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tted through the front wall of the chamber. The entire chamber is disinfected using strong bleach before it is brought into service. Water owing from the spring chamber outlet pipe can either be collected in containers brought by individual users (a common arrangement in developing countries), or else the pipe can be connected to a water distribution network, which will carry the water to individual taps elsewhere. It is often worth installing a few chambers to capture the ows of several small springs, which together give a more useful yield than any one spring could provide on its own. In some cases, the same effect has been achieved by driving tunnels into hillsides to intersect the fracture systems that feed a number of discrete springs. In terms of resource assessment, springs offer us only Hobson s Choice (take it or leave it). A spring will only ow within a given discharge range, and there is really nothing we can do to increase the yield of a spring. To assess the resource available from a given spring, therefore, we need to measure its ow rate regularly over a period of at least one year (and preferably many years), and then construct a ow duration curveiii (see Box 5.2). Once a ow duration curve is available, we can use it to estimate percentile ows (as shown in Figure 5.9), with Q95 being a particularly popular choice for resource assessment purposes. Given Q95, we can judge

Find an admissible ensemble assignment B Color GB using some heuristic graph coloring procedure For the second stage, a plethora of different graph coloring algorithms is already available In fact, it turns out that fairly simple sequential coloring methods like Br laz DSATUR [2] or Matula/Beck s smallest-last algorithm [16] usually perform very well on the kind of geometric graphs arising as models of broadcast networks So we can concentrate on the first step, which is a kind of simultaneous bin packing problem The difficult part here is to devise a packing strategy that reduces the global number of required colors instead of merely optimizing the packings for individual areas Of course, no single strategy will work with all area graphs equally well, so let us take a look at the two extreme cases: Independent (edgeless) graphs.

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FIGURE 4.4 The bi-static radar principle (Pt is the transmitted power, Gt and Gr are the transmitted and received antenna gains, dt and dr are the distances of the transmitter and receiver to the equivalent surface, respectively).

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In this case the problem decomposes into |V | indeR pendent bin packing problems, one for each vertex of G, and we have that M (G) = max{pM(Rv) : v V} Complete graphs In this case, all distinct ensembles will have to be assigned differR ent frequency blocks, hence M (G) = pM(RV) (Here and in the following, by RW we denote the set of all requested services in a set of areas W V, ie, RW = w WRw Thus, in particular, RV is the set of all requested services) This suggests two different types of ensemble packing heuristics that we would expect to work reasonably well with sparse and dense graphs, respectively For sparse graphs, we will pack the individual requirement sets independently of each other.

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For dense graphs, we will pack the entire collection of requested services, and then assign to each vertex those ensembles that are needed to satisfy the supply requirements at that vertex For an arbitrary graph, we might try both approaches and see which one works best Since the bin packing problem is NP-complete, we need some heuristic for solving it A simple method, which works reasonably well, is the so-called first-fit (FF) algorithm, which considers the items (aka services) to be packed in some order, and puts each item into the first bin (aka ensemble) into which it fits It is well-known that if the items are ordered by decreasing sizes, this method never performs worse than 11/9 times the optimum (asymptotically) (We refer the reader to [7] for the details of this algorithm and its analysis.

Excel 2002 provides several new options in the area of sheet protection. Select Tools Protection and you ll see the new Protect Sheet dialog box. This allows you to specify exactly which actions can be performed on a protected sheet. Also, the Save Options dialog box has an Advanced button. Clicking this button allows you to set the encryption level of the workbook.

) In the following, we use FF(R, M) to denote the set of ensembles returned by the firstfit algorithm when applied to a set of required services R S and the ensemble size M We can employ the first-fit algorithm for packing ensembles using the two strategies sketched out above, as follows: Simultaneous First-Fit (SFF) Algorithm: Bv = FF(Rv, M) v V (125).

relates to the additive contribution, either positive or negative, of each attribute. For example, consider the log posterior odds ratio for a new customer who belongs to both the International Plan and the VoiceMail Plan. Then for the International Plan we have log p(I |C) p(I |C) = log 0.2836 = 0.6378 0.0653

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