Comparative references meaning

In these cases it is not necessary to determine the absolute bioavailability or the absorption rate constant for the product under study. It is only necessary to prove that the plasma concentration versus time curve is not significantly different from the reference product s curve. This is done by comparing the means and standard deviations of the plasma concentrations for the two products at each sampling time using an appropriate statistical test. 

Measurement bias is determined by comparing the mean of measurement results obtained for a reference material, using the method being validated, with the assigned property values for that reference material. The number of replicate analyses required (n) depends on the precision of the method (.s ) and the level of bias (8) that needs to be detected [12]. A useful approximation is shown in the following equation ... 

The foregoing discussion identifies the need for comparability and its achievement by metrological traceability. Comparability of measurement results is conveniently defined in terms of metrological traceability. If two results are traceable to the same stated metrological reference, then they must be comparable. Please note that in common speech comparable often means about the same magnitude, but this is not the case here. In the laboratory comparability of measurement results means simply that the results can be compared. The outcome of the comparison, whether the results are considered near enough to be equivalent, is not a factor in comparability here. 

The critical value of the t test can be abbreviated as fa(2),v, where a(2) refers to the two-tailed probability of a and v = n — 1 (degree of freedom). For the two-tailed t test, compare the calculated t value with the critical value from the t distribution table. In general, if t > ta(2),v, then reject the null hypothesis. When comparing the means of replicate determinations, it is desirable that the number of replicates be the same in each case. 

The three papers by Rice and co-workers involve differences of elaboration. Figure 3.22 compares a mean field with a MC approach, between which not much difference is found. Density profiles are typical results from this type of analysis. The quantitative outcome depends of course on the choice of peiriuneters, which is analyzed in some detail by the authors the reader is referred to their papers for further information. As expected, the layer becomes thicker at higher surface occup-cmcy it is also seen that at low occupancy some segments close to the head group are in contact with the solvent. 

Among these difficulties are that, even where the two formulations are absolutely identical, the probability of meeting this requirement cannot necessarily be increased to 100% simply by increasing the sample size. (One would have to increase the number of occasions a given individual was studied instead and compare the mean of the occasions under test to the mean under reference.) Because of inherent variability from occasion to... 

An original route is that proposed by Ter-Minassian and Million in 1983 [44] with their pneumatic compensation calorimeter, represented in Fig 10. The tubular sample cell 4 is in good thermal contact with four metallic bulbs. Two of them operate like bulb 1 in the figure, Le. as pneumatic thermal detectors. They are filled with gas, say around 1 bar, and their pressure is compared, by means of a differential manometer, with the constant pressure of a reference reservoir 3 immersed in the surrounding thermostat block 5. Therefore, they detect any temperature change of the sample. The two oflier bulbs operate like bulb 2, i.e. as pneumatic energy-compensating devices. They are also filled with gas, say around 1 bar, but they are connected to flie piston-cylinder 7 which enables the heat of compression (or decompression) necessary to cancel the temperature difference between the sample and thermostat (as detected with the first set of bulbs) to be produced in the bulb. More recently, Zimmermaim and Keller built a comparable pneumatic compensation calorimeter whose calorimetric performances were carefully examined [45]. 

Beside counts of cases or severity of incidents, other statistical methods may help determine if a safety program has affected accident or incident performance. Refer to a standard textbook on inferential statistics. For example, one can compare the mean accident rate for six months before program implementation to a six-month period after the program begins. 

In all these applications, isotope ratio data are produced, which are interpreted on an absolute or relative basis and which have an impact on our daily life, whether this is in science (e.g., age of an artifact), in society (e.g., provenance of food), or in public safety (e.g., neutron shielding in nuclear power plants). To ensure that these data are reliable and accurate, some specific requirements have to be fulfilled. The main requirement is that all these measurement results are comparable, which means that the corresponding results can be compared and differences between the measurement results can be used to draw further conclusions. This is only possible if the measurement results are traceable to the same reference [25]. This in turn can only be realized by applying isotopic reference materials (IRMs) for correction for bias and for validation of the analytical procedure. Whereas in earlier days only experts in mass spectrometry were able to deliver reproducible isotope ratio data, nowadays many laboratories, some of which may even have never been involved with mass spectrometry before, produce isotope ratio data using inductively coupled plasma mass spectrometry (ICP-MS). Especially for such users, IRMs are indispensable to permit proper method validation and reliable results. The rapid development and the broad availability of ICP-MS instrumentation have also led to an expansion of the research area and new elements are under investigation for their isotopic variations. In this context, all users require IRMs to correct for instrumental mass discrimination or at least to allow isotope ratio data to be related to a commonly accepted basis. 

Some of the coarse-grained parameters, i e and can be easily measured by experiments or in simulations. The other two parameters, %N and the suppression of density fluctuations, XqN, are thermodynamic characteristics, which are not directly related to the structure (i.e., they cannot be simply expressed as a function of the molecular coordinates). If density fluctuations of the polymeric liquid are small on the length scale of interest (e.g., width of an interface between domains), then the value of the compressibility has only a minor relevance and decreasing it even further will not significantly affect the behavior of the system. Thus, field-theoretic calculations often take the idealized limit of strict incompressibility. In particle-based simulations, however, one often softens the constraint in order to facilitate the motion of the interaction centers and, thereby, reduces the viscosity of the polymer liquid. The Flory-Huggins parameter, in turn, is a crucial coarse-grained parameter and different methods have been devised to extract it from experiments or simulations [16, 20-25]. We shall briefly discuss this important issue in Section 5.2.3, and further refer the reader to the literature, where computer simulations have been quantitatively compared with mean field predictions and where the role of fluctuations on the coarse-grained parameters is discussed [16, 22]. 

There are two procedures for doing this. The first makes use of a metal probe coated with an emitter such as polonium or Am (around 1 mCi) and placed above the surface. The resulting air ionization makes the gap between the probe and the liquid sufficiently conducting that the potential difference can be measured by means of a high-impedance dc voltmeter that serves as a null indicator in a standard potentiometer circuit. A submerged reference electrode may be a silver-silver chloride electrode. One generally compares the potential of the film-covered surface with that of the film-free one [83, 84]. 

Table 2. Percentage error for LN compared to reference Langevin trajectories (at 0.5 fs) for energy means and associated variances for BPTI over 60 ps at 7 = 20 ps At = 0.5 fs, Atm = 3 fe, and At — k2Atm, where hz ranges from 1 for LN 1 to 96 for LN 96.

The comparison plot offers a particularly simple and direct means of comparing the shapes of a pair of isotherms but for more general applications which involve a numt>er of samples of a solid covering a wide range of specific surface, the a,-method is preferable. The j-curve represents a convenient way of recording and using the reference isotherm. 

Because of the complex nature of the discharge conditions, GD-OES is a comparative analytical method and standard reference materials must be used to establish a unique relationship between the measured line intensities and the elemental concentration. In quantitative bulk analysis, which has been developed to very high standards, calibration is performed with a set of calibration samples of composition similar to the unknown samples. Normally, a major element is used as reference and the internal standard method is applied. This approach is not generally applicable in depth-profile analysis, because the different layers encountered in a depth profile of ten comprise widely different types of material which means that a common reference element is not available. 

There is a nice point as to what we mean by the experimental energy. All the calculations so far have been based on non-relativistic quantum mechanics. A measure of the importance of relativistic effects for a given atom is afforded by its spin-orbit coupling parameter. This parameter can be easily determined from spectroscopic studies, and it is certainly not zero for first-row atoms. We should strictly compare the HF limit to an experimental energy that refers to a non-relativistic molecule. This is a moot point we can neither calculate molecular energies at the HF limit, nor can we easily make measurements that allow for these relativistic effects. 

A comparison between Gl, G2, G2(MP2) and G2(MP2,SVP) is shown in Table 5.2 for the reference G2 data set the mean absolute deviations in kcal/mol vary from 1.1 to 1.6 kcal/mol. There are other variations of tlie G2 metliods in use, for example involving DFT metliods for geometry optimization and frequency calculation or CCSD(T) instead of QCISD(T), with slightly varying performance and computational cost. The errors with the G2 method are comparable to those obtained directly from calculations at the CCSD(T)/cc-pVTZ level, at a significantly lower computational cost. ... 

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