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Now you can double-click the top layer in the clipping mask the knockout layer and, in its Layer Style dialog box, choose either Shallow or Deep for the Knockout option to create the knockout. Assuming you deselected the Blend Clipped Layers as Group option for the base layer, the options behave as follows: n Shallow causes the knockout layer to bore down through the other layers in the clipping mask to reveal the first visible layer below the base layer of the mask. (As with group knockouts, if the first visible layer is the Background layer, then the effect of Shallow is identical to that of Deep.) n Deep makes the knockout layer drill right down through to reveal the Background layer; if the Background layer is hidden or not present, it drills through to reveal transparency.

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Table B-1: Shell Special Characters (continued)

Skeletal muscle has been little studied with respect to contaminant exposure. The main pathological change is degeneration and necrosis of the myo brils (Haensley et al., 1982; Khan & Kicenuik, 1984). Clearly, signi cant damage to the musculature will adversely affect the performance of the sh for feeding, spawning migrations and predator avoidance.

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As we mentioned in 1, The Random House unabridged dictionary gives a de nition of learning as the act or process of acquiring knowledge or skill In this chapter, we will see how expert systems can learn in two very simple ways The rst way will add new facts to the programs knowledge base; the second way will add new rules The eld of machine learning has received much attention for many years; see for example, Mitchell (1997) and Kecman (2001) We will restrict ourselves in this chapter to two basic and fundamentally different ways in which a rule-based system can learn In one technique, we write our rules to interpret a database, and add facts to the database as the program learns from experience; in the other technique the program creates new rules from its experience.

2 exp( 2 t2i )

Recursive estimator : Kalman gain : where Mean square error :

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The programs to illustrate these two techniques are different ways of solving the same problem: pavlov1par and pavlov2par, which emulate learning by Pavlovian classical conditioning Pavlov1 learns by adding rules to deal with important external stimuli; Pavlov2 learns by adding stimulus data to a database Each technique has its own programming advantages and disadvantages The learning techniques are so simple that they might seem trivial, but the importance of Pavlovian learning is exceedingly well known The combination of simplicity and power is sometimes called elegance, and these simple and elegant techniques have a lot of power The idea of Pavlovian learning, often called classical conditioning and more recently behavior modi cation, came originally from Pavlov s experiment on ringing a bell, then offering food to a dog.

The conductivity of a solution is measured using an AC bridge with a two-electrode conductance cell on one arm (Fig. 5.40 (a)); a balance is sought, manually or automatically, by adjusting the variable resistance and capacitance in another arm of the bridge. Usually AC voltage of a few volts and * 1 kHz is applied to the cell. The impedance caused by the double-layer capacity at the electrodes does not affect the measured values of conductivity. In some cases, the conductance is measured with a four-electrode cell, as shown in Fig. 5.40 (b). For practical methods of measurement, see the reviews in Ref. [25]. Conductimetry is used to detect and determine ionic species in solution. It plays important roles in environmental analyses and in the detection of ion chromatography. It is also used in acid-base, precipitation and chelometric titrations to detect end-points. However, the biggest advantage of conductimetry is displayed in the fundamental studies of solution chemistry. Its applications to chemical studies in non-aqueous solutions will be discussed in 7.

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