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In this way, almost real-time measurement of demand can be achieved and many stores now have replenishment deliveries two or three times per day The data from stores is usually sent back to regional headquarters There, planning programs may modify the immediate demands with forecast requirements, including information from short-term weather forecasts, analyses of current trends and cyclical data from earlier years, before communicating with warehouses and to the transportation eets that deliver the goods The warehouses must themselves have ways of accurately knowing what goods they have in stock For a variety of reasons, of which pilfering is not the least important, this is not as simple as it seems Goods must then be batched for loading onto eets and the eets guided securely to the desired destination One dream of the distribution business has been cross docking wherein there are no, or at least, minimal warehouses.

The ability of a complex expert system to run in real-time on-line to a process has strong implications for event detection, especially for fault detection and analysis. While such a system resembles fuzzy control systems insofar as input is concerned, the programming techniques for event detection and analysis are quite different and more complex than for fuzzy control. We have written two such programs, both for hospital intensive care units (ICU). The rst and simplest was for suppression of false alarm detections in a neonatal ICU for low blood oxygen saturation. As an infant squirmed around, a simple threshold method detected many more false alarms than true ones. The second was for detection and classi cation of several alarm conditions from pulmonary artery pressure catheters in a heart transplant ICU and an open-heart surgery ICU. Not only was the detection and classi cation dif cult, but like the rst problem there were many measurement artifacts that had to be accounted for. Both projects were successful.

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Goods arriving from each supplier to a dock rather like a railway platform, are spilt into their outgoing order sizes and loaded onto lorries, in more or less one operation By and large, this remains a dream, but is a good conceptual model and an aspiration for a process designer Direct vending to the customer certainly does not let this become any easier: order quantities are much reduced, whereas order uctuation probably increases..

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Mean number and height of planted and invader trees are given for the 10 stands evaluated by Allen (1990).a

By drawing the demand and supply curves for a good or service together on the same graph, you can easily compare the benefits and costs of producing each and every unit of output. To see how this is done, take a look at Figure 11-1, on which we draw a demand curve, D, and a supply curve, S.

In classical data streaming [5,43,56,57], input data are accessed sequentially in the form of a data stream = x1 , ..., xn and need to be processed using a working memory that is small compared to the length n of the stream. The main parameters of the model are the number p of sequential passes over the data, the size s of the working memory, and the per-item processing time. All of them should be kept small: typically, one strives for one pass and polylogarithmic space, but this is not a requirement of the model. There exist at least three variants of classical streaming, dubbed (in increasing order of generality) time series, cash register, and turnstile [57]. Indeed, we can think of stream items x1 , ..., xn as describing an underlying signal A, that is, a one-dimensional function over the reals. In the time series model, each stream item xi represents the ith value of the underlying signal, that is, xi = A[i]. In the other models, each stream item xi represents an update of the signal: namely, xi can be thought of as a pair (j, Ui ), meaning that the jth value of the underlying signal must be changed by the quantity Ui , that is, Ai [j] = Ai 1 [j] + Ui . The partially dynamic scenario in which the signal can be only incremented, that is, Ui 0, corresponds to the cash register model, while the fully dynamic case yields the turnstile model. 8.2.2 Semi-Streaming

Variations of load voltages with time in the range 0 50 ps for several values of interconnection lengths are shown in Fig. 3.3.3 while Fig. 3.3.4 shows the dependence of delay time on interconnection lengths in the range 20 1000 mm. Figure 3.3.4 shows that the delay time increases almost linearly for interconnection lengths above about 100 mm. For lengths below about 100 mm, the delay is caused

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