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Twin classification

There are several systems of twin classification, depending for example on the morphology or on the twin element. Several types of nomenclature may also be encountered. Friedel (1928) has distinguished four kinds of twins. [Pg.109]

Table 1. Characteristic Classification Parameters for Twin-Cone Classifier ... Table 1. Characteristic Classification Parameters for Twin-Cone Classifier ...
Under the classification of Amelinckx, stacking faults give rise to fringes. The stmcture in the region below the fault is derived from that above by a simple translation r parallel to the fault. In contrast, across a twin boundaiy the region below the fault is related by a similar displacement vector r which increases linearly from the boundaiy and the fringes thus produced are known as fringes. [Pg.215]

Y. Takano, Classification of Twins - Twin Groupoids, Tokyo. Kokinshoin, 1973 (in Japanese)... [Pg.149]

Minerals are often riddled with microstructures when observed under the electron microscope. Although the observation and classification of microstructures such as twin boundaries, anti-phase boundaries, exsolution lamellae etc., has been a longstanding activity of mineralogists and crystallographers it has only been very recently that we started to understand the enormous importance of microstructures for the physical and chemical behaviour of minerals. [Pg.65]

To illustrate the above introduced classification, several examples will be presented where the development of twin drugs has been accompanied or guided by crystallographic, biophysical or molecular modeling studies. [Pg.403]

This chapter has argued for the critical role of interfacial defects in solids. We have favored an artificial classification into three primary types of interfacial defects, namely, surfaces, stacking faults and twins, and grain boundaries. In each of these cases, it has been argued that a continuum description of the energetics of such... [Pg.502]

Table 12a. Classification of diffraction patterns for N = 3K+L. For the correspondence between the relative rotations of twinned individuals and the twin laws see Table 11 (after Nespolo 1999). [Pg.225]

Prediabetes. The widespread belief that there is a hereditary basis for diabetes leads to the classification of close kin of diabetics (usually the nondiabetic identical twins of diabetics and the children of two diabetic parents) as prediabetics, even though these people may show no signs of the disease. Although it is not inevitable that those so classified will eventually develop the disease, there is a high probability that this may be the case. Hence, these people should be watched for the early signs of the disease. [Pg.270]

The most frequently used supervised pattern recognition method is the linear discriminant analysis (LDA), not to be confused with its twin brother canonical correlation analysis (CCA) or canonical variate analysis (CVA). Recently, classification and regression trees (CART) produced surprisingly good results. Artificial neural networks (ANNs) can be applied for both prediction and pattern recognition (supervised and unsupervised). [Pg.146]


See other pages where Twin classification is mentioned: [Pg.218]    [Pg.525]    [Pg.226]    [Pg.80]    [Pg.205]    [Pg.403]    [Pg.217]    [Pg.267]    [Pg.267]    [Pg.268]    [Pg.276]    [Pg.212]    [Pg.109]    [Pg.111]    [Pg.113]    [Pg.115]    [Pg.117]    [Pg.403]    [Pg.588]    [Pg.10]    [Pg.418]    [Pg.418]    [Pg.47]    [Pg.26]    [Pg.26]    [Pg.2198]    [Pg.136]    [Pg.145]    [Pg.148]    [Pg.1011]    [Pg.368]    [Pg.182]   
See also in sourсe #XX -- [ Pg.47 ]




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