Interpretive characteristics

Recently, Hwang et al. have performed conceptually similar predictions using three-dimensional structural information (Hwang et al., 1999). Here, structural similarities between a protein of unknown function and others available in the public databases were used to infer the general function of the URF as a new nucleotide triphosphosphatase. It is expected that as many new structures of URFs are solved over the next few years, the conceptual approaches described here will be useful in interpreting characteristics of such proteins even in the absence of a clear understanding of overall function or identification of specific substrates and products. 

Finally, we should mention some approximate calculations on H2. Jug77 has developed a semi-empirical version of the multiconfiguration SCF (MCSCF) method, using CNDO- and INDO-type approximations, and has reported the results of a double-configuration approach to Ha. It was shown that the eigenvalues of the EHF operator have physically interpretable characteristics and follow dissociation properly. Further results of this method should be very interesting. 

Interpretive Characteristics of PbB Values for Dose—Response Relationships... 

Interpretive Characteristics of PbP Values for Dose-Response Relationships... 

The most commonly used technique to assess this perceived efficacy is sensorial analysis, i.e. the scientific discipline used to measure, analyze and interpret characteristics of materials which are perceived by the human senses. This subject falls outside the scope of this book, but interested readers can find interesting literature on sensorial analysis of cosmetics (IFSCC, 1987 Civille and Dus, 1991 Piacquadio and Kligman, 1998 Civille and Meilgaard, 1999 Koehler and Maibach, 2000 Torres, 2001 Barkat et al, 2003 Musnier et al., 2004 Eisfeld et al., 2005 Sang-Woong et al., 2005 Coll, 2006). 

First, the typical characteristics of inspection problems which result in heterogeneous data are presented. Next, typical AI techniques which can be used for the automated data interpretation are presented. The applicabihty of the techniques to various inspection problems is discussed. Two example apphcations for automatic NDT data interpretation are briefly described, and finally, the conclusions are given. 

To simplify further discussion we would like to present in this section the four characteristics of NDT inspection which we think are of the most influence on the (options for) choice of any technique for automated interpretation of the data. 

However, several techniques have been developed according to the characteristics of the signal to be analyzed, and lead to interpretable time-frequency representation. 

The aim of this work which enter in a research project on NDT, is to conceive a system of aid for interpretation and taking decisions, on imperfections in metallic fusion welds, we have studied and tested several segmentation techniques based on the two approaches ( contour and regions ). A quantitative analysis will be applied to extract some relatives geometricals parameters. To the sight of these characteristics, a first classification will be possible. 

For interpretation of measuring results, calibration characteristics obtained on the samples in advance is used in the above instruments. However, if number of impediment factors increases, the interpretation of the signals detected becomes more complicated in many times. This fact causes the position that the object thickness T and crack length I are not taken into consideration in the above-mentioned instruments. It is considered that measuring error in this case is not significant. 

Customarily, it is assumed that e is unity and that ]l = p,cos 9, where 0 is the angle of inclination of the dipoles to the normal. Harkins and Fischer [86] point out the empirical nature of this interpretation and prefer to consider only that AV is proportional to the surface concentration F and that the proportionality constant is some quantity characteristic of the film. This was properly cautious as there are many indications that the surface of water is structured and that the structure is altered by the film (see Ref. 37). Accompanying any such structural rearrangement of the substrate at the surface should be a change in its contribution to the surface potential so that AV should not be assigned too literally to the film molecules. 

We must now mention, that traditionally it is the custom, especially in chemo-metrics, for outliers to have a different definition, and even a different interpretation. Suppose that we have a fc-dimensional characteristic vector, i.e., k different molecular descriptors are used. If we imagine a fe-dimensional hyperspace, then the dataset objects will find different places. Some of them will tend to group together, while others will be allocated to more remote regions. One can by convention define a margin beyond which there starts the realm of strong outliers. "Moderate outliers stay near this margin. 

A series of monographs and correlation tables exist for the interpretation of vibrational spectra [52-55]. However, the relationship of frequency characteristics and structural features is rather complicated and the number of known correlations between IR spectra and structures is very large. In many cases, it is almost impossible to analyze a molecular structure without the aid of computational techniques. Existing approaches are mainly based on the interpretation of vibrational spectra by mathematical models, rule sets, and decision trees or fuzzy logic approaches. 

Woodruff and co-workers introduced the expert system PAIRS [67], a program that is able to analyze IR spectra in the same manner as a spectroscopist would. Chalmers and co-workers [68] used an approach for automated interpretation of Fourier Transform Raman spectra of complex polymers. Andreev and Argirov developed the expert system EXPIRS [69] for the interpretation of IR spectra. EXPIRS provides a hierarchical organization of the characteristic groups that are recognized by peak detection in discrete ames. Penchev et al. [70] recently introduced a computer system that performs searches in spectral libraries and systematic analysis of mixture spectra. It is able to classify IR spectra with the aid of linear discriminant analysis, artificial neural networks, and the method of fe-nearest neighbors. 

