goals. This person must be able to communicate the vision to all members of the company and act as a leader and mentor for the project. Breaking down the goals and modeling the processes is the responsibility of a business architect or process modeler, but the upper management member acts as a supporter of the modeling efforts, continuously emphasizing its importance to the employees. There are three techniques used in the Business Vision view: Strategy definition. Position of the company with regard to the current and future world, and the strategic goals or necessary changes in the business. Conceptual modeling. Definitions of the important concepts used in the business, along with their relationships to each other. Goal/problem modeling. Definition of the goals of the company, including the breakdown of goals into subgoals, and the definition of the problems that hinder the achievement of goals. The results of these techniques are used in other views as well, since they define the goals of the business, the concepts used when describing the business, and strategies that describe necessary changes in the business.
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Figure 3.18 Structure of a coherent Rake receiver
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Levins, R. (1966) The strategy of model building in population biology. American Scientist 54, 421 31. Levins, R. (1968) Evolution in Changing Environments. Princeton University Press, Princeton, NJ. Levins, R. (1969) Some demographic and genetic consequences of environmental heterogeneity for biological control. Bulletin of the Entomological Society of America 15, 237 40. Levins, R. (1970) Extinction. In: Some Mathematical Problems in Biology (M. Gerstenhaber, ed.), pp. 77 107. Mathematical Society, Providence, RI. Levins, R. and Culver, D. (1971) Regional coexistence of species and competition between rare species. Proceedings of the National Academy of Sciences USA 68, 1246 8. Lewis, E.G. (1942) On the generation and growth of a population. Sankhya 6, 93 6. Lewontin, R.C. and Cohen, D. (1969) On population growth in a randomly varying environment. Proceedings of the National Academy of Sciences USA 62, 1056 60. Lonsdale, W.M. (1999) Global patterns of plant invasions and the concept of invasibility. Ecology 80, 1522 36. Lotka, A.J. (1925) Elements of Physical Biology. Williams and Wilkins, Baltimore, MD. Reprinted 1956, Dover Publications, New York. Lotka, A.J. (1927) Fluctuations in the abundance of species considered mathematically (with comment by V. Volterra). Nature 119, 12 13. Mace, G.M. and Lande, R. (1991) Assessing extinction threats: towards a re-evaluation of IUCN threatened species categories. Conservation Biology 5, 148 57. Magallon, S. and Sanderson, M.J. (2001) Absolute diversi cation rates in angiosperm clades. Evolution 55, 1762 80. Manly, B.J. (1990) Stage-Structured Populations. Sampling, Analysis and Simulation. Chapman and Hall, London. Martinez, N.D., Hawkins, B.A., Dawah, H.A. and Feifarek, B.P. (1999) Effects of sampling effort on characterization of food-web structure. Ecology 80, 1044 55. May, R.M. (1972) Will a large complex system be stable Nature 238, 413 14. May, R.M. (1973a) Stability and Complexity in Model Ecosystems. Princeton University Press, Princeton, NJ. May, R.M. (1973b) On relationships among various types of population model. American Naturalist 107, 46 57. May, R.M. (1976) Simple mathematical models with very complicated dynamics. Nature 261, 459 67. May, R.M. (1978) Host-parasitoid systems in patchy environments, a phenomenological model. Journal of Animal Ecology 47, 833 43. May, R.M. (1981) Theoretical Ecology. Principles and Applications. Blackwell Science, Oxford. May, R.M. (1984) An overview: real and apparent patterns in community structure. In: Ecological Communities: Conceptual Issues and the Evidence (D.R. Strong, D. Simberloff, C.G. Abele and A.B. Thistle, eds), pp. 3 18. Princeton University Press, Princeton, NJ. May, R.M and Oster, G.F. (1976) Bifurcations and dynamic complexity in simple ecological models. American Naturalist 110, 573 99. May, R.M. and Watts, C.H. (1992) The dynamics of predator-prey and resource-harvester systems. In: Natural Enemies (M.J. Crawley, ed.), pp. 431 57. Blackwell Science, Oxford. May, R.M., Conway, G.R., Hassell, M.P. and Southwood, T.R.E. (1974) Time delays, density dependence and single-species oscillations. Journal of Animal Ecology 43, 747 70. Maynard Smith, J. (1968) Mathematical Ideas in Biology. Cambridge University Press, Cambridge.
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Figure 3.5. PECAN architecture.
