Small parameters exclusion

In the described MC simulation, the action of several simultaneous sources of variation is considered. The explanation of the different time courses of parameter influence on volume size between sensitivity and MCCC analyses lies in the fact that classic sensitivity analysis considers variations in model output due exclusively to the variation of one parameter component at a time, all else being equal. In these conditions, the regression coefficient between model output and parameter component value, in a small interval around the considered parameter, is approximately equal to the partial derivative of the model output with respect to the parameter component. 

The experimental techniques for the study of conformational branched properties in solution are the same as used for linear chains. These are, in particular, static and dynamic light scattering, small angle X-ray (SAXS) and small angle neutron (SANS) scattering methods, and common capillary viscometry. These methods are supported by osmotic pressure measurements and, nowadays extensively applied, size exclusion chromatography (SEC) in on-line combination with several detectors. These measurements result in a list of molecular parameters which are given in Table 1. 

Ksec = 0 when the molecule hydrodynamic radius is higher than the mean pore diameter. KSEC is 1 with small molecules, which can easily penetrate into the pores. The most important parameters influencing resolution are the pore volume, pore size distribution, and particle size. The separation domain is between the exclusion volume Va and the inclusion volume ( V0 + Vp). 

The first fraction of the previous equation shows that An is exclusively determined by the effective permeability Peff of drug since all other variables are species-dependent physiological parameters. In terms of characteristic times, the An of a drug can also be defined as the ratio of the mean small intestinal transit time (Tsi), to its absorption time R/Peff. 

In these studies, the input sequence was represented by seven non-orthogonal physicochemical properties, namely, hydrophobicity, volume, surface area, hydrophilicity, bulkiness, refractivity, and polarity, with normalized values of (-1, 1). Thirteen-amino acid sequence windows were used, resulting in an input vector of 91 (i.e., 13 x 7) units. With the small number of training examples, the input units were only partially connected to the hidden layer in order to reduce the number of free parameters and avoid overtraining the network. The constraint was to connect each first hidden layer unit to one amino acid property exclusively, and that the size of the second hidden layer size was no larger than the first hidden layer. The output layer had exactly one unit to indicate whether the middle residue was in a membrane/non-membrane boarder. The process of development started with randomly generated architectures and resulted in 18 and 8 units in the first and second hidden layers, with a total number of free parameters of... 

Thus after exclusion of the bare interaction from the = 0 term of the real-space sum, that contribution yields a constant. In practical calculations it is common to use a convergence parameter of 17 5.6/jC, in which case the first term on the right side of Eq. (5.43) is negligibly small, and we shall omit this term in the discussion to follow. The numerical value of for a cubic lattice is approximately —2.837297 (see the Exercises). The choice of Eq. (5.39) thus removes the interaction of each ion with the charge backgrounds in this energy... 

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