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As can be seen, what we obtain is the fuzzy set of values y0 that are FT-similar to y, truncated by the global degree FS St; St0 of the similarity between St and St0. Since it applies to all pairs St; y 2 M, we obtain the following fuzzy set B of possible values y0 for y0 B y0 Py0 y0 : B y0 max St;y 2M min FS St; St0 ; FT y; y0 3:14



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Absolute frequency of the attribute value ci , that is, Ni. = t {s R|s(C)=ci } wR (t). Absolute frequency of the attribute value aj , that is, N.j = t {s R|s(A)=aj } wR (t). Absolute frequency of the combination of the values ci and aj , that is, Nij = t {s R|s(C)=ci s(A)=aj } wR (t).

[object, dual, uuid(75DA6450-DD0E-00d1-8B59-0089C73915CB] interface DIPlayer: IDispatch { [id(1),propget] HRESULT Name([out] BSTR val); [id(2),propget] HRESULT Number([out] short val); [id(3)] HRESULT book([in] Date val) };





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The political fallout from the crisis created a nger pointing environment in which these tactics were natural targets. On discovering documents that alluded to the above tactics, the Governor of California, Governor Davis, spoke of a smoking gun that pointed to Enron and other energy companies engaging in similar activities as the cause of the crisis. A reexamination of the crisis indicates that the tactics did not cause the energy crisis. The resulting pro ts from these tactics were not even close to the energy costs resulting from the crisis that Californians paid and are projected to pay for several years into the future. These tactics were symptoms of a dysfunctional system. The system was dysfunctional on two levels. First, the utilities were far from equilibrium, placing California consumers at the mercy of the energy merchants. Energy merchants took advantage of their position to attain handsome pro ts within the legal framework of the market that was established. Second, the market design did a poor job of accounting for operational requirements of delivering power. The above tactics fully exploited the operational cracks.

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If a trademark ceases to serve primarily as an identification of the source of goods and instead comes to identify the goods themselves (i.e., if it becomes the generic term for such goods), the rights to exclusive use of the mark are lost. Notable examples of such lost marks are escalator, thermos, and aspirin. Proper trademark usage is directed to the prevention of such loss. Prior to release, all publications should be reviewed for

In this section, we will see how persistence in files is supported by the different forms of object-oriented middleware. We first review the CORBA Externalization service, which supports writing CORBA objects, possibly composite, onto a file. We then review the serialization interfaces of Java, which can be used to achieve persistence of stateful Java remote objects. Finally, we discuss structured storage in COM. As files do not have a schema, the discussion of this subsection is concerned with how implementations of storage homes and storage object incarnations can be achieved using files. We note that there are two different principles that can be used to store objects onto a file. The first approach is to flatten objects into a sequential form and then store that sequential representation onto a file. This approach is employed when using the C O RBA Externalization service and the Java serialization architecture. The second approach is to retain the structure of objects in the file so as to be able to update portions of the file incrementally. This approach is taken in C O M s structured storage.

The term electrically small antennas applies to antennas whose dimensions are small compared to the wavelength. As will be shown, these are mostly dipoles or loops, or minor modi cations of these. Various techniques for improving performance are discussed below. Both short dipoles and small loops are superdirective; the directivity remains at 1.5 as the dimensions decrease, but the radiation resistance decreases and the reactance increases. Thus the bandwidth is narrow, the ef ciency may be low, and the necessary tolerances will be small. See 2 for more discussion of superdirectivity.

To illustrate the effectiveness of using a neural network for case selection and retrieval, we describe here some results on a glass identi cation problem [69]. The glass database available in UCL (University College London, London) Machine Learning Repository [70] consists of 214 records, and each record has 11 attributes: 1. Id number: 1 to 214 2. RI: refractive index 3. Na: sodium (unit measurement is weight percent in corresponding oxide; the same unit measure is applicable to attributes 4 to 10) 4. Mg: magnesium 5. Al: aluminum 6. Si: silicon 7. K: potassium 8. Ca: calcium 9. Ba: barium 10. Fe: iron 11. Type of glass: window glass/non-window glass

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