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During both volcanic eruptions and some earthquakes, shallow groundwater can be forced to the surface, leading to liquefaction of loose sediments, which contributes to the generation of devastating mud ows High groundwater heads are also responsible for the triggering of many landslides (Box 81; Sections 821 and 831) At more modest scales, groundwater is implicated in many forms of subsidence (Section 823), ie the localized lowering of the ground surface, forming closed depressions Such subsidence depressions can be hazardous if they develop beneath buildings or roads, or in other locations where humans or animals might be injured by falling into them Groundwater is implicated in triggering subsidence in a range of hydrogeological environments (Section 822); some of the most dramatic examples have been induced by human activities (Sections 831 and 832).

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This kind of model is related to the partial adjustment model used in sociometrics (eg, Coleman, 1968; Tuma & Hannan, 1984), and has proven useful in recent studies of physical growth (see Hauspie, Lindgren, Tanner, & Chrzastek-Spruch, 1991) More complex linear (and nonlinear) models have been used to represent growth Some of these share the common feature of a piecewise model applied to different age or time.

The vertical bar (|) is the logical OR operator and works sort of like addition in this context. You can also build up a byte incrementally using the |= operator:

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The chart is an XY chart. The X and Y data are generated using formulas stored in columns A and B. The Scrollbar controls at the top let you adjust the three parameters that determine the look of the chart. These controls are linked to cells B1, B2, and B3. These are controls from the Forms toolbar, and are not ActiveX controls. In addition, the chart has a Random button that generates random values for the three parameters.

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In order to address this issue, wireless fair queueing algorithms differentiate between a nonbacklogged flow and a backlogged flow that perceives channel error. A flow that is not backlogged does not get compensated for lost channel allocation. However, a backlogged flow f that perceives channel error is compensated in future when it perceives a clean channel, and this compensation is provided at the expense of those flows that received additional channel allocation when f was unable to transmit. Of course, this compensation model makes channel errors transparent to the user to some extent, but only at the expense of separation of flows. In order to achieve a trade-off between compensation and separation, we bound the amount of compensation that a flow can receive at any time. Essentially, wireless fair queueing seeks to make short error bursts transparent to the user so that long-term throughput guarantees are ensured, but exposes prolonged error bursts to the user. 8.2.4.2 Separation versus Compensation Exploring the trade-off between separation and compensation further, we illustrate a typical scenario and consider several possible compensation schemes. Let flows f1, f2, and f3 be three flows with equal weights that share a wireless channel. Let f1 perceive a channel error during a time window [0, 1), and during this time window, let f2 receive all the additional channel allocation that was scheduled for f1 (for example, because f2 has packets to send at all times, while f3 has packets to send only at the exact time intervals determined by its rate). Now, suppose that f1 perceives a clean channel during [1, 2]. What should the channel allocation be During [0, 1], the channel allocation was as follows:

The basic steps to install a wired print server are as follows:

A hydro-geohazard well known to all a cionados of Hollywood movies (especially those of the western or jungle adventure genres) is quicksand (Section 822): to judge from its frequency of deployment as a dramatic device, one.

Choosing the youngest transaction as the victim Choosing the shortest transaction (transaction with fewest required data items) as the victim Choosing the transaction with the lowest restart overhead as the victim

13 9:3:4 12:1:2 14:1:3

23

We de ned the entropy of a single random variable in Section 2.1. We now extend the de nition to a pair of random variables. There is nothing really new in this de nition because (X, Y ) can be considered to be a single vector-valued random variable. De nition The joint entropy H (X, Y ) of a pair of discrete random variables (X, Y ) with a joint distribution p(x, y) is de ned as H (X, Y ) =

Figure 2-4

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