Titlebook: High Performance Non-Oxide Ceramics II; Martin Jansen Book 2002 Springer-Verlag Berlin Heidelberg 2002 Keramik.Materials Science.Materialw

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0081-5993 eld of materials research. The scope of the individual contributions is such as to address both graduate students, specializing in ceramic materials, and all scientists in academia or industry dealing with mate978-3-642-07724-1978-3-540-45623-0Series ISSN 0081-5993 Series E-ISSN 1616-8550

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Silicon Nitride Ceramics,and on the quality of the Si.N. starting powders, which closely is related to the chemistry of the production process, and on the other on the liquid phase sintering as the most important step in the densification route. The interrelation between pure Si.N., the densification of the powder including

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tional neural network (CNN) algorithm is one such methodology that has shown to give favorable results on satellite imagery as well. In this research, CNN was used to binary the ROI into built-up and non-built-up classes. The algorithm was trained and tested on 2 multi-resolution satellite datasets

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Book 2002de to cover both the essential and the most recent advances achieved in this particular field of materials research. The scope of the individual contributions is such as to address both graduate students, specializing in ceramic materials, and all scientists in academia or industry dealing with mate

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0081-5993 mistry, each written by the world‘s renowned experts.Still vThe nitrides and carbides of boron and silicon are proving to be an excellent choice when selecting materials for the design of devices that are to be employed under particularly demanding environmental and thermal con- tions. The high degr

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Book 2002 employed under particularly demanding environmental and thermal con- tions. The high degree of cross-linking, due to the preferred coordination numbers of the predominantly covalently bonded constituents equalling or exceeding three, lends these non-oxidic ceramics a high kinetic stability, and is

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G. Petzow,M. Herrmannlevel questions. Two integrated deep learning techniques, namely BI-LSTM with attention and BIGRU-CNN with attention, have been implemented for more useful question classification in QAS, and it is observed