Data interpretation tables

All packing correlation methods for hydraulics have systemic limitations. These limits arise from the underlying correlation forms and the difficulty in data interpretation. Table 9 lists applicability ranges for Fig. 7. Strigle and Kister and Gill both have excellent discussions of the limitations of the method shown. In summary, the limitations are ... 

Table 1 Influence of various factors on the use of AI techniques for NDT data interpretation.

In order to interpret the data in Table 5.1 in a quantitative fashion, we analysed the kinetics in terms of the pseudophase model (Figure 5.2). For the limiting cases of essentially complete binding of the dienophile to the micelle (5.If in SDS and 5.1g in CTAB solution) the following expression can be derived (see Appendix 5.2) ... 

In such a process, the water molecule fonned in the elimination step is captured primarily fiom the fixmt side, leading to net retention of configuration for the alcohol. For the ester, the extent of retention and inversion is more balanced, although it vari among individual systems. It is clear om die data in Table 5.18 that the two pairs of stereoisomeric amines do not form the same intermediate, even though a simple mechanistic interpretation would sugg that both would fmm the 2-decalyl cation. The coUap of the ions to product is pvidoitly so rapid that diere is not time for relaxation of the initially formed intermediates to reach a common stnicture. 

The important observation from the data in Table 3 is that NC samples subjected to acid bod followed by tetrahydrofuran (THF)-benzene treatment yielded essentially the same sulfate contents as samples subjected to THF-benzene treatment alone. From this the authors interpret that sulfate contents from THF-benzene treatments actually represent absolute values of the difficult-to-remove sulfate which may very well be true sulfate ester . It is also tentatively concluded that approximately 90% of the original sulfate content in unstabilized NC is readily removable sulfuric acid with the remainder the more difficult-to-remove sulfate ester. Dilute acid boil treatment of NC for 56 hours does not eliminate all of the free sulfuric acid and leaves the difficult-to-remove sulfate practically unchanged... 

Use data in Table 7.3 or Appendix 2A to calculate the standard reaction entropy for each of the following reactions at 25°C. For each reaction, interpret the sign and magnitude of the reaction entropy, (a) The formation of... 

Both the reactors are operated in batch, and the concentrations of components involved are measured online by electro-conductivity. Data interpretation is made by the kinetic equation of second order. The results obtained in the range of 25-45"C are given in Table 3. Again, the values for the rate constant measured in SCISR, ks, are S5 tematically higher than those in STR, ksr, by about 20%, and no significant difference betvi een the values for the active energy measured in SCISR and STR has been found. 

The model induced via the decision tree is not a blackbox, and provides explicit and interpretable rules for solving the pattern classification problem. The most relevant variables are also clearly identified. For example, for the data in Table I, the value of the temperature are not necessary for obtaining good or bad quality, as is clearly indicated by the decision tree in Fig. 22. 

Table 6 Soil characterization results used in water balance calculations and data interpretations...

The data in each table and figure of reports submitted to sponsors should be verified by QA personnel. Team leaders, laboratory managers, field managers and the Study Director should meet routinely to discuss the meaning of the data as the study develops. This allows early discussions regarding data interpretation and allows several viewpoints to be explored, which ultimately strengthen the final report for the study. 

The main characteristics of FAB-MS are indicated in Table 6.15. FAB ionisation is relatively simple to perform. However, parameter optimisation and data interpretation of the resulting FAB spectra can be complex. Matrix selection for additive analysis is crucial. Solubility of the additives in the matrix is essential for production of viable spectra. FAB/FIB is well suited to organic compounds which exhibit some polarity, and contain either acidic and/or basic functional groups. Compounds with basic groups run well in positive ionisation mode, and those with acidic centres run well in the negative ionisation... 

The available data for deriving dose-response relationships for 144Ce are relatively limited. A complicating feature is that the spectrum of diseases produced is dependent upon the form of the 144Ce entering the body and the resultant radiation dose. This is apparent from data in Table 23 in which several different and competing diseases were produced by inhaled 144CeCl3. Additional factors that confuse the interpretation of internal emitter dose-response studies in laboratory... 

The data in Table 5 were used (Miles et al., 1966) to construct a Bransted plot of the variation of the rate coefficient for proton removal with acidity along the series of substituted malonate monoanions the plot is reproduced in Fig. 12. The value of the gradient of the best line (a = ca 0.5) was interpreted (Miles et al., 1966) as indicating that proton removal by hydroxide ion occurs in a single step through a transition state in which the... 

