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One of the nice features of SWAT is the fact that the documentation to which it s linked is always up-to-date. SWAT takes its documentation directly from the manual pages, which are part of the SAMBA package. When you upgrade your SAMBA packages, you automatically upgrade the manual pages, and the documentation linked on the main SWAT page is automatically upgraded as well.
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possible. Replicate measurements should never be present in both the calibration and test sets. This risk exists with a random selection which is proposed in current NIR software. If it is the simplest way to select a test set, it leaves the selection to the computer. Manual selection is recommended since it gives full control over the selection of a test set. Multiple regression programs also calculate auxiliary statistics, designed to help decide how well the calibration ts the data, and how well it can be expected to predict future samples. For example, two of these statistics are the standard error of calibration (SEC) and the multiple correlation coef cient (R). The SEC (also called standard error of estimate, or residual standard deviation) and the multiple correlation coef cient indicate how well the calibration equation ts the data. Their formulas are given in Table 3. The SEC has the same units as the dependent variables, and it re ects the differences between the instrumental value for the analyte of interest and the reference laboratory value. It expresses the modeling error and cannot be used to estimate future prediction errors. In the absence of instrumental error, SEC is only the measure of reference laboratory error. It is an indication of whether calculation using the calibration equation will be suf ciently accurate for the purposes for which it has been generated. In practice, SEC is less accurate than the reference laboratory, even in the absence of instrumental error, if the wavelength range used as independent variables in the calibration does not account for all interference in the samples or if other physical phenomena are present. The detection limit and sensitivity, or signal-to-noise, de nes instrument performance for a speci c NIR application. Detection limits can usually be approximated for any NIRS method as equal to three times the SEC for the speci ed application. Sensitivity for quantitative NIR methods can typically be evaluated as the slope of the calibration line for the concentration of the examined analyte (y axis), versus the change in optical response (x axis), between samples of varying concentration. Sensitivity, from a purely instrumental point of view, is expressed as a signal-to-noise or peak height ratio for a particular compound versus peak-to-peak noise at some absorbance (usually zero). However, in a practical sense, the above considerations do not really matter in NIR spectroscopy. This is due to the fact that the applications developed for practical NIR are mostly based on empirical calibration methods and that the calibration is speci c to the problem of interest. It is a characteristic of calibration equations using multivariate mathematical techniques to compensate for the common variations found in noisy chemical samples and imperfect instrumental measurements. This is why properly calculated and validated NIR calibration models are robust and work extremely well. The multiple correlation coef cient R is a dimensionless measure of how well the calibration ts the data. R can have values between 1 and +1, but in a calibration situation only positive values exist. A value close to zero indicates that the calibration fails to relate the spectra to the reference values. As the correlation coef cient increases, the spectra become better and better predictors of the reference values. Because the multiple correlation coef cient is dimensionless, it is a useful way of comparing data or results with different units, and that are dif cult to compare in other ways. However, its value gives no indication of how well the calibration equation can be expected to perform on future samples.
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Shift: Relates work shifts and employee data. Customer: This entity is fairly self-contained. Other entities such as Order and Course refer to it but it doesn t refer to others.
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Key Image Note Profile: Improved communication between the radiologist and referring physician
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In the first example, I used the Channel Mixer to replace the red channel with the blue. I did this by setting the Output Channel to Red, changing the Red value to 0 percent and the Blue value to 100 percent. The result is a green woman floating in a green sea under a purple sky. To achieve the next example, I again started from the original RGB image and used Channel Mixer to replace the green channel with the red. The result this time is a yellow woman against a deep blue background. To create the purple woman in a green world on the right side of Color Plate 4-4, I replaced the blue channel with the red. You can create more interesting effects by using Color Mixer to swap the contents of color channels. For example, in the lower left example of Color Plate 4-4, I swapped the contents of the red and blue channels to create a blue woman on a green sea under an orange sky. To accomplish this, I set the Output Channel to Red, set the Red value to 0 and the Blue to 100. Then I switched to the Blue channel (Ctrl+3) and set the Red value to 100 and the Blue to 0. The next two examples along the bottom of Color Plate 4-4 show the results of swapping the red and green channels (for a bright green woman) and the green and blue channels. Because the green and blue channels contain relatively similar data, this produces the subtlest effect, chiefly switching the sea and sky colors and turning the swimsuit pink.
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Last: The Last option selects the last object that was created and is visible within the current view. Often you create an object and then want to move or copy it. In this situation, the Last option is an easy way to select the object that you just created. Crossing: Crossing enables you to pick two diagonal corners that define a window. All objects entirely or partly within the window are selected. As you move your mouse to specify the second diagonal corner, a green transparent rectangle appears to help you preview which objects will be selected. Figure 9-15 shows the process of picking the window. Figure 9-16 shows the result the selected objects are highlighted.
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