Variation fitness components

Figure 35 Top row XPS C(ls) and O(ls) band envelopes and curve-fitted components for (a) PMMA and (b) PEEK films A is untreated, B the DBD-treated at 5.7J cm-2 and C the post-treatment-aged (stored) sample. Bottom row contact angle and XPS O/C variation of (c) PMMA film and (d) PEEK film solid lines are for the freshly treated in air DBD samples, the dashed lines are for the post-process-aged films. Reprinted from Upadhyay et al. [98]. Copyright 2005, with permission of Elsevier.

In summary, there is good evidence that both optimal-world and cruel-world scenarios supply variation in fitness components, but the quantitative balance between the two is unclear. 

Density - Typically 900 kg/m (from 700 kg/m to 1(KX) kg/m at 20°C floats on water) linear temperature variation fit the density of spilled oil will also increase with time as the more volatile and less dense components are lost, so that after considerable evaporation, the density of some crude oils may increase enough for the oils to sink below the water surface. 

Once the variability risks, and q, have been calculated, the link with the particular failure mode(s) from an FMEA for each critical characteristic is made. However, determining this link, if not already evident, can be the most subjective part of the analysis and should ideally be a team-based activity. There may be many component characteristics and failure modes in a product and the matrix must be used to methodically work through this part of the analysis. Past failure data on similar products may be useful in this respect, highlighting those areas of the product that are most affected by variation. Variation in fit, performance or service life is of particular interest since controlling these kinds of variation is most closely allied with quality and reliability (Nelson, 1996). 

We have seen that PCR and RRR form two extremes, with CCA somewhere in between. RRR emphasizes the fit of Y (criterion ii). Thus, in RRR the X-components t, preferably should correlate highly with the original T-variables. Whether X itself can be reconstructed ( back-fitted ) from such components t, is of no concern in RRR. With standard PCR, i.e. top-down PCR, the emphasis is initially more on the X-side (criterion i) than on the T-side. CCA emphasizes the importance of correlation whether the canonical variates t and u account for much variance in each respective data set is immaterial. Ideally, of course, one would like to have the best of all three worlds, i.e. when the major principal components of X (as in PCR) and the major principal components of Y (as in RRR) happen to be very similar to the major canonical variables (as in CCA). Is there a way to combine these three desiderata — summary of X, summary of Y and a strong link between the two — into a single criterion and to use this as a basis for a compromise method The PLS method attempts to do just that. 

Figure 4. The uranium concentration in unfiltered water, 0.2 gm and 3 kD filtered water in river water from the Kalix River mouth and samples from the low salinity estuarine zone (0-3). Data plotted against conductivity (although the salinity scale is not defined below 2, a tentative scale is indicated). The lines represent the best fit for each fraction in the estuary. The data from the Kalix river mouth represent the river water component, which show <10% aimual variation in concentration. The analytical errors are smaller than the symbols. Data from Andersson et al. (2001). Copyright 2001 Elsevier Science.

The heavy elements carry clear excesses in a s-process component as can be seen from Figure 7. This has been demonstrated for Kr, Sr, Xe, Ba, Nd, Sm (Ott and Begemann 1990 Prombo et al. 1993 Lewis et al. 1994 Richter 1995 Hoppe and Ott 1997). The models can be made to fit very precisely the measured data (Lattanzio and Boothroyd 1997 Busso et al. 1999). Mo and Zr can occur as microcrystals of Mo-Zr-C within graphite grains. Typical s-process patterns are observed, with isotopic variations of about a factor of more than 5 (Nicolussi et al. 1997 Nicolussi et al. 1998a). 

Fig. 6.6 Variation of the static structure factor S(Q) measured on hPE-dPEE diblock copolymer chains (sample IV) as a function of the wave number Q. Temperature closed star 393 K, closed circles 403 K, closed square 413 K, inverted triangle 423 K, closed star 433 K, open triangle 433 K, open circle 453 K, open square 463 K. Solid lines represent the fit with a two-component static RPA approach (Eq. 6.12). (Reprinted with permission from [44]. Copyright 1999 American Institute of Physics)...

If (and only if) replicate experiments have been carried out on a system, it is possible to partition the sum of squares of residuals, SS, into two components (see Figure 6.10) one component is the already familiar sum of squares due to purely experimental uncertainty, 55. the other component is associated with variation attributed to the lack of fit of the model to the data and is called the sum of squares due to lack of fit, SS. ... 

FIGURE 13.12 Fits to percentage deviation in regionally averaged ozone in (a) North America, (b) Europe, and (c) the Far East after subtracting components due to the solar cycle, seasonal variations, the QBO, and atmospheric nuclear tests (adapted from Sto-larksi et al., 1992). 

The three coefficients in angular brackets are given in Table 4.8. It was previously found that for j, f = 0...4, higher-order terms in j(j + 1), /(/ + 1) are not needed. The coefficients are functions of separation, R, and can readily be fitted to an analytical expression of the form Eq. 4.39, with BXL = (00 al 10), 0 I lI l), (o AXL T, respectively. These coefficients are given in the lower part of Table 4.8. The results show that the j, j corrections amount to 10 or 15% for j, / = 1...3 for the main components, XL = 01 and 23, and even more for the lesser components. Since the associated spectral intensities vary as the squares of dipole strength, these variations are clearly significant for the spectra. 

---