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The above code uses the C programming language binding to C O RBA/IDL. The client stub types P l a y e r and T e a m have directly evolved from the IDL types. The client includes the C header file " s o c c e r . h h " , which defines the stub classes. The variable declarations in the main program of the example of the client declare pointer variables to client stubs of types T e a m and P l a y e r . O ur example assumes that T e a m and P l a y e r client stubs have been initialized, either bycreating new objects or bylocating these objects. U nlike C O RBA, C O M does not support attributes. Attributes that exist in the C O RBA object model are mapped to operations, rather than instance variables, in all programming language bindings. This is for several reasons. First, attributes may have a structured type and they would then have to be marshalled and unmarshalled in the same way as operation parameters or results. Secondly, attribute accesses or updates could fail for the same reasons as operation calls and these failures should be treated in the same way. Thirdly, treating them as operations gives flexibility to the server object designer as to how the attribute should be implemented. It could be implemented within the server object as an instance variable, but could also be mapped to a value stored in a database. Finally, C O RBA attributes may be declared as r e a d o n l y and if they were not encapsulated this semantics could not be enforced in most programming languages.

with game theory and what it can and cannot do, and those who practice in the game theory field need to become aware of the potential of their profession to organizations concerned with occupational fraud. Brandenburger and Nalebuff put this situation as follows:

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One of the most signi cant short-term effects of pollutant exposure is the reduction in population size due to increased mortality and reduced fecundity Such a population bottleneck causes important changes in the gene pool, which may further increase the population s vulnerability to extinction by demographic and environmental stochasticity These processes in small populations are well known from population genetic models, and have been demonstrated empirically in wild populations Subsequently, the three main concepts in the area inbreeding, genetic drift and the effective population size will be introduced and their relevance to the ecotoxicology of sh populations discussed As a simple hypothetical experiment, a large population may be divided into many subpopulations which are completely isolated from each other (Falconer, 1989) It is assumed that there is no mutation and no selection, and that each subpopulation has the same number of breeders.

It should not be a surprise that companies invest signi cantly in scheduling systems. People and inventory stock are perhaps the most expensive commodities in a company. Getting them to the right place and in the appropriate situation is therefore one of the most important tasks in an organisation. It may seem rather inhumane to talk about people and goods in the same sentence, but the fact of the matter is, that scheduling either of them, in many but not all ways, requires the same technology. In any case, often the actual service to be provided, for example, a maintenance task, requires that both people and goods be simultaneously present and both need to possess the correct qualities for the operation in hand (Table 2.3). Examining Table 2.3 we indeed see that most situations require planning of both people and goods. What is also signi cant is the complexity of making the choice. It is seldom that quantity alone is required. For people there is usually skill requirement; for products, the choice must come from the right box. Scheduling is not just about amount, but also quality.

Considering a locus with two alleles, each subpopulation is a sample of the original population, with the mean allele frequency of all these samples being the allele frequency of the original population (p0) The allele frequencies of the lines are distributed around this mean with the binomial variance for sample means, and the continuous sampling of alleles in each generation causes an irregular change in allele frequencies in each subpopulation (Fig 71), only predictable in amount but not in direction This process is called random genetic drift Due to the random uctuations in allele frequencies, some alleles will be lost from the population just by chance The likelihood of loss depends on both initial allele frequency and effective population size, and is largest for rare alleles in small populations (Fig 72).

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