M. E. Elyashberg, Infrared spectra interpretation by the characteristic frequency approach, in The Encyclopedia of Computational Chemistry,... 

Alkylidenehydrazinothiazoles (297) can be prepared either from 2-hydrazinothiazoles (549) or by direct heterocyclization (527). Their characteristic infrared bands have been reported (550). The main mass spectrometric peaks of (4-coumarinyl-2-thiazolyl)hydrazone (302) (Scheme 179) (134, 551) are situated at mle = 361. 244, 243, 118, 216, 202, 174, 117 the proposed interpretation of the fragmentation pattern should, however, be reconsidered. Scheme l80 summarizes some representative reactions of this class of compounds. 

At first glance splitting may seem to complicate the interpretation of NMR spectra In fact It makes structure determination easier because it provides additional information It tells us how many protons are vicinal to a proton responsible for a particular signal With practice we learn to pick out characteristic patterns of peaks associating them with particular structural types One of the most common of these patterns is that of the ethyl group represented m the NMR spectrum of ethyl bromide m Figure 13 15... 

Type 1 isotherms, as will be demonstrated in Chapter 4, are characteristic of microporous adsorbents. The detailed interpretation of such isotherms is controversial, but the majority of workers would probably agree that the very concept of the surface area of a microporous solid is of doubtful validity, and that whilst it is possible to obtain an estimate of the total micropore volume from a Type I isotherm, only the crudest guesses can be made as to the pore size distribution. 

The mass spectrum is characteristic for different substances and can be used like a fingerprint to identify a substance, either by comparison with an already known spectrum or through skilled interpretation of the spectrum itself (Figure 3.2). 

Several early interpretations of the polymerization mechanism have been proposed (1,17,29—31). Because of the complexity of this polymerization and insoluble character of the products, key intermediates have not ordinarily been isolated, nor have the products been characterized. Later work, however, on the resinification of furfural (32,33) has provided a new insight on the polymerization mechanism, particularly with respect to thermal reaction at 100—250°C in the absence of air. Based on the isolation and characterization of two intermediate products (9) and (10), stmcture (11) was proposed for the final resin. This work also explains the color produced during resinification, which always is a characteristic of the final polymer (33). The resinification chemistry is discussed in a recent review (5). 

Powder diffraction patterns have three main features that can be measured t5 -spacings, peak intensities, and peak shapes. Because these patterns ate a characteristic fingerprint for each crystalline phase, a computer can quickly compare the measured pattern with a standard pattern from its database and recommend the best match. Whereas the measurement of t5 -spacings is quite straightforward, the determination of peak intensities can be influenced by sample preparation. Any preferred orientation, or presence of several larger crystals in the sample, makes the interpretation of the intensity data difficult. 

If the perturbations thus caused are relatively slight, the accepted perturbation theory can be used to interpret observed spectral changes (3,10,39). The spectral effect is calculated as the difference of the long-wavelength band positions for the perturbed and the initial dyes. In a general form, the band maximum shift, AX, can be derived from equation 4 analogous to the weU-known Hammett equation. Here p is a characteristic of an unperturbed molecule, eg, the electron density or bond order change on excitation or the difference between the frontier level and the level of the substitution. The other parameter. O, is an estimate of the perturbation. 

J. D. Hem, Study and Interpretation of the Chemical Characteristics of Natural Water, 3rd ed., U.S. Geological Survey Water-Supply Paper 2254, U.S. Geological Survey, Reston, Va., 1985. 

Eor a number of cognitive or interpretive tasks, there are alternatives to mainstream knowledge-based systems that may be more appropriate, especially if adaptive behavior and learning capabihty are important to system performance. Two approaches that embody these characteristics are neural networks (nets) and case-based reasoning. 

Isoxazole dissolves in approximately six volumes of water at ordinary temperature and gives an azeotropic mixture, b.p. 88.5 °C. From surface tension and density measurements of isoxazole and its methyl derivatives, isoxazoles with an unsubstituted 3-position behave differently from their isomers. The solubility curves in water for the same compounds also show characteristic differences in connection with the presence of a substituent in the 3-position (62HC(17)1, p. 178). These results have been interpreted in terms of an enhanced capacity for intermolecular association with 3-unsubstituted isoxazoles as represented by (9). Cryoscopic measurements in benzene support this hypothesis and establish the following order for the associative capacity of isoxazoles isoxazole, 5-Me, 4-Me, 4,5-(Me)2 3-Me> 3,4-(Me)2 3,5-(Me)2 and 3,4,5-(Me)3 isoxazole are practically devoid of associative capacity. 

While the manufacturers of measurement devices can supply some information on the dynamic characteristics of their devices, interpretation is often difficult. Measurement device dynamics are quoted on varying bases, such as rise time, time to 63 percent response, settling time, and so on. Even where the time to 63 percent response is quoted, it might not be safe to assume that the measurement device exhibits first-order behavior. 

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