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Another way to look at consumer decisions is the level of involvement the consumer has in the decision. Consumers differ considerably in terms of their interest in fashion. Even those people who are very interested in fashion may be more interested in some types of garment than in others. A common way consumer theorists have classi ed consumer decisions is into high- and low-involvement purchases. This classi cation, in part, re ects different theoretical paradigms, although some signi cant attempts at synthesis have emerged over recent years. The level of involvement depends on the person, the purchase object and the time and place of purchase. For one consumer, the purchase of a pair of socks may involve considerable deliberation and visiting several stores to make comparisons in order to obtain a product that meets fairly precise speci cations in terms of colour, size and construction. For another consumer, the purchase of socks may be relegated to a simple commodity purchase undertaken with little conscious thinking through and they may be selected along with other low-involvement purchases in a supermarket. The implication for fashion marketers is to nd out the level of involvement of the target market or markets and design the marketing mix accordingly. A central issue is the provision of marketing information to consumers who may or may not want or use it. A connection between low-level involvement and impulse buying can be shown and this has a bearing on the relative proportion of the promotional budget that goes on in-store promotions rather than on advertising. If consumers do not really pay much attention to information about some fashion products, but simply make decisions in the store, it would be more productive to concentrate promotional efforts in-store.

images (a window within a personal computer screen for example) that are a little jerky, with poorer de nition and which tend to break-up if there is too much movement, can be delivered using as little as 64 kbit/s. A succession of still pictures, refreshed in less than a second, can be sent by domestic modem connection across the Internet, under certain circumstances. Because of the need to conserve bandwidth, all practical coding methods for video, and some for speech, introduce a delay between the capturing of the picture by the TV camera and the generation of the bit stream. There is a further time taken to pass across the network and, nally, a further delay between the reception of the data and its conversion to a picture on a screen (Figure 2.1). Some of this delay is simply due to the time taken for the coding algorithm to work, but some is also due to the fact that the algorithm involves

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The value of p can be determined by a compromise between the desired accuracy and the computation time and is chosen to be 0.8. Summation in Eq. (4.4.19) usually converges very slowly; it takes more than 5000 terms to achieve an accuracy of four signi cant digits. To overcome this problem, we can use the Wynn algorithm [25, 26] to accelerate the summation. The algorithm can be described as follows: For a summation series S de ned as S we de ne a 2D array as em em p 1 p 1 with em 0 0 e m Sm 1 4:4:24 4:4:25 1 m 1 e m ep p p 1; 2; 3; . . . 4:4:23

The knowledge structure [ie, r e ] that is learned may be in the form of a set of rules or a decision tree, which effectively selects a class or cluster of cases, Y 2 P fC1 ; C2 ; C3 ; ; Cn g, where P denotes a partition, from the case library (CB) based on the query case, e Determination of the case feature weights is carried out by an introspective learning process Figure 34 illustrates the basic structure used in nding the feature weights The entire set of available cases is divided randomly into three sets: training, testing set, and evaluation sets In each learning cycle, the newly determined feature weights are used to test the performance of the case base using test cases If a satisfactory threshold is reached, the case base is evaluated against a set of testing cases Figure 3.

Road safety becomes rather problematic in countries which heavily rely on the road networks for the transportation of goods Various research initiatives aim at developing and applying advanced technological systems to increase highway capacity and safety, and to reduce road congestion and air pollution The PATH project is a US collaboration between the California state government, the University of California, and private industry [194] who have been working towards this objective Projects in Europe [195, 196, 197] and Japan [198] have similar long-term research goals Despite the fact that promising results have been achieved in some of these projects, major issues remain to be resolved This might explain the large list of proposed traf c applications with no real-life deployment This section intends to outline the other works and discuss the issues in the area of inter-vehicle and road-vehicle communication 371.

4 shows the architecture for learning case feature weights through a combined introspective and evolutionary learning approach This framework can be used to learn both the global and local weights The use of genetic operators is shown in the box labeled adjust weights For determination of both global and local case feature weights, a chromosome was represented by a vector Sr of n 10-digit binary bit strings Srj ; j 1; 2; ; n, [ie, Sr Sr1 ; Sr2 ; ; Srn ], where n is the number of features used to describe a case Each bit string, Srj , represents the weight wj of a case feature One of the notable things in the algorithm is that the mutation operation on the feature weight vector chromosome Sr was divided into macro- and micromutation operations.

Fig. 6.4 Model to show potential population responses to reduced reproductive capacity as a result of pollution exposure.

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