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VOLUME OUTLINE The third chapter is all about X-ray optics, another eld that has seen an explosive growth in the last decade, in various ways, resulting in new commercial instruments and new application lines. In Subchapter 3.1, M. Yanagihara (Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Japan) and K. Yamashita (Department of Physics, Nagoya University, Japan) discuss advances in multilayer production technology, due to the progress in thin- lm technology and polishing of super-smooth substrates to the sub-nanometer level, and in their performance and applications. For soft X-rays, the latter include focusing, microscopy and polarimetry; for hard X-rays, obtaining microbeams for microscopy, X-ray telescopes and multilayercoated gratings are discussed. In Subchapter 3.2, Y. Hosokawa (X-ray Precision, Inc., Kyoto, Japan) presents the state-of-the-art for single capillaries, which make use of multiple external total re ections. He shows how a very bright and narrow X-ray microbeam can be realised using single capillaries (or X-ray guide tubes), and how this leads to a tabletop X-ray analytical microscope. Several applications are presented. In Subchapter 3.3, N. Gao (X-ray Optical Systems, Albany, NY, USA) and K. Janssens (University of Antwerp, Belgium) give a detailed treatment of the fundamentals of multi- ber polycapillaries and the recent fused and heat-shaped monolithic versions. In the last decade, polycapillary optics have become widespread and successfully used as crucial components in commercial X-ray microanalysis and low-power compact instruments. Novel analytical applications are situated in elemental microanalysis in laboratory scale, portable and synchrotron systems, micro-X-ray absorption nearedge spectroscopy (XANES), EMPA, etc. Future developments in performance and spot size are discussed. Finally, the new compound refractive lenses, rst fabricated in 1996, are presented in Subchapter 3.4 by A. Simionovici (European Synchrotron Research Facility, Grenoble, France) and C. Schroer and B. Lengeler (Aachen University of Technology, Germany). Their theory, design and properties are considered, as well as their use for imaging and microbeam production. Some focus use birt reports barcodes drawer to include barcode with java unicode KeepDynamic.com/barcodeprint barcodes c# generate, create bar code command none for c#.net projects KeepDynamic.com/ bar codeShortcodes function in much the same way that hooks do. They use the function do_shortcode (), which takes two arguments. The first argument is the name of the shortcode. This name will be inserted into a post, so if the shortcode name is dosomething, a blogger could use [dosomething] in a post. 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REPLACING THE GAUSSIAN BY A VOIGTIAN representing the convolution gives: V (Ei ) = If we choose 6 sufficiently small, this is of a smaller order of magnitude than E . Compare also Kersting (1978). On the other hand (2.35) is true for dp and d g L if F is the uniform distribution on a finite interval, but it fails again for the uniform distribution on the unit cube in three or more dimensions [see Dudley (1969) for details]. It seems that we are in trouble here because of a phenomenon that has been colorfully called the curse of dimensionality ; the higher the dimension, the more empty space there is, and it becomes progressively more difficult to relate the coarse and sparse empirical measure to the true one. Mere Gsteaux differentiability does not suffice to establish asymptotic normality [unless we also have higher order derivatives, cf. von Mises (1937, 1947), who introduced the concept of differentiable statistical functionals, and Filippova (1962)l. The most promising intermediate approach seems to be that of Reeds (1976), which is based on the notion of compact differentiability (Averbukh and Smolyanov 1967, 1968). See also Clarke (1983, 1986). In any case, an approach through differentiable functionals does not seem to be adequate for dealing with non-smooth true underlying distributions and for establishing finer details, such as the non-normal limiting distributions occurring in Section 3.2.2. See also Sections 6.2 and 6.3. You can, of course, mix and match filters too. That is, you can specify multiple filters until you have exactly the search query you re looking for. A(F.S) = 0.4 0.8
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