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itself as a threat, which can lead to degradation of the environment or loss of sustainability (see, for example, Gough et al., 2000). Conversely, risk can also lead to the enhancement of the environment when a risk management process is used to identify opportunities and they are pursued. When reviewing environmental risks and the actions that may be taken to manage them, the threats and opportunities that an activity, service or product may present should be considered. Opportunities and threats are both important parts of risk management, as assessing the opportunities on offer may influence the prioritization and subsequent treatment strategies.

Topologies are logical arrangements and need not be physically cabled as shown in topology diagrams. A ring topology is shown as a logical ring, but physically it is cabled like a star topology. Following are examples of the various architecture-based network topologies:

d,(i) = W, - gn(f).

. . . . . . . . . . .

Many database designers stop normalizing the database at 3NF because it provides the most bang for the buck. It s fairly easy to convert a database to 3NF and that level of normalization prevents the most common data anomalies. It stores separate data separately so you can add and remove pieces of information without destroying unrelated data. It also removes redundant data so the database isn t full of a zillion copies of the same information that waste space and make updating values dif cult. However, the database may still be vulnerable to some less common anomalies that are prevented by the more complete normalizations described in the following sections. These greater levels of normalization are rather technical and confusing. They can also lead to unnecessarily complicated data models that are hard to implement, hard to maintain, and hard to use. In some cases, they can give worse performance than less completely normalized designs. Though you may not always need to use these super-normalized databases, it s still good to understand them and the problems that they prevent. Then you can decide whether those problems are a big enough issue to justify including them in your design. (Besides, they make great ice breakers at parties. Hey everyone! Let s see who can put the guest list in 4NF the fastest! )

(10.66)

The implementation of our adaptive immersed boundary code derives much of its capability from two software libraries, SAMRAI [43, 44] and PETSc [45, 46]. SAMRAI is a general object-oriented software infrastructure for implementing parallel scienti c applications that employ structured adaptive mesh re nement. SAMRAI provides the backbone of our implementation, managing the locally re ned Cartesian grid patch hierarchy as well as the Eulerian data de ned on the hierarchy. It also provides facilities for performing adaptive gridding, load balancing, and parallel data communication. PETSc is also an object-oriented toolkit for constructing parallel scienti c applications, and its design makes it especially well suited for performing unstructured or nonadaptive structured grid computations. We employ PETSc vector objects to store quantities de ned on the distributed curvilinear mesh, as well as PETSc sparse matrix objects to perform computations on the curvilinear mesh. During each timestep, our numerical scheme requires the solution of several systems of linear equations on the locally re ned Cartesian grid. These systems of equations are solved by preconditioned Krylov methods provided by PETSc. A modi ed version of the PETSc SAMRAI interface provided by the SAMRAI library [47] allows this to be done without requiring data copying between SAMRAI and PETSc. Preconditioning is performed by an FAC solver described in Ref. [17].