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The discrete probability approach used in this book is useful at a conceptual level, even if it is replaced by a Monte Carlo simulation approach for operational purposes Mean variance approaches are also conceptually useful, but need great care when used in practice, especially if they tempt people to assume independence that does not exist..

Manipulating numbers in oating point arithmetic, such as adding the squares of a large and a small number, may propagate or accumulate errors, which may in turn produce answers that are wildly different from the truth. If two nearly equal numbers are subtracted, cancellation may occur, leaving only the accumulated rounding error as a result, perhaps to be further multiplied and propagated in other calculations (Higham 2002, p. 9). 2.4.2.1 Floating Point Arithmetic Example Consider the following calculation: i = 1000000000 + 2 0.1 1000000000. (We assume here that operations are executed from left to right, as appears, but see below concerning the reproducibility of oating point calculations for further complications.) How does this oating point operation work using (singleprecision) oating point arithmetic 9 First, the operand 100000000 must be represented in oating point. We do this by taking the binary expansion (100000000)10 = (111011100110101100101000000000)2 , normalizing it, (1.11011100110101100101000000000)2 229 , and truncating to 23 points following the decimal: (1.11011100110101100101000)2 229 .

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In Gianni s learning trajectory, the access to the knowledge diffused in the community of practice was mediated by the form of the linguistic and relational modalities that we have called knowledge pointers because they called the novice s attention to the knowledge items that the community deemed important to ensure safe working practices Ordinary language was used to transmit the speci c content of how the work was or was not to be done That content was reinforced by the transmission of a practical ability which consisted in the capacity to look and see, listen and understand In becoming a practitioner, Gianni developed the ability to grasp the knowledge pointers contained in the instructions he received and which gave him access to socially organized ways of seeing and understanding.

3.10.2 Shorter Delay Times and Hybrid ARQ (HARQ)

Fig. 3.7 Constant f curves on Smith chart. These are theoretical noise factors f rather than standard noise factors f .

How It Works The adaptive business network is a group of independent organizations linked by standard business processes and common technology in order to capitalize on the strengths of each organization to be more competitive than they could on their own. This business network focuses on the needs of the customer using a set of aligned measurements. It enables participants to adapt to a constantly changing business climate and thereby maximize profits. Participants in an adaptive business network work together on everything from monitoring inventory to coordinating production schedules to sharing information about customer purchases. Each network has a lead company or coordinating partner that is responsible for the relationship with the customer and that coordinates the establishment of the rules that govern how the network operates. In addition, each network will have dozens to hundreds of participating companies linked to it. Some of these participating companies may provide the parts that are combined into the final product. Others may provide services such as the transportation required to deliver the goods to the customer, the technology that links participants together, or financial services for the whole group, including the customer. Within the adaptive business network, information is communicated instantaneously and as frequently as required to make good decisions. The network can be set up to receive sales information every week, hour, minute, or even instantaneously. And the information received can be

Many problems that recur when modeling business systems have been solved before. So why solve them all over again Patterns make it possible to capture and describe these business-modeling problems and their corresponding solutions so that the solutions can be reused. Solutions, in the context discussed here, are not program code. They comprise a realization that is used to structure or model the business. A solution cannot be directly translated into code. A pattern is a generalized solution that can be implemented and applied in a problem situation (a context), and thereby eliminate one or more of the inherent problems in order to satisfy one or more objectives. Patterns can be considered prototypes for production. The architect Christopher Alexander defines the term pattern in his book, The Timeless [1] Way of Building [Alexander 79] as follows: Each pattern is a three-part rule, which expresses a relation between a certain context, a problem, and a solution. As an element in the world, each pattern is a relationship between a certain context, a certain system of forces, which occurs repeatedly in that context, and a certain spatial configuration, which allows these to resolve themselves. As an element of language, a pattern is an instruction, which shows how this spatial configuration can be used, over and over again, to resolve the given system of forces, wherever the context makes it relevant.

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