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The performance management operations process model has strong dependencies on, and is integrated with, the operations process models for fault, con guration, and security, as captured via the input and output triggers. Performance violations are inputs to an integrated root cause analysis engine, implemented within the fault management process (as discussed in 7 while discussing fault diagnosis). A policy management component ties together the FCAPS functions by seamlessly integrating these functions via policies. A critical new function for QoS assurance is included that is responsible for providing QoS assurances to applications with differing priorities using the MANET. NETWORK MONITORING
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Although some individuals with Williams syndrome do have average intelligence, most individuals have mild to moderate mental retardation (Howlin, Davies, & Udwin, 1998; Mervis & Morris, 2007; Tassabehji et al., 1999). There is a general pattern of verbal abilities stronger than visual-spatial abilities (Bellugi, Lichtenberger, Mills, Galaburda, & Korenberg, 1999; Braden & Obrzut, 2002; Mervis et al., 2000). Frequently impaired domains include visual-spatial construction (praxis), motor skills, abstract relational vocabulary, and math skills. In contrast, verbal short-term memory, language ability, and concrete vocabulary as well as reading skills tend to be relative strengths (Gothelf et al., 2005; Mervis & Morris, 2007). As such, it has been suggested that with Williams syndrome, any composite intelligence score is misleading, given the typical pattern of strengths and weaknesses associated with the syndrome (Stinton, Farran, & Courbois, 2008). Within language areas, children with Williams syndrome frequently evidence strengths in language areas, particularly concrete vocabulary; abstract relational vocabulary is more likely to be a relative weakness (Mervis & Morris, 2007; Mervis, Robinson, Rowe, Becerra, & Klein-Tasman, 2004). Comprehension problems emerge early in life despite relatively even development of receptive and expressive language (Mervis & Klein-Tasman, 2000). De cits in language areas, include word- nding dif culty and pragmatic de cits similar to those with autism spectrum disorder (Bellugi et al., 2000, 2007; Philofsky, Fidler, & Hepburn, 2007; Udwin & Yule, 1991). Similarly, Williams syndrome is associated with a disconnect between language and face processing (Bellugi et al., 1999). Individuals with Williams syndrome have dif culty with mental rotation tasks (Farran, Jarrold, & Gathercole, 2001; Vicari, Bellucci, & Carlesimo, 2006). These dif culties are not ameliorated when salience of the stimulus is increased (Stinton et al., 2008) but may relate to their dif culties in visual-spatial areas. In early childhood, the presence of hypotonicity can contribute to delayed motor milestones; however, over time, the child may become hypertonic in lower limbs with increased deep tendon re exes and tightening of the Achilles tendon. Both hypotonicity and hypertonicity affect motor functions. As a result, motor performance and dexterity are delayed (S.-W. Tsai, Wu, Liou, & Shu, 2008).
3.3 BEST PRACTICES FOR CREATING PEOPLE INFRASTRUCTURE Forming a people infrastructure requires identifying groups within the organization, ensuring that each group has supporting technologies, training the groups, and then making sure that each group follows a group work ow by applying ADP s best practices. The supporting technology within the infrastructure thus facilitates applying these best practices consistently, thoroughly, and ef ciently within the group and the organization. As described in the previous chapter, the ADP should be introduced into the organization one group at a time. Thus, before practicing our methodology, it is necessary to identify how the organization can be organized into groups that are logical for defect prevention, identify a pilot group to start practicing the methodology, and implement a supporting technology infrastructure for that pilot group. Having these components in place ensures that the pilot group members adopt a work ow that integrates our methodology into the full software life cycle. Once the pilot group is successfully practicing ADP, work can begin on building an infrastructure and training schedule for the second group. This process is repeated until all groups are fully practicing it.
As indicated earlier, the simplest formal quantitative model of uncertainty for project duration analysis is the basic PERT (Program Evaluation and Review Technique) model; the most complex the authors are aware of in a source response impact dimension is the basic SCERT model (Chapman, 1979). An earlier publication (Chapman et al., 1985b) addresses making choices along the PERT SCERT axis, and subsequent publications have discussed these choices in more detail (e.g., Chapman, 1990). Other modelling complexity dimensions include systems dynamics models to capture feedback and feedforward loops (Forrester, 1961; Richardson and Pugh, 1981; Senge, 1990), cognitive mapping to capture other interdependencies in a qualitative manner (Eden, 1988), and more general soft methods (Rosenhead, 1989; Checkland and Scholes, 1990), as mentioned in 8. Such modelling complexity dimensions are worth exploring by the reader with a view to more effective modelling of uncertainty, and further approaches may prove worthy of development. The more modelling choices become available the more dif cult it is to make the most appropriate choices, unless we clearly understand what each model feature costs and what bene ts it is likely to yield. Only some very general guidelines can be offered here in terms of where to start with basic model development:
As little communication overhead as possible should be incurred between a client application and a service to determine how the client will demultiplex and process completion events after asynchronous operations finish executing. Minimizing communication overhead is important for client applications that are latency-constrained and those that interact with services over bandwidth-limited communication links.
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