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Model aggregation

FIGURE 22.6 Payne effect of butyl composites with various amounts of N330, as indicated (left) [28]. Scaling behavior of the small-strain modulus of the same composites right). The obtained exponent 3.5 confirms the cluster-cluster aggregation model. (From Kliippel, M. and Heinrich, G., Kautschuk, Gummi, Kunststoffe, 58, 217, 2005. With permission.)... [Pg.617]

Derivations of other aggregation models are described elsewhere [8, 10, 16, 20, 31]. [Pg.73]

Burd AB, Moran SB, Jackson, GA (2000) A coupled adsorption-aggregation model of the POC/ Th ratio of marine particles. Deep-Sea Res 147 103-120... [Pg.488]

The direct relevance of these diffusion-driven aggregation models to /tow-driven aggregation is somewhat questionable, though they serve to highlight the influence of the aggregation process on the structures produced. These models have been used to synthesize structures to investigate how they break (Horwatt et al. 1992a,b). [Pg.181]

Horton, P., Wentworth, M., and Ruban, A. 2005. Control of the light harvesting function of chloroplast membranes The LHCII-aggregation model for non-photochemical quenching II. FEBS Lett. 579 4201 1206. [Pg.134]

Application of Aggregation Models in Reducing the Binary Dimension... [Pg.34]

Table 2.7 gives the computational results from the aggregation model. [Pg.35]

Table 2.7 Results for the second literature example using aggregation models... [Pg.36]

AM = aggregation model GM = general model NTP = number of time points NC = number of constraints NV = total number of variables NB = number of binary variables... [Pg.36]

Lastly, this chapter presents the concept of aggregation as a means of reducing the binary dimension in large-scale problems. In the examples cited, the objective values predicted by the aggregation model were very close to those predicted by the general formulation. However, the aggregation model requires a much smaller number of binary variables which is concomitant with significantly reduced computational effort. [Pg.37]

Equations 27 and 28 present the extension of the Szyszkowski-Langmuir model to the adsorption of one-surfactant systems with aggregation at the interface. For the formation of dimmers on the surface, n = 2 and Eqs. 27 and 28 can be expanded to obtain the Frumkin equation of adsorption state. In general, the surface aggregation model described by Eqs. 27 and 28 contains four free parameters, including coi, n, b and Fc, which can be obtained by regression analysis of the data for surface tension versus surfactant concentration in the solution. [Pg.34]

It is noted that the investigation of a mixed adsorption layer of CioEs and TPeAB (tetrapentyl ammoniiun bromide) [35] shows evidence for attractive forces / > 0), which suggests that the presence of the ionic surfactant can prevent aggregation in the extended S-L adsorption layer. Therefore, the main question of interest concerns how the Frumkin model and the aggregation model are related. One can find from Eq. 29 that the size of the elementary adsorption cell increases with the aggregation munber resulting in a reduction in the munber of cells. Negative has the same effect of de-... [Pg.42]

Blandamer et al. (4) have recently made a half-hearted effort to resurrect the e2 2 species. They state that if a species such as M2 or M contains two ammoniated electrons with sufficient overlap of the electronic wave functions to cause the species to exist in a singlet state, then only by a coincidence could the absorption spectrum be similar to that of the far-separated ammoniated electrons. They suggest that a comparable coincidence could just as well occur in the case of the e2 2 species. Some clarification of the ionic aggregate model is therefore needed. It should be recognized that the optical absorption peak does shift slightly... [Pg.35]

Dannenberg J J. 2002. Coopertivity in hydrogen bonded aggregates. Models for crystals and peptides. J Mol Struct 615 219-226. [Pg.109]

Besides the general criticism of additive aggregation models the most criticized aspects about these simple methods are the fact that direct scoring of alternatives and direct estimation of weights often lead to results that lack an argumentative justification and that the resulting preference values lack an economic interpretation. Also, it is not possible to perform consistency checks on the decision maker s inputs. [Pg.137]

Caballero JA, Grossmann IE. Aggregated models for integrated distillation systems. Ind Eng Chem Res 1999 38 2330. [Pg.454]

There are also many similarities and interconnections among most of the 10 studies. The earliest study (Osteen and Kuchler, 1986) and several others (NAPIAP, 1992 Danielson et al., 1993 and Pike et al., 1994) used similar information compiled by similar groups of weed scientists. However, the information for the Osteen and Kuchler study was based on conditions and herbicides available in 1985, while the latter three studies used information from 1992 to 1993. The two Battelle studies (funded by Ciba Crop Protection) and the three CEEPES-based studies (CEEPES, 1993,1994 Ribaudo and Bouzaher, 1994) are more distinct because they used different farm level data and different aggregate models. CEEPES was designed to evaluate various agricultural policy tradeoffs in 15 Midwest states. The three triazine studies based on the CEEPES model were partially financed by USDA and USEPA. [Pg.155]


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See also in sourсe #XX -- [ Pg.70 ]

See also in sourсe #XX -- [ Pg.259 ]




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Aggregate model

Aggregate model application

Aggregate model limitation

Aggregated models

Aggregates, colloidal dynamic modeling

Aggregation Smoluchowski model

Aggregation efficiency, modeling

Aggregation frequencies, modeling

Aggregation models computational results

Aggregation of Colloids Recent Developments in Population Balance Modeling

Aggregation, computer models

Aggregation, molecular orbital modeling

Aggregation-field effect model

Aggregative growth model

Application of the Aggregate Model

Catalysis by micelles, membranes and other aqueous aggregates as models of enzyme

Catalysis by micelles, membranes and other aqueous aggregates as models of enzyme action

Computer modeling, aggregate shape

Diffusion-limited-aggregation model

Enzyme action, catalysis by micelles, membranes and other aqueous aggregates models

Enzyme action, catalysis of micelles, membranes and other aqueous aggregates as models

Hard-Sphere Aggregation Models

Inter-aggregates distances model

Irreversible aggregation models

Irreversible protein aggregation, model

Kraus model aggregates

Linear Programming Model for Aggregate Planning

Liquid crystalline polymers aggregate model

Micelles, membranes and other aqueous aggregates, catalysis by, as models

Micelles, membranes and other aqueous aggregates, catalysis by, as models enzyme action

Modelling for aggregation

Models aggregate, solid fraction

Models aggregates flocculation

Network Models of Ion Aggregation

Network, aggregate model

Nonlinear Programming Model for Aggregate Planning

Shrinking aggregate model

Surfactant aggregation model

The Aggregation Model

The Random Micelle Aggregation Model for Sphere-to-Rodlike Transition

The aggregate model and mechanical anisotropy

The aggregate model for chain-extended polyethylene and liquid crystalline polymers

The pseudo-affine aggregate model

Ward aggregate model

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