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Shifting selected colors . . . . . . . . . . . Shifting predefined colors . . . . . . . . . . Using the Variations command . . . . . . . Enhancing colors in a compressed image . Making Custom Brightness Adjustments . . . . . The Levels command . . . . . . . . . . . . . The Curves command . . . . . . . . . . . . Gradient maps. . . . . . . . . . . . . . . . . Practical applications: continuous curves. Practical applications: arbitrary curves . . Adjustment Layers . . . . . . . . . . . . . . . . . The advantages of layer-based corrections Correcting a flat image using layers . . . .

Facilities Management Electrical/electronic Multimedia However, AutoCAD has many other lesser-known uses, such as pattern making in the garment industry, sign making, and so on. In this book, I try to provide examples from many fields. The world of AutoCAD is very broad, and you can learn from seeing the many approaches that AutoCAD makes possible.

If the menu group is the same as that of the menu where you re placing the macro, you can leave it out. The menu group name is the same as the customization group name, which is usually the name of the file for example, ACAD for acad.cui. The menu name is either the menu s label or an alias. The different sections or menu types have their own special abbreviations for menu swapping, as shown in Table 33-2.

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FIGURE 15.8 A 3D look, with a shadow cast beneath the layer (top) or simulating a shadow seen through the layer (bottom) can be created easily with the Drop Shadow or Inner Shadow effects.

Figure 12-11: Drag a layer between two other layers to make the all-important black bar appear (left). Then release the mouse button to change the hierarchy of the layer (right).

(b) Near ' y k ( S k ) = 0.5

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Case study: planning a timber development Table 21.1 Stages in project optimization Stage Stage 1 Activities Market appraisal Market requirements study Resources appraisal Resources use strategy Production flow analysis Capital cost estimation Operating cost appraisal Financial and economic analysis Schedule analysis (time and cost) Cost-benefit analysis Detailed project optimization Outputs Project feasibility decision Outline production targets

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index and tness in obese children and adolescents a clinical experience European Journal of Pediatrics, 161, 449 54 Elster, AB and Kuznets, NJ (1994) AMA Guidelines for Adolescent Preventive Services (GAPS) Recommendations and Rationale, American Medical Association, Illinois Fairbank, L, O Meara, S, Renfrew, MJ et al (2000) A systematic review to evaluate the effectiveness of interventions to promote the initiation of breastfeeding Health Technology Assessment Programme, 4, 1 171 Farmer, RDT and Miller, DL (1983) Lecture Notes on Epidemiology and Community Medicine, 2nd edn, Blackwell Scienti c Publications, Oxford Feldman, W and Beagan, BL (1994) Screening for childhood obesity, in Canadian Task Force on the Periodic Health Examination, Canadian Guide to Clinical Health Care, Health Canada, Ottawa, pp 334 44 Geckova, A (2002) Slovakia a national case Baby friendly hospitals in Europe.

Although output 4.6 is ungrammatical, it does preserve some of Poe s writing style. For example, it is appropriate (though accidental) that it starts off with hideous me. And it does include many words with negative connotations: hideous, scream, brute, blood, destroying, and so forth. On the other hand, reading the original story is much more enjoyable. An example of a word frequency text analysis is done in section 3.7.1, which illustrates Zipf s law applying program 3.3 to Dickens s novel A Christmas Carol. This program produces the counts needed to create figure 3.1, Zipf s law implies words that appear once are the most common, which suggests that authors do not use all the words they know in a text (empirical evidence of this is given in the next section). Hence a text has many zero counts for words known by the author. As noted in section 4.2.2, this implies that these word frequencies have biases. This is an important conclusion, and one based on a bag-of-words model, which shows that a simplistic language model can produce interesting results. Finally, to finish this chapter, we analyze how word frequencies depend on the size of the text used. This reveals an important difference between coin tossing and texts. Unfortunately, this difference makes the latter harder to analyze.

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