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Whereas the loss of resources will affect these behaviours, the latter are more dif cult to relate to xenobiotics. Pollution due to organic enrichment, and thus the lowering of dissolved oxygen, may affect these behaviour patterns (Elliott & Hemingway, 2002). The migratory behaviour of sh occurs on a seasonal to daily basis (Moyle & Cech, 1988). The use of migration to separate life history stages is characteristic not only of diadromous shes such as salmon and eels but also of pelagic shes such as herring and sardines, of many freshwater shes, and of many marine benthic shes (McDowall, 1988).

We can create JSP pages to communicate with enterprise beans in two ways, by implementing either a JavaBean or a custom JSP tag. Either way entails writing a Java class and coding methods that perform the EJB client tasks. Coding a JavaBean results in the JSP pages using the bean containing <jsp:usebean tags and references to the bean methods in the JSP pages in JSP scriptlets or expressions. Coding a custom tag means that the JSP pages will contain references to the bean methods as JSP tags and, possibly, as code in JSP scriptlets or expressions. The end result is the same whether you use JavaBeans or custom tags. However, as mentioned in 7, JSP Tag Extensions, using custom tags gives your JSP pages more of a natural look than using JavaBeans. The natural look comes from having the page's functionality encapsulated in code invoked by tags as opposed to having the page's functionality encapsulated in bean method invocations. The example JSP page shown in this chapter uses a custom tag to locate the EJB home object.

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Blythe, J. (2006), Principles and Practice of Marketing, Thomson, London. Brassington, F. and Pettitt, S. (2005), Essentials of Marketing, Prentice Hall, Harlow. Hines, T. and Bruce, M. (2001), Fashion Marketing: Contemporary Issues, Butterworth-Heinemann, Oxford. Mintel Reports: Value Clothing Retailing (May 2005); Clothing Retailing (July 2005); Keynote Reports: Clothing Manufacturing (May 2006); Clothing and Footwear Industry (March 2006). Oldroyd, M. (2003/2004), CIM Coursebook Marketing Environment, Butterworth-Heinemann, Oxford.

Sankar K. Pal is a Professor and Distinguished Scientist at the Indian Statistical Institute, Calcutta, and the Founding Head of the Machine Intelligence Unit. He received the M. Tech. and Ph.D. degrees in radio physics and electronics in 1974 and 1979, respectively, from the University of Calcutta. In 1982 he received another Ph.D., in electrical engineering, along with a DIC from Imperial College, University of London. During 1986 1987 he worked at the University of California, Berkeley and the University of Maryland, College Park, as a Fulbright Post-doctoral Visiting Fellow; and during 1990 1992 and in 1994 at the NASA Johnson Space Center, Houston, Texas as an NRC Guest Investigator. He has been a Distinguished Visitor of the IEEE Computer Society (United States) for the Asia-Paci c Region since 1997, and held several visiting positions in Hong Kong and Australian universities during 1999 2003. Professor Pal is a Fellow of the IEEE and the International Association for Pattern Recognition in the United States, and all the Third World Academy of Sciences in Italy, four National Academies for Science/Engineering in India. His research interests include pattern recognition and machine learning, image processing, data mining, soft computing, neural nets, genetic algorithms, fuzzy sets and rough sets, Web intelligence, and bioinformatics. He is a coauthor of 10 books and about 300 research publications. He has received the 1990 S. S. Bhatnagar Prize (the most coveted award for a scientist in India) and many other prestigious awards

(7.30) for s < 1 > 0, 2 > 0, > 0, > 0, 0 p34 1. The model can accommodate any transition time distribution, discrete or continuous. In particular, if the 4 5 transition took on values 18 and 19 with probabilities 2/3 and 1/3, the term e18s in (7.30) would be replaced by (2/3)e18s + (1/3)e19s . The maple program code used to generate the MGF (7.30), as well as the CGF and its rst two derivatives for saddlepoint computations, is given in Figure 7.17. Our parameter vector is = (p34 , , , 1, 2 ). To compute the Bayes predictive density of (5.7), we assign a Unif(0, 1) prior for p34 and we assume diffuse Unif(0, 20) priors on all of the other parameters. The likelihood is separable in p, ( , ), and ( 1 , 2 ). This results in a Beta(15, 7) posterior distribution on p34 , and

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