Visualization correlations

R D and technology plans can also be analyzed with widely used portfoHo analysis (47), which provides visual correlations between variables such as potential value, probabiUty of success, time-to-completion, technology maturity, and project cost as a means of optimizing the plaimed effort. The analyses can be used to redirect effort and reallocate resources as a project progresses. 

Visual correlation between geological, geochemical and geophysical data is... 

Initial staging. The first study comparing the diagnostic accuracy of integrated [ F]-FDG-PET/CT with CT alone, [ F]-FDG-PET alone and conventional visual correlation of [ F]-FDG-PET and CT in patients with NSCLC was published by Lardinois et al. [43]. Integrated [ F]-FDG-PET/CT provided additional information beyond that provided by visually correlated [ F]-FDG-PET and CT in 41% of the included patients. p F]-FDG-PET/CT also revealed to be significantly... 

Comment on the graph and discuss whether there is any visual correlation between the two variables. Consider natural processes and human activities when discussing whether there is a likely causal relationship between the two variables. 

When analyzing the whole measurement period, the measured HR by invasive ECG and ICG visually correlate perfectly but show a temporal shift (see fig. 5a). 

The CT-guided insertion of catheters has some major drawbacks. During the intervention, only non-enhanced fluoroscopy can he used to determine the target, often visually correlated with and corrected using MRI prints. Unfortunately,... 

Overall void fraction measurements (made by a radar probe detecting the surface level averaged over several seconds) are shown in Figure 11-24. Similar data confirm the lower gas holdup in heated systems. In this figure the sharp fall-away in void fraction at low shaft power (i.e., at low speeds) seen at room temperature is visually correlated with the loss of radial pumping action by the asymmetric BT-6 impeller. 

After converting experimental IR and VCD to molar absorptivity units (M cm ), both observed and calculated spectra should be plotted in stack mode with VCD above the IR on the same wavenumber frequency scale. Plotting in this manner allows visual correlation between IR peak features and VCD peak features. 

To make the correlation results applicable for the field development process it may be desirable to display the correlated units in their true structural position. For instance if water injection is planned for the field, water should enter the structure at or below the owe and move upwards. Hence the correlation panel should visually show the sand development in the same direction. For this, all markers on the panel are displayed and connected at their TVSS position (Fig. 5.43). This is called a structural correlation. 

The visibility level VL of special viewing tasks may be estimated from the the correlations of the quantitative visual recognition. For an adaptation luminance of 10 cd/m2 and given contrasts (C = 0.5 1.0 2.0), VL is described in Fig. 1 in dependance on the object dimension 3 in angular minutes. An object dimension of T represents a circle with a diameter of 0 12 mm in a distance of 0 4 m. 

Optical Properties. When light falls on an object, it is either partially absorbed, reflected, or transmitted. The behavior of the object as it relates to each of these three possibiUties determines visual appearance. Optical properties of fibers give useful information about the fiber stmcture refractive indexes correlate well with fiber crystalline and molecular orientation and birefringence gives a measure of the degree of anisotropy of the fiber. 

Color Difference Evaluation. Shade evaluation is comparable in importance to relative strength evaluation for dyes. This is of interest to both dye manufacturer and dye user for purposes of quaUty control. Objective evaluation of color differences is desirable because of the well-known variabihty of observers. A considerable number of color difference formulas that intend to transform the visually nonuniform International Commission on Illumination (CIE) tristimulus color space into a visually uniform space have been proposed over the years. Although many of them have proven to be of considerable practical value (Hunter Lab formula, Friele-MacAdam-Chickering (FMC) formula, Adams-Nickerson formula, etc), none has been found to be satisfactorily accurate for small color difference evaluation. Correlation coefficients for the correlation between average visually determined color difference values and those based on measurement and calculation with a formula are typically of a magnitude of approximately 0.7 or below. In the interest of uniformity of international usage, the CIE has proposed two color difference formulas (CIELAB and CIELUV) one of which (CIELAB) is particularly suitable for appHcation on textiles (see Color). 

The prediction of drop sizes in liquid-liquid systems is difficult. Most of the studies have used very pure fluids as two of the immiscible liquids, and in industrial practice there almost always are other chemicals that are surface-active to some degree and make the pre-dic tion of absolute drop sizes veiy difficult. In addition, techniques to measure drop sizes in experimental studies have all types of experimental and interpretation variations and difficulties so that many of the equations and correlations in the literature give contradictoiy results under similar conditions. Experimental difficulties include dispersion and coalescence effects, difficulty of measuring ac tual drop size, the effect of visual or photographic studies on where in the tank you can make these obseiwations, and the difficulty of using probes that measure bubble size or bubble area by hght or other sample transmission techniques which are veiy sensitive to the concentration of the dispersed phase and often are used in veiy dilute solutions. 

Comparison of Models Only scattered and inconclusive results have been obtained by calculation of the relative performances of the different models as converiers. Both the RTD and the dispersion coefficient require tracer tests for their accurate determination, so neither method can be said to be easier to apply The exception is when one of the cited correlations of Peclet numbers in terms of other groups can be used, although they are rough. The tanks-in-series model, however, provides a mechanism that is readily visualized and is therefore popular. 

The success of separation of colored compounds is usually monitored visually. Such compounds absorb a particular portion of the polychromatic (white) light in the visible wavelength range. The remaining radiation (complementary radiation) is reflected and detected by the eye it determines the color of the substance zone. Table 1 correlates the wavelengths, colors and complementary colors. 

In spite of its simplicity and the visual similarity of this equation to Eq. (7), we would like to note that Eq. (11) leads to a nontrivial thermodynamics of a partially quenched system in terms of correlation functions, see, e.g.. Ref. 25 for detailed discussion. Evidently, the principal route for and to the virial theorem is to exploit the thermodynamics of the replicated system. However, special care must be taken then, because the V and s derivatives do not commute. Moreover, the presence of two different temperatures, Pq and P, requires attention in taking temperature derivatives, setting those temperatures equal, if appropriate, only at the end of the calculations. 

Among cluster probabilities derived by the PPM, multibody correlations including triangle and tetrahedron cluster probabilities are not input in the simulation procedure. It is, however, worth noting that the agreements between the calculated multibody correlations and resultant ones in the simulated crystal are fairly reasonable. This is rather encouraging to go further discussions of the visualized configuration. 

Equal suspension of particles referenced to visual appearances and physical sample testing. Empirical correlations generalized to apply to most problems. 

Once a linear relationship has been shown to have a high probability by the value of the correlation coefficient (r), then the best straight line through the data points has to be estimated. This can often be done by visual inspection of the calibration graph but in many cases it is far better practice to evaluate the best straight line by linear regression (the method of least squares). 

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