In the middle range of styrene concentrations, a compromise is attained where there is sufficient styrene to scavenge all excess methanol radicals not involved in activation of the trunk polymer, yet an excess of monomer remains for grafting by the charge-transfer mechanism proposed by Dilli and Garnett (12) originally for copolymerisation to cellulose (4) and subsequently extended to wool (3), polyolefin (2,5) and PVC (13) systems. The data in Table V are consistent with this interpretation. 

The metabolism of C-DEHP by rainbow trout liver subcell-ular fractions and serum was studied by Melancon and Lech (14). The data in Table VI show that without added NADPH, the major metabolite produced was mono-2-ethylhexyl phthalate. When NADPH was added to liver homogenates or the mitochondrial or microsomal fractions, two unidentified metabolites more polar than the monoester were produced. Additional studies showed that the metabolism of DEHP by the mitochondrial and the microsomal fractions were very similar (Figure 1). Both fractions show an increased production of metabolites of DEHP resulting from addition of NADPH and the shift from production of monoester to that of more polar metabolites. The reduced accumulation of monoester which accompanied this NADPH mediated production of more polar metabolites may help in interpreting the pathway of DEHP metabolism in trout liver. This decreased accumulation of monoester could be explained either by metabolism of the monoester to more polar metabolites or the shift of DEHP from the hydrolytic route to a different, oxidative pathway. The latter explanation is unlikely because in these experiments less than 20% of the DEHP was metabolized. 

It is of interest to compare the data in Table 2 with the results of the investigation by KPTT. Only the tabulated function with nonzero y is qualitatively comparable with the KPTT wavefunction for He these two wavefunctions have about the same energy (that of KPTT is — 2.9000). Our wavefunction has die exact virial ratio ( — potential energy/kinetic energy=2 vs. the KPTT value of 2.074) this may be interpreted as an indication that it is at a better overall scaling than the KPTT function. That function, however, gives better results than ours for (pi P2) KPTT, 0.1545 vs. our 0.1928 and the exact value of 0.1591. 

Although the software used was not a full-featured factor analysis program, portions of the printed output are useful in studying the spectral data set. Table VI shows some information obtainable from PCR models (large data set) with 5, 10 and 13 factors. In this case, the "factors" are principal components derived entirely from the sample data set. PLS factors are not interpretable in the same manner. 

At just about this time (2), Pearson coined the words Hard and Soft to include, along with other effects, the electrostatic (hard) and covalent (soft) contributions to acid-base interactions. Hard-hard and soft-soft interactions reportedly dominate soft-hard combinations. The above interpretation of the data in Table 2 can be restated in terms of the hard and soft vocabulary. The softer sulphur donors react more strongly with the softer acid iodine and the harder oxygen donors react more strongly with the harder acid phenol. 

In our original work, we used an ionic-covalent model to interpret the E and C parameters. It has been suggested that our E and C parameters are a quantitative manifestation of the hard-soft model. "Softness (or hardness") can be considered (67) as a measure of the ratio of the tendency of a spedes to undergo covalent interaction to the tendency of the species to undergo electrostatic interaction. The relative "softness or hardness is depicted in the C/E ratio. The ratios for the acids and bases can be calculated from the data in Tables 3 and 4. If the ratio C/E is comparatively large, the add or base would be classified as type B or soft. Inasmuch as the relative ratios of C/E tells the relative importance of the two effects for various donors and acceptors, we agree that the hardness or softness discussed in the HSAB model is given by this ratio. 

What is the most reasonable interpretation, in terms of controling resistances, of the kinetic data of Table E18.7 obtained in a basket type mixed flow reactor if we know that the catalyst is porous Assume isothermal behavior. 

Table III shows the results on the various elements reported. The elements are arranged alphabetically by symbol. Cases where we have extrapolated or averaged data are so marked (—e.g., the averages of results for laboratory 33 in copper). Table III is the most important table here and contains all the information necessary to compile Tables IV, V, and VI. The latter are included to facilitate data comparison and interpretations. Table III is the true result of this phase of the study although we interpret it in some detail below, this is not absolutely necessary. A superficial perusal of Table III reveals discrepancies which must be caused by systematic errors in the various laboratories. A repeat of the comparative analysis program, with more uniform samples, coupled with circulation of known standards, should reduce this variation.

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