Replication errors application

Vertzoni et al. (30) recently clarified the applicability of the similarity factor, the difference factor, and the Rescigno index in the comparison of cumulative data sets. Although all these indices should be used with caution (because inclusion of too many data points in the plateau region will lead to the outcome that the profiles are more similar and because the cutoff time per percentage dissolved is empirically chosen and not based on theory), all can be useful for comparing two cumulative data sets. When the measurement error is low, i.e., the data have low variability, mean profiles can be used and any one of these indices could be used. Selection depends on the nature of the difference one wishes to estimate and the existence of a reference data set. When data are more variable, index evaluation must be done on a confidence interval basis and selection of the appropriate index, depends on the number of the replications per data set in addition to the type of difference one wishes to estimate. When a large number of replications per data set are available (e.g., 12), construction of nonparametric or bootstrap confidence intervals of the similarity factor appears to be the most reliable of the three methods, provided that the plateau level is 100. With a restricted number of replications per data set (e.g., three), any of the three indices can be used, provided either non-parametric or bootstrap confidence intervals are determined (30). 

As in many such problems, some form of pretreatment of the data is warranted. In all applications discussed here, the analytical data either have been untreated or have been normalized to relative concentration of each peak in the sample. Quality Assurance. Principal components analysis can be used to detect large sample differences that may be due to instrument error, noise, etc. This is illustrated by using samples 17-20 in Appendix I (Figure 6). These samples are replicate assays of a 1 1 1 1 mixture of the standard Aroclors. Fitting these data for the four samples to a 2-component model and plotting the two first principal components (Theta 1 and Theta 2 [scores] in... 

Fig. 16.36 Change in bacteria populations in remoistened Gilat soil after application of 10-160 pg parathion per g dry soil. Plotted points are means of three replicates standard error. Continuous curves represent model simulations. Values obtained in control soils to which hexane alone was added have been subtracted. Reprinted from Nelson LM, Yaron B, Nye PH (1982) Biologically-induced hydrolysis of parathion in soil. Soil Biol Biochem 14 223-227. Copyright 1982 with permission of Elsevier...

When doing an experiment by application of a second-order CCRD, one need not replicate trials to estimate Sy2 in all trials of the design since the variance homogeneity of the trials makes several replications in one point possible as well as a determination of its error in that point, which is valid as an estimate for all other points of the design of experiments. 

Measurement models, developed for impedance spectroscopy by Agarwal et 56,86,262 generally applicable and can be used to estimate both stochastic and bias errors of a measxuement from imperfectly replicated impedance measurements. Orazem et al. used a measurement model approach to show that a general model for the error structure could take the form... 

Figure 2. Metabolism of C-chlorsulfuron in leafy spurge and Canada thistle cell cultures or in conditioned medium at 24, 48, and 72 h after application of 0,10-uCi C-chlorsulfuron. Conditioned medium had been passed through a 20-um filter to remove cells at the time of treatment. Closed and open symbols represent separate experiments. Each data point is the mean of two replications. Vertical bars represent the standard error of the mean when not present, the standard error was less than 1%. (Reproduced with permission from reference 26. Copyright 1986, Weed Science Society of America.)...

The study of evolutionary trees seems to indicate that almost no natural selection was invoked in the choice of proteins [1]. The selection appears to be random. One thus tends to think the selection was guided by the stability of the protein and the efficiency in the active site region. A different view has emerged recently. The synthesis of proteins after all went through a selection process, but one implemented at the molecular level, and the fitness was determined by none other than water. We describe and develop this view briefly here. The process of selection by elimination of error in protein synthesis is known as kinetic proofreading (KPR), which is applied generally to the selectivity of enzymes towards substrate absorption and conversion to product. However, more specifically, it applies to the avoidance of error in protein synthesis. Here we shall first discuss KPR from a general point of view, with application to protein synthesis and DNA replication. 

The Gaussian error function correctly describes the variations to be expected in a population of values of infinite size, an obviously unattainable level of replication in practice. With the general guidelines embodied in this function, a body of statistical rules applicable to small sets of data has been developed. 

Figure 4, Mass remaining in replicate mesocosms at 28 days after A TS/water application relative to the mass present just prior to application. (Mass is indicated by the volume under the concentration contours.) Values indicate the mean of two mesocosms and error bars indicate the standard error.

Encoding software using arithmetic codes facilitates software-implemented hardware error detection. In contrast to replication, arithmetic codes enable also the detection of permanent errors. Furthermore, the error detection capabilities of arithmetically encoded applications can be determined independent of the used hardware (see Sect. 3). 

Usually soft-error tolerant hardware uses replication of large hardware parts and voting for error detection and correction [38,27,4]. Currently research efforts include more sophisticated approaches than simple replication. [18] reuses testing circuitry for error detection and correction and [17] extends hardware with built-in soft error resilience which is able to detect and correct soft errors and even to predict a soon hardware failure. The hardware design presented in [26] on-the-fly replicates executed instructions. [23] checks consistency of data independent parts of instruction fetch and decoding for repeated traces within an application. For this to be useful, it is required that an application consists to large parts of traces which are repeated often. All those approaches only aim... 

Objective. So far, safety transformations are just claimed and believed to be correct, since the applied techniques of replication or arithmetic encoding are well known. However, the implementation is complicated in many places. Hence, there is no guarantee that the resulting safe software produces the same results as the unsafe original software in an error-free execution or that the implementation ensures the best error detection theoretically possible. Thus, before applying these transformations in safety-critical applications, we need to check the following properties ... 

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