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Note that this replacement need not be carried out explicitly, because is maintained by projection (see above). Therefore we can work directly with the projection of a relation of imprecise tuples as defined in Definition 5.1.4, which indicates equally well which tuples of the subspace are possible. Note that in the relational case the tuple weights are ignored. In the probabilistic case it is usually assumed that the given database represents a sample of independent cases, generated by some random process that is governed by a multivariate probability distribution. With this presupposition, the relational projection operation needs to be extended only slightly, in order to estimate a marginal probability distribution from a database of precise tuples. All we have to do is to take the tuple weights properly into account. This is done by summing for each precise tuple tX of the subspace defined by the set X of attributes the weights of all tuples tU in the database the projection of which is equal to tX (hence the name sum projection ). The result of this operation is an absolute frequency distribution on the subspace, which can be represented as a database on the subspace: the weight function states the absolute frequency of each tuple. From this frequency distribution a marginal probability distribution is estimated using standard statistical techniques. We may, for example, use maximum likelihood estimation. That is, we may estimate the parameters in such a way that the likelihood of the data, that is, the probability of the data given the model and its parameters, is maximized. This yields [Larsen and Marx 2005] a1 dom(A1 ) : . . . an dom(An ) : wX X p

, 95 Grisso, T, 59 Grunder, T M, 274 Gutkin, T B, 218, 219, 220, 223 Haas, L J, 1, 4, 5, 20 Hakola, S R, 169, 174 Hall, S, 56 Hammill, D D, 93 Handelsman, M M, 5, 6 Hansen, J C, 65 Hansen, N D, 12, 20, 21, 190, 191, 206, 223, 359, 360, 361 Hanson, M J, 81, 190, 191, 232, 234, 359, 360 Hardy, B S, 202 Hare, R, 20 Harris, A, 207 Harris, K R, 209 Harrison, P L, 244 Hartsell, T L, 49, 79 Hartshorne, T S, 167, 186, 227 Harvey, V S, 98, 286, 287, 291, 293, 294, 295, 296 Havey, J M, 51 Hays, P A, 191, 360 Hehir, T, 77 Helm, D T, 226 Helton, G B.

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In the late 1990s and early 2000s a gloomy picture was painted as a result of the move towards global sourcing. Several larger clothing companies such as J. Baird Ltd closed factories and others such as Dewhurst in the north-east of England who relied on a few major customers such as Marks and Spencer have suffered from this loss of business. There has been a reduction in the clothing manufacturing industry in the UK and many foreign companies have changed from both designing and manufacturing to one of merely cut, make and trim (CMT) for other people s designs. Other parts of this chapter look at the way forward for the UK manufacturing industry. There is undoubtedly a role that it can play in the international sourcing market if it exploits the strengths of exibility and quality and moves away from

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, 302 Henderson, D H, 248 Henker, B, 214 Henning-Stout, M, 233, 260, 285, 359 Henry, K, 59 Herlihy, B, 68, 69 Herlihy, S, 68 Heumann, J E, 125 Himes, B S, 65 Hindman, S E, 209, 248 Hofer, P T, 56, 57, 60, 188 Hoff, K E, 213, 214 Holmes, D S, 59, 275, 276 Hopkins, B R, 45 Horton, C B, 201, 215, 235 Hostetler, A J, 282 Hubbard, D D, 234 Hubsch, A W, 31, 36, 41, 42, 43 Hughes, J N, 4, 220, 221 Hummel, D L, 34, 48, 53, 61, 62, 68, 202 Humphreys, K L, 19 Hunley, S A, 44, 83, 187, 218 Hurley, C M, 224 Hyman, I A, 245, 247 Ikeda, M J, 208 Ingraham, C L.

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The sum (3.9) is the overall system utility (note that since ij are integers, they must all be either 0 or 1). Given an optimal solution to this problem (i.e. a set of integers ij that maximizes (3.9) subject to (3.10)), an optimal assignment is constructed by assigning mobile node i to static node j only when ij = 1. If the mobile nodes utilities can be collected at one machine, then a centralized linear programming approach (e.g. Kuhn s Hungarian method [168]) will nd the optimal allocation in O(mn2 ) time. Alternatively, a distributed auction-based approach (e.g. Bertsekas s auction algorithm [169]) will nd the optimal allocation, usually requiring time to be proportional to the maximum utility and inversely proportional to the minimum bidding increment. The two approaches (i.e. centralized and distributed) represent a trade-off between solution time and communication overhead. To implement a centralized assignment algorithm, n2 messages are required to transmit the utility of each mobile node for each visit; an auction-based solution usually

The classic disease of the neuromuscular junction is myasthenia gravis. Characteristics of myasthenia gravis: uncommon; no wasting; tone normal; weakness; fatiguability; re exes normal; positive response to anticholinesterase. The pattern of muscle involvement in this rare disease: ocular muscles common: ptosis; diplopia; bulbar muscles fairly common: dysarthria; dysphagia; trunk and limb muscles less common: limb weakness; trunk weakness; breathing problems. More common paralysis due to neuromuscular blockade is that which is produced by anaesthetists during operative surgery. Myasthenia gravis would not be a likely diagnosis in a patient presenting with left leg malfunction.

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