consequences of water and land development. This requires an understanding of the relevant cause and effect relationships and suitable data to develop predictive models for basin management. This understanding may be achieved through qualitative, semi-quantitative or quantitative approaches. When quantitative or semi-quantitative methods are not available the qualitative approach must be applied. Qualitative assessments involve knowledge of how basin activities may affect river quality. This requires the use of various descriptive methods. An example of this kind of assessment is laboratory evaluation of the extent to which increases in plant nutrients, temperature or flow may lead to accelerated eutrophication with consequent reduction of water quality. 

A mechanistic model for the kinetics of gas hydrate formation was proposed by Englezos et al. (1987). The model contains one adjustable parameter for each gas hydrate forming substance. The parameters for methane and ethane were determined from experimental data in a semi-batch agitated gas-liquid vessel. During a typical experiment in such a vessel one monitors the rate of methane or ethane gas consumption, the temperature and the pressure. Gas hydrate formation is a crystallization process but the fact that it occurs from a gas-liquid system under pressure makes it difficult to measure and monitor in situ the particle size and particle size distribution as well as the concentration of the methane or ethane in the water phase. 

Polymer production technology involves a diversity of products produced from even a single monomer. Polymerizations are carried out in a variety of reactor types batch, semi-batch and continuous flow stirred tank or tubular reactors. However, very few commercial or fundamental polymer or latex properties can be measured on-line. Therefore, if one aims to develop and apply control strategies to achieve desired polymer (or latex) property trajectories under such a variety of conditions, it is important to have a valid mechanistic model capable of predicting at least the major effects of the process variables. 

The model proposed by Brandt et al. is consistent with the experimental observations, reproduces the peculiar shape of the kinetic curves in the absence and presence of dioxygen reasonably well, and predicts the same trends in the concentration dependencies of t, p that were observed experimentally (80). It was concluded that there is no need to assume the participation of oxo-complexes in the mechanism as it has been proposed in the literature (88-90). However, the model provides only a semi-quantitative description of the reaction because it was developed at constant pH by neglecting the acid-base equilibria of the sulfite ion and the reactive intermediates, as well as the possible complex-formation equilibria between various iron(III) species. In spite of the obvious constraints introduced by the simplifications, the results shed light on the general mechanistic features of the reaction and could be used to identify the main tasks for further model development. 

For the most part, these difficulties still exist, since much of the data generated over the past 25 years has been empirical or semi-empirical. It is our view that progress towards a quantitative understanding of trace element uptake will require work in the areas that were identified by Jackson and Morgan 25 years ago. Such data should help refine the mechanistic (and hopefully predictive) models that will be required in order to quantitatively understand trace-element accumulation by aquatic organisms. 

The surface hopping study was rather expensive in terms of CPU time, and consequently large numbers of trajectories could not be run. This is important to obtain statistically converged dynamical properties. The main goal of the surface hopping study was thus not to obtain such information but to provide mechanistic insight into the photodissociation and subsequent relaxation processes. The semi-classical work in the full space of nuclear coordinates provides the important vibrational degrees of freedom that one needs to include in any quantum model of the nuclear motion. This will now be described. 

Ross and Shu [38], discussing the computer modelling of hydrocarbon pyrolysis for olefin production, classify reaction models in four categories in order of increasing sophistication empirical, semi-kinetic, stoichiometric and mechanistic. Most concepts of this classification are included in Table 3 with, however, a more classical meaning of the word stoichiometry. 

Consider now a multicompartment structure aiming not only to describe the observed data but also to provide a rough mechanistic description of how the data were generated. This mechanistic system of compartments is envisaged with the drug flowing between the compartments. The stochastic elements describing these flows are the transition probabilities as previously defined. In addition, with each compartment in this mechanistic structure, one can associate a retention-time distribution (a). The so-obtained multicompartment model is referred to as the semi-Markov formulation. The semi-Markov model has two properties, namely that ... 

This section proposes the use of a semi-Markov model with Erlang- and phase-type retention-time distributions as a generic model for the kinetics of systems with inhomogeneous, poorly stirred compartments. These distributions are justified heuristically on the basis of their shape characteristics. The overall objective is to find nonexponential retention-time distributions that adequately describe the flow within a compartment (or pool). These distributions are then combined into a more mechanistic (or physiologically based) model that describes the pattern of drug distribution between compartments. The new semi-Markov model provides a generalized compartmental analysis that can be applied to compartments that are not well stirred. 

Computer modeling of hydrocarbon pyrolysis is discussed with respect to industrial applications. Pyrolysis models are classified into four groups mechanistic, stoichiometric, semi-kinetic, and empirical. Selection of modeling schemes to meet minimum development cost must be consistent with constraints imposed by factors such as data quality, kinetic knowledge, and time limitations. Stoichiometric and semi-kinetic modelings are further illustrated by two examples, one for light hydrocarbon feedstocks and the other for naphthas. The applicability of these modeling schemes to olefins production is evidenced by successful prediction of commercial plant data. 

G. Maria and D.W.T. Rippin, Modified Integral Procedure (MIP) as a Reliable Short-Cut Method in Mechanistic Based ODE Kinetic Model Estimation Non-Isothermal and (Semi-)Batch Process Cases, Comp. Chem. Eng. 19 (1995) S709-S714. 

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