Subject discrepancies

Experimental exposure studies have attempted to associate various neurological effects in humans with specific trichloroethylene exposure levels. Voluntary exposures of 1 hours resulted in complaints of drowsiness at 27 ppm and headache at 81 ppm (Nomiyama and Nomiyama 1977). These are very low exposure levels, but the results are questionable because of the use of only three test subjects per dose, lack of statistical analysis, sporadic occurrence of the effects, lack of clear dose-response relationships, and discrepancies between the text and summary table in the report. Therefore, this study is not presented in Table 2-1. No effects on visual perception, two-point discrimination, blood pressure, pulse rate, or respiration rate were observed at any vapor concentration in this study. Other neurobehavioral tests were not performed, and the subjects were not evaluated following exposure. 

Most of the studies have involved the alloying of a second metal to platinum. The second metal was generally chosen because of its ability to increase the concentration of oxygenated species on the electrode surface, but also for its corrosion resistance. Even if some discrepancies exist in the literature, R-Ru is now widely accepted as the most interesting one, and hence our analysis will focus on this alloy in the next subsection. Other alloys such as R-lr, R-Os, or R-Re have also been reported to be good candidates, and R-Mo under specific conditions of preparation was claimed to have the desired properties. The Pt-Sn alloy is still a subject... 

The unknown model parameters will be obtained by minimizing a suitable objective function. The objective function is a measure of the discrepancy or the departure of the data from the model i.e., the lack of fit (Bard, 1974 Seinfeld and Lapidus, 1974). Thus, our problem can also be viewed as an optimization problem and one can in principle employ a variety of solution methods available for such problems (Edgar and Himmelblau, 1988 Gill et al. 1981 Reklaitis, 1983 Scales, 1985). Finally it should be noted that engineers use the term parameter estimation whereas statisticians use such terms as nonlinear or linear regression analysis to describe the subject presented in this book. 

Given the mixed results in the literature, it is difficult to know just how caffeine does affect memory. To some extent, the differential effects may depend on the memory assessment method (recall or recognition) and the time frame (immediate or delayed). Gender differences may also cloud the picture, as discussed above. Even when these differences are taken into account, however, unexplained discrepancies remain. One partial explanation may be that the differential effects of caffeine are a function of the subject s memory load. For example, Anderson65 found that caffeine enhanced low load memory tasks but was detrimental in high load tasks. This could be due to the increased arousal induced by the high load task, which, in the presence of caffeine, could produce overarousal. The drop in arousal output as the subject crossed the peak of the inverted U-shaped function could cause the memory deficits observed in some studies. 

A variety of factors differentiating tolerance studies could have contributed to the observed discrepancy. Lower doses are less likely to lead to tolerance than higher doses or will do so less rapidly. The habitual coffee drinkers in some studies may have had different levels or durations of consumption. In the cases of acute dosing, caffeine consumed by subjects outside the laboratory on the day of the experiment may have varied. This is particularly true when some investigators request in advance that subjects abstain from caffeine prior to the experiment, while others do not. Differences in age, gender, and arousal-relevant personality dimensions,... 

The interesting (and important) difference is in the values for the ratio of sums-of-squares, which is the nonlinearity measure. As we see, at small values of nonlinearity (i.e., k — 0,1, 2) the values for the nonlinearity are almost the same. As k increases, however, the value of the nonlinearity measure decreases for the case of Normally distributed data, as compared to the uniformly distributed data, and the discrepancy between the two gets greater as k continues to increase. In retrospect, this should also not be surprising, since in the Normally distributed case, more data is near the center of the plot, and therefore in a region where the local nonlinearity is smaller than the nonlinearity over the full range. Therefore the Normally distributed data is less subject to the effects of the nonlinearity at the wings, since less of the data is there. 

Process measurements are subject to errors. These errors give rise to discrepancies in material and energy balances. 

From this point of view, let us wander a little from the subject to discuss briefly the comparison of the computed and experimental values of a dipole moment. Too often, people compare their theoretical results with experimental values obtained in solution, and if there is a discrepancy between the two sets, they generally blame the so-called failure of quantum chemistry to predict dipole moments. 

The reconciliation mentioned in the title of this work is, fundamentally, one of an unreal conflict arising from a lack of understanding. Despite this author s attempts over several decades (which are the subject of Chapter 5) to show that in the context of cationoid polymerisations the concept of rate constant must be used with great care, many colleagues were dismayed by the discrepancy implicit in the title. 

The previous example (which incidentally is not entirely fictional) shows that a discrepancy of 0.4 kJ mol-1 in the standard enthalpy of formation of methane has some economic impact. To avoid disputes like this one, the International Organization for Standardization (ISO) has issued an International Standard on the subject [34], The 46-page publication includes a detailed discussion of the... 

It is not the purpose of this paper at this moment to investigate further for more detailed reasons for discrepancies in confidence bands or estimated amount intervals. That will be investigated fully at a later time. I do wish to point out that the assumptions one makes about the information he has and the statistical approaches he makes profoundly affect the resultant error calculations. Far from being a staid and dormant subject matter, statistical estimations of error are currently very actively being studied in order for scientific workers and citizens alike to be informed about the error in their work. 

While microscopic techniques like PFG NMR and QENS measure diffusion paths that are no longer than dimensions of individual crystallites, macroscopic measurements like zero length column (ZLC) and Fourrier Transform infrared (FTIR) cover beds of zeolite crystals [18, 23]. In the case of the popular ZLC technique, desorption rate is measured from a small sample (thin layer, placed between two porous sinter discs) of previously equilibrated adsorbent subjected to a step change in the partial pressure of the sorbate. The slope of the semi-log plot of sorbate concentration versus time under an inert carrier stream then gives D/R. Provided micropore resistance dominates all other mass transfer resistances, D becomes equal to intracrystalline diffusivity while R is the crystal radius. It has been reported that the presence of other mass transfer resistances have been the most common cause of the discrepancies among intracrystaUine diffusivities measured by various techniques [18]. 

Becanse the vapor pressnre of chemicals is a key factor in controlling their dissipation within the snbsnrface, and from the snbsnrface to the atmosphere, accurate estimation of this valne is reqnired. Comprehensive reviews on this subject are given by Plimmer (1976) and Glotfelty and Schombnrg (1989). For contaminants with low vapor pressnre that reach the snbsnrface as a result of a nonpoint disposal (e.g., pesticides nsed in agricnltnral practices), their vapor pressure is sufficiently low to be below detection limits, which may explain some discrepancies in the reported results. 

Other possible reasons for the discrepancy in the Mark-Houwink parameters may be due to the band spreading effects and inadequate signal-to-noise quality at the tails of the viscometer chromatogram. These subjects will be the topic of our future investigations in this area. 

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