Surveys correlation coefficient

Food Biomarker Tissue/biofluid N Food survey Correlation coefficient (r) P References... 

The use of the icterus index, as described by Meulengracht, for the assessment of jaundice has fallen into disrepute because of the errors caused by the presence of lipochromes, carotenoids, and other yellow pigments. Josephson (J6) in his survey found that the correlation coefficient between icterus index and serum bilirubin concentration was 0.69 in 360 healthy subjects and 0.84 in 40 jaundiced subjects. In newborn infants however, bilirubin is the only yellow pigment likely to be present and the possibility of determining serum bilirubin concentrations by direct measurement has again been re-examined. Abelson and Boggs (Al) diluted serum from infants with erythroblastosis 1 in 50 and studied the absorption curves. They found that in addition to the bili-... 

HIV/AIDS human immunodeficiency virus/acquired immunodeficiency syndrome HUI Health Utilities Index HYEs healthy-year equivalents ICC intraclass correlation coefficient KDQOL Kidney Disease Quality of Life instrument MCS mental component summary scale of the SF-36 MOS-HTV Medical Outcomes Study HIV Health Survey MOT Medical Outcomes Trust MSQOL Migraine Specific Quality of Life NHP Nottingham Health Profile PCS physical component summary scale of the SF-36 QALY quahty-adjusted hfe year QOL quahty of life QOLIE Quality of Life in Epilepsy QWB Quality of WeU-Being scale SF-36 MOS 36-Item Short-Form Health Survey SIP Sickness Impact Profile VAS visual analog scale WY well year YHL years of healthy life... 

An autopsy survey of ribs and vertebrae of two groups of cadavers has confirmed that mature bones acquire fluoride and its concentration relates to water fluoride levels. The incidence and severity of intervertebral osteoarthritis and osteoporosis seemed unaffected by the fluoride in potable waters at least over a 5-50 jumol dm" range [290]. The proportionate rates of increase of fluoride in plasma and bone with age and the similarity of the correlation coefficients imply an equilibrium between bone fluoride and plasma fluoride [337]. 

We list in Table 2 the computational chemists among the chemists ranked 51-1000 in the ISI survey. Of these 950 highly cited chemists, we see from the table that about 100 are in computational chemistry or closely related to it. This again strikes us as an impressively high proportion. Most of those listed have published many papers in the period covered by the ISI survey, but some individuals have published only a few papers, often as a coauthor. Thus, it is possible to make the most-cited list with just a few key papers from some of the well-known groups. The correlation coefficient between the number of papers and the number of citations in the ISI data set of 10856 chemists is only 0.4. Thus, someone who has not published many papers can nevertheless be well cited, and vice versa. We include in Table 2 some theoreticians, crystallogra-phers, and others whose work is closely aligned with computational chemistry. Many of the tabulated authors have contributed a chapter to Reviews in Computational Chemistry. 

We then computed correlation coefficients to look at the relationships between the survey score, the RAIR rating, and the NAMST rating. Table 8.3 is a matrix of these correlation coefficients. 

To put the strength of these correlation coefficients into context, remember the example from another field, that the correlation between SAT and Yale grade point average is in the range of. 2 to. 3. This means that the SAT, the best available predictor, can only account for about 10 percent of the variation in college GPA. Compared to the SAT, the perception survey appears to be a much better predictoi accounting for 25-50 percent of the variation. 

Mass-transfer coefficients seem to vary as the 0.7 exponent on the power input per unit volume, with the dimensions of the vessel and impeller and the superficial gas velocity as additional factors. A survey of such correlations is made by van t Riet (Ind Eng. Chem Proc Des Dev., IS, 3.57 [1979]). Table 23-12 shows some of the results. 

A survey of the mathematical models for typical chemical reactors and reactions shows that several hydrodynamic and transfer coefficients (model parameters) must be known to simulate reactor behaviour. These model parameters are listed in Table 5.4-6 (see also Table 5.4-1 in Section 5.4.1). Regions of interfacial surface area for various gas-liquid reactors are shown in Fig. 5.4-15. Many correlations for transfer coefficients have been published in the literature (see the list of books and review papers at the beginning of this section). The coefficients can be evaluated from those correlations within an average accuracy of about 25%. This is usually sufficient for modelling of chemical reactors. Mathematical models of reactors arc often more sensitive to kinetic parameters. Experimental methods and procedures for parameters estimation are discussed in the subsequent section. 

The mass transfer between phases is, of course, the very basis for most of the diffusional operations of chemical engineering. A considerable amount of experimental and empirical work has been done in connection with interphase mass transfer because of its practical importance an excellent and complete survey of this subject may be found in the text book of Sherwood and Pigford (S9, Chap. Ill), where dimensionless correlations for mass transfer coefficients in systems of various shapes are assembled. 

Since the fraction of empty sites in a zeolite channel determines the correlation factor (Section 5.2.2), as is well known from single-file diffusion in the pores of a membrane, the strong dependence of the diffusion coefficients on concentration can be understood. This is why a simple Nernst-Planck type coupling of the diffusive fluxes (see, for example, [H, Schmalzried (1981)]) is also not adequate. Therefore, we should not expect that sorption and desorption are symmetric processes having identical kinetics. Surveys on zeolite kinetics can be found in [A. Dyer (1988) J. Karger, D.M. Ruthven (1992)]. 

Various correlations exist for mass and heat transfer coefficients in terms of dimensionless numbers. Table 8.7 surveys the most appropriate ones for laboratory fixed-bed reactors [8,17-19]. The Sherwood number, Sh, and the Nusselt number, Nu, express the ratio of total mass transfer to diffusive mass transfer, and the ratio of total heat transfer to conductive heat transfer, respectively. Values of kf and h for gases in laboratory systems range from 0.1 to 10 mf " s ... 

The previous sections serve to illustrate the wide range of compounds that can interact with the molybdenum hydroxylases, although the survey is by no means exhaustive. Substrate and inhibitor specificity is governed by an interplay of steric, lipophilic and electronic factors. With simple molecules, it may be possible to demonstrate a correlation between enzymic activity and electron density [50] or partition coefficient, which is indicative of lipophilicity [175], but with more complex substrates it is often difficult to differentiate between such effects. 

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