Data anomalies

In several previous papers, the possible existence of thermal anomalies was suggested on the basis of such properties as the density of water, specific heat, viscosity, dielectric constant, transverse proton spin relaxation time, index of refraction, infrared absorption, and others. Furthermore, based on other published data, we have suggested the existence of kinks in the properties of many aqueous solutions of both electrolytes and nonelectrolytes. Thus, solubility anomalies have been demonstrated repeatedly as have anomalies in such diverse properties as partial molal volumes of the alkali halides, in specific optical rotation for a number of reducing sugars, and in some kinetic data. Anomalies have also been demonstrated in a surface and interfacial properties of aqueous systems ranging from the surface tension of pure water to interfacial tensions (such as between n-hexane or n-decane and water) and in the surface tension and surface potentials of aqueous solutions. Further, anomalies have been observed in solid-water interface properties, such as the zeta potential and other interfacial parameters. 

Is there a process in place for resolving questions about data reporting or to highlight data anomalies ... 

If anomalies are discovered using the screening methods described above, several outcomes are possible (1) the anomaly can be traced and rectified (e.g., it may be attributable to a human or computer error with backup available) (2) the anomaly may be explainable as a procedural error, but it may not be possible to rectify and (3) the anomaly maybe unexplainable. Data anomalies that are explainable with supporting documentation could justify classification of the given data items as missing data or replacement of aU data for the corresponding subject in the data set (i.e., the subject could be dropped from the study and... 

Note some particularities of new USCT method. At first, data collection and search of areas with anomalous (inhomogeneous)SD of acoustic parameters (velocities of spreading of US waves) is joined. As a sought image, on which anomalies is revealed, it is offered total image B (r), which practically is the low frequency copy of restored fimction g(f). As PMF SD of... 

A problem obviously exists in trying to characterise anomalies in concrete due to the limitations of the individual techniques. Even a simple problem such as measurement of concrete thickness can result in misleading data if complementary measurements are not made In Fig. 7 and 8 the results of Impact Echo and SASW on concrete slabs are shown. The lE-result indicates a reflecting boundary at a depth corresponding to a frequency of transient stress wave reflection of 5.2 KHz. This is equivalent to a depth of 530 mm for a compression wave speed (Cp) of 3000 m/s, or 706 mm if Cp = 4000 m/s. Does the reflection come from a crack, void or back-side of a wall, and what is the true Cp ... 

The first step in developing a QSPR equation is to compile a list of compounds for which the experimentally determined property is known. Ideally, this list should be very large. Often, thousands of compounds are used in a QSPR study. If there are fewer compounds on the list than parameters to be fitted in the equation, then the curve fit will fail. If the same number exists for both, then an exact fit will be obtained. This exact fit is misleading because it fits the equation to all the anomalies in the data, it does not necessarily reflect all the correct trends necessary for a predictive method. In order to ensure that the method will be predictive, there should ideally be 10 times as many test compounds as fitted parameters. The choice of compounds is also important. For... 

The validation of the prediction equation is its performance in predicting properties of molecules that were not included in the parameterization set. Equations that do well on the parameterization set may perform poorly for other molecules for several different reasons. One mistake is using a limited selection of molecules in the parameterization set. For example, an equation parameterized with organic molecules may perform very poorly when predicting the properties of inorganic molecules. Another mistake is having nearly as many fitted parameters as molecules in the test set, thus fitting to anomalies in the data rather than physical trends. 

The population balance analysis of the idealized MSMPR crystallizer is a particularly elegant method for analysing crystal size distributions at steady state in order to determine crystal growth and nucleation kinetics. Unfortunately, the latter cannot currently be predicted a priori and must be measured, as considered in Chapter 5. Anomalies can occur in the data and their subsequent analysis, however, if the assumptions of the MSMPR crystallizer are not strictly met. 

In Fig. 9 the relaxation time shows a very smooth variation with e (analogously smooth data has been obtained for Dj e)). In particular, no evidence for a critical anomaly as e — e, is seen. The straight line in Fig. 9 represents a law = T2 ec)Qxp[ ec — e)/k T], which indicates a simple thermally activated behavior. 

Positive Eu anomaly is observed for hydrothermal solution issuing from the hydrothermal vent on the seawater at East Pacific Rise (Bence, 1983 Michard et al., 1983 Michard and AlbarMe, 1986). Guichard et al. (1979) have shown that the continental hydrothermal barites have a positive Eu anomaly, indicating a relatively reduced environment. Graf (1977) has shown that massive sulfide deposits and associated rocks from the Bathurst-Newcastle district. New Brunswick have positive Eu anomalies. These data are compatible with positive Eu anomaly of altered basaltic rocks, ferruginous chert and Kuroko ores in Kuroko mine area having positive Eu anomaly and strongly support that Eu is present as divalent state in hydrothermal solution responsible for the hydrothermal alteration and Kuroko mineralization. 

A few REE data on hydrothermal solutions are available (Fig. 2.34). Chondrite normalized REE patterns of hydrothermal solutions from Vienna Wood, Pacmanus and Desmos, Manus Basin exhibit positive Eu anomaly and LREE enrichment are similar to midoceanic ridge solution and Kuroko ore fluids. This positive Eu anomaly (Fig. 2.35) may have been caused by the selective leaking of Eu due to the interaction of an ascending hydrothermal solution and footwall volcanic rocks (Gena et al., 2001). It is interesting to note that altered basaltic andesite has a negative Eu anomaly and this feature is the same as that found in the Kuroko mine area (Shikazono, 1999). 

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