Variables /observations

As was cited in the case of immersion testing, most SCC test work is accomplished using mechanical, nonelecdrochemical methods. It has been estimated that 90 percent of all SCC testing is handled by one of the following methods (1) constant strain, (2) constant load, or (3) precracked specimens. Prestressed samples, such as are shown in Fig. 28-18, have been used for laboratory and field SCC testing. The variable observed is time to failure or visible cracldng. Unfortunately, such tests do not provide acceleration of failure. 

Fiypothesis unable to explain the values of the state variables observed... 

The renal excretion of drugs depends on glomerular filtration, tubular secretion, and tubular absorption. A twofold increase in glomerular filtration occurs in the first 14 days of life [36], The glomerular filtration rate continues to increase rapidly in the neonatal period and reaches a rate of about 86 mL/min per 1.73 m2 by 3 months of age. Children 3-13 years of age have an average clearance of 134 mL/min per 1.73 m2 [37]. Tubular secretion approaches adult values between 2 and 6 months [11], There is more variability observed in maturation of tubular reabsorption capacity. This is likely linked to fluctuations in urinary pH in the neonatal period [38],... 

The biaiyl phenol (X) was the penultimate intermediate in the synthesis of this final drag substance. Thus after the Suzuki coupling reaction is performed to give the phenol, the levels of Pd have to be lowered to < 10 ppm. In the pharma industry this can be a significant problem. Additionally there is always batch to batch variability observed when catalysts like Pd2(dba)3 have been used in Suzuki couphng reactions. 

Finally, Fig. 27 compares the PE-data with the predictions of the rubber-like model of des Cloizeaux [51]. This model describes the experimental data very appropriately over the entire range of the scaling variable observed. In particular, we note that compared to the Ronca model description the predicted Q splitting is less pronounced and much closer to the experimental observation. In his analysis des Cloizeaux derives a mean distance squared between entanglement of = 12.55 nm2. In order to compare results with the entanglement... 

If repeatability is the only estimate of precision that is obtained, this is unlikely to be representative of the variability observed when the method is used over a long period of time. Intermediate precision is often more relevant - this expresses the within-laboratory variation or within-laboratory reproducibility (different days, different analysts, different equipment, etc.). This is initially obtained from validation studies and confirmed later by examining the results obtained for quality control material measured over a period of about three months (see the quality control (QC) charts in Chapter 6). 

A major reason for this lies in methodological problems connected with the observation and description and the nature of human behaviour itself. In order to asses causation and function of behaviour we rely on an observational device. The process of information reduction which is applied to the study of behaviour results in highly variable observations. The assessment of meaning and function rarely produces reproducible results, and different signals especially in human communication seem to take many meanings which are context-specific. Partially this might be due to the observational approaches used for coding behaviour. 

The precision of the assay for nonreduced samples was demonstrated by the evaluation of six independent sample preparations on a single day (repeatability) and the analysis of independent sample preparations on three separate days by two different analysts (intermediate precision). The RSD values for the migration time were 0.9%. The RSD values for peak area percent of the main peak and the minor peaks in the profile were 0.6 and 12.6%, respectively. The higher variability observed with the minor peaks was determined to be primarily related to the sample heating during preparation for the analysis. These results demonstrate that the use of uncoated fused-silica capillaries in combination with a sieving matrix can provide adequate precision and analyte recovery. 

This principle is not applicable in biological psychiatry. One can and should not simply discard the possibility that a biological variable observed in a psychotic condition is linked to a concurrent depression or that one found in depression is in fact related to a comorbid anxiety disorder. The hierarchical principle is a deus ex machina that resolves the problem of comorbidity only in appearance. Comorbidity in itself is merely a descriptive, not an explanatory, term. The multiplicity of psychiatric disorders, as they are presently defined, in so many patients permits a variety of explanations (Van Praag 1996], and thus the term comorbidity conceals more than it discloses. 

Fluoropyrimidines (e.g., 5-FU, oral capecitabine) remain unchallenged as reference drugs for treating munerous solid tumors in adults, including digestive, head and neck, and breast cancers. The wide inter-patient variability observed in the pharmacokinetic profiles of these drugs is mainly caused by the erratic activity of dihydropyrimidine... 

Asteroids in the outer asteroid belt show considerable spectral variability, due in part to differences in the degree of aqueous alteration. However, alteration alone is not sufficient to explain all the compositional variability observed in meteorites derived from these objects. Laboratory studies of carbonaceous chondrites show significant differences in the compositions and proportions of the various primary components, demonstrating that accreted materials in the asteroid belt were not uniform. 

In a high-resolution mapping of the cardiac activation in isolated rabbit hearts, the stochastic nature of propagation mentioned above may be reflected by the beat-to-beat variability observed by others [Dhein et al., 1990]. 

NUMBER OF SANPLE POINTS NUMBER OF INDEPENDENT VARIABLES TABLE OF INDEPENDENT VARIABLES OBSERVATIONS OF DEPENDENT VARIABLE IDENTIFIER OF NEIGHTING OPTIONS... 

The problem to be solved here primarily comes from chemical engineering where one simultaneously observes several variables that are expected to satisfy a number of balance equations such as stoichiometric relations. Due to measurement errors the observed values obviously do not fulfill this expectation. Let x, . xnv denote these variables observed in a single... 

For over a century it has been known that two classes of variables have to be distinguished the microscopic variables, which are functions of the points of ClN and thus pertain to the detailed positions and motions of the molecules and the macroscopic variables, observable by operating on matter in bulk, exemplified by the temperature, pressure, density, hydro-dynamic velocity, thermal and viscous coefficients, etc. And it has been known for an equally long time that the latter quantities, which form the subject of phenomenological thermo- and hydrodynamics, are definable either in terms of expected values based on the probability density or as gross parameters in the Hamiltonian. But at once three difficulties of principle have been encountered. 

In the UK the Joint Food Science and Safety Group of the Department of Health and the Ministry of Agriculture, Fisheries and Food have published the results of many analyses for chemical contaminants in food carried out under their Food Surveillance Programme. In many cases the raw data from these surveys are available for analysis. Table 2.1 lists the results of analyses for lead in some samples of cow, sheep and pig kidney obtained in Scotland and England.5 There are clear differences between species and some evidence of differences between sampling locations. What is not clear is the extent to which the variability observed is due to real and consistent differences between species and location or to normal biological variation. 

In near shore and estuarine environments, changes in salinity might be a major factor in the variability observed in DMS concentrations in these areas. It is clear that, in those organisms which synthesize DMSP, changes in salinity... 

In oncology, drug dosage individualization for conventional cytotoxic anticancer therapy is performed according to mg/m2 or mg/kg. However, even after dose adjustment, the pharmacokinetic variability observed for many cytotoxic... 

The full model can reproduce all the systematic features of the absorption curves in Fig. 2.5 [11]. In the present form, the model does not account for the interpatient variability observed in Fig. 2.4 or for the nonsystemahc phenomena discussed in connection with Fig. 2.5. Ideally, in our mechanism-based modeling approach, any form of unsystematic variation in the experimental results should be given a separate explanation, i.e. specific studies should be undertaken to explain the observed variability in absorption rates and binding capacities. Statistical outliers should be considered as potential sources of new information. 

The covariate analysis revealed physiologically plausible covariate effects, partly explaining the variability observed in the pharmacokinetic parameters. A high interindividual variability on the CL/F not responsible for the generation of Ml still remained even after the incorporation of the covariates sex and creatinine clearance. This indicates that there still might be yet undiscovered covariates additionally influencing the elimination of NS2330. 

A Dynamic Perspective of Variability The model under study here offers an opportunity to refer to some implications of the existence of nonlinear dynamics. Apart from the jagged cortisol concentration profile, elements such as the sensitive dependence from the initial conditions (expressed by the positive Lyapunov exponent), as well as the system s parameters, play an important role and may explain the inter- and intraindividual variability observed in the secretion of cortisol. These implications, together with other features absent from classical models, are demonstrated in Figure 11.12. 

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