Parameter, adjustable, effect

The volume of tissue activated (VTA) by DBS is still the object of debate. Reference [12] developed a model to predict the effect of electrode location and stimulation parameters adjustments on tbe VTA and correlated the results of the model with clinical results recorded in a patient They showed that therapeutic DBS of the STN is characterized by a VTA that spreads well outside the borders of the STN (toward the internal capsule and thalamus). By increasing the voltage and keeping other parameters constant they observed that while rigidity continued to decrease, bradykinesia improved and then worsened (inverted U-shape) and side effects such as paresthesia appeared suggesting that both sensorimotor signs and side effects react differently to stimulation voltage. 

Because the landscape of real proteins is unknown, most of the results we describe in Section III rely on assumptions discussed in Section II. The results are presented for a range of different theoretical landscapes—for example, the random energy model and the uncoupled case—with the assumptions that the real protein landscape lies between these bounds and can be described statistically. Determining the most effective combination of parameters, adjusting them according to the landscape fea-... 

The extraction of toluene and 1,2 dichlorobenzene from shallow packed beds of porous particles was studied both experimentally and theoretically at various operating conditions. Mathematical extraction models, based on the shrinking core concept, were developed for three different particle geometries. These models contain three adjustable parameters an effective diffusivity, a volumetric fluid-to-particle mass transfer coefficient, and an equilibrium solubility or partition coefficient. K as well as Kq were first determined from initial extraction rates. Then, by fitting experimental extraction data, values of the effective diffusivity were obtained. Model predictions compare well with experimental data and the respective value of the tortuosity factor around 2.5 is in excellent agreement with related literature data. 

The first two examples show that the interaction of the model parameters and database parameters can lead to inaccurate estimates of the model parameters. Any use of the model outside the operating conditions (temperature, pressures, compositions, etc.) upon which the estimates are based will lead to errors in the extrapolation. These model parameters are effectively no more than adjustable parameters such as those obtained in linear regression analysis. More complicated models may have more subtle interactions. Despite the parameter ties to theory, they embody not only the uncertainties in the plant data but also the uncertainties in the database. 

This is suggestive of the calculation of a partition function for a modest-sized set of states with effective interactions. The interactions are n-functional interactions, in contrast to density functional (or p-functional) theories, but with strength parameters adjusted to conform to the data available. A standard procedure for obtaining the Lagrange multipliers is to minimize the function... 

Figure 2 Viscosity as a function of chain length, reduced with entanglement degree of chain length The points correspond to the theory allowing for fluctuations in the entangled path length of chains showing without adjustable parameters an effective exponent of 3.4. The line with a slope of 3.0 corresponds to classical reptation theory. Inset Plotted as tjN versus NINg, crossover occurs at around NINe 200, as in experiment d...

Results from KMC simulations carried out with a detailed chemistry model were reported in Ref. 1 The KMC simulation is able to capture qualitative experimental features without parameter adjustment. However, simulated length scales are slightly short, and finite-size effects probably affect the longer time evolution. Furthermore, the time scales predicted were longer than the experimental ones (dispersive NEX-AFS data show that the difference in time scales is probably smaller ). 

In all single-crystal studies, the variation in resonance frequency or magnetic field is studied as a function of the orientation of the crystal in the magnetic field. A spin Hamiltonian of appropriate form is then solved and the parameters adjusted to fit the calculated variation with the experimental data. Most errors in doing this occur because approximate solutions of spin Hamiltonians are used for systems for which the approximations are not justified. Second-order effects are often very important in analyzing single-crystal ESR and ENDOR measurements. 

The effect of the molecular weight of the PPS and parameters adjusted in the melt spinning process on the properties of the final fibers have been elucidated. Structure-property-relationships were established by the use of tensile testing, differential scanning calorimetry, polarized light optical microscopy, and wide-angle X-ray scattering. 

A second distinction is to be made, this not on the objective function, but rather on the mechanism through which adaption is introduced. I f enough is known about a process that parameter adjustments can be related to the variables which cause its properties to change, adaption may be programmed. However, if it is necessary to base parameter manipulation upon the measured value of the objective function, adaption is effected by means of a feedback loop. This is known as a self-adaptive system. 

Figure Levels of 6-hydroxynorleucine (p<0.0001) and 2-aminoadipic acid (p<0.014) versus chronological age in water insoluble lens protein fractions (WI) from non-diabetic individuals, and effects of diabetes on levels of 6-hydroxynorleiicine (p <0.0001) and 2-aminoadipic acid (n.s) in water insoluble fraction (WI) upon adjustment to age SOyrs. (A) 6-hydroxynorleucine vs. chronological age Y=0.38x+12.81, n=30, r=0.67, p<0.0001 (B) 2-aminoadipic acid V5. chronological age. y=0.66x- 8.25, n-23, r=0.49, p< 0.01. Regression line equations where x=age andy=parameter. (C) Effect of diabetes. Student t test was used to compare two groups, p value below 0.05 was considered significant. Bars represent means S.D.

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