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Below is a step-by-step method for selecting the market instruments that minimize the risk of the portfolio. The selection is referred to as the optimal hedge. This method is used repeatedly throughout the remainder of the chapter. Step 1. Develop a set of simplifying assumptions. As noted above, the expression Var(R) is complicated. This expression becomes more dif cult to analyze with the addition of market instruments in the portfolio. Simplifying the expression is necessary for analysis of results.

The simplest search method is, of course, an exhaustive search of the space of possible graphs. That is, all possible candidate graphs are inspected in turn and evaluated. The graph with the best assessment is selected as the search result. As an illustration recall the examples of the preceding section, in which all eight possible candidate graphs were evaluated.

If you are running Windows 9x, you can assign these environment variables in your autoexec.bat file as follows:

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The following sections present recent advances in high-performance optical switching, examples of applications, and possible future evolutions that can impact networks for Grids Optical switches have existed and been used for many years, but they were limited to small sizes (eg, 1 2) Several important technologies received significant research attention and range from nanoseconds to milliseconds in switching speed (Figure 151) [22] However, it is only recently, with the advent of 3D Micro-ElectroMechanical Systems (3D-MEMS), that large optical switches have been able to be built with hundreds of ports and low insertion loss (Figure 152) This technology breakthrough relies on switching in free space in three dimensions (3D) instead of two dimensions (2D) and the ability to precisely align and steer beams of light [23] Large optical switches have been demonstrated with typical insertion loss as low as 13 dB [24].

The loss distribution for a 347 347 3D-MEMS switch [25] is shown in Figure 153b, where the median insertion loss of the switch is 14 dB Optical loss is minimized by control of lens array focal length uniformity, collimator optical beam pointing accuracy, and MEMS micro-mirror reflectivity The optical switches are transparent throughout the transmission spectrum of single-mode fibers, having negligible wavelength-dependent losses as well as very low chromatic and polarization mode dispersions [25] The negligible wavelength-dependent loss is achieved by designing with sufficiently large mirrors to minimize diffraction loss across the whole wavelength band, and with the appropriate design of broadband antireflection coatings on all optical surfaces The optical switch produces negligible distortion of high-bandwidth optical signals, as demonstrated by optical loop experiments.

Our other assumption, that all of the patents contribute equally to the portfolio, led us to assign the same value to each patent active for a given number of months during that particular year This need not be so Patents may have different values One would not, for example, assign the same value to a broad patent on a basic technology as one would assign to a narrow patent on a relatively minor improvement in the technology The litmus test in determining the relative values of the patents in a portfolio is to ask the following question: How much of the monopoly will be lost if this particular patent is removed from the portfolio (sold, expired, or invalidated) The broader the scope of the patent claims, the broader the monopoly secured by the patent and, therefore, the larger the relative value of this patent to the portfolio.

In these experiments with a large switch 320 320 , Kaman demonstrated sending 10-Gbps signals through 60 nodes with less than 1 dB of degradation [26] In the other experiment, Kaman demonstrated sending 320 Gbps of data consisting of 32 wavelengths of 10-Gbps data, through 10 nodes over a total of 400 km of fiber without electrical regeneration [27]..

In this chapter we assume that data are complete and allow for the possibility of right censoring Example 51: Bayes Predictive Density for a Hydraulic Pump System Consider the hydraulic pump series system of Example 22 In Example 32 we derived the density of time to total failure of both pumps using exact inversion of the owgraph MGF Recall that each pump fails according to an Exp( ) distribution and that the waiting time for the rst pump to fail is the minimum of two exponentials, Exp(2 ), and the waiting time for the second pump to fail is Exp( ) Let Y0 be the waiting time distribution for the 0 1 transition and Y1 be the waiting time distribution for the 1 2 transition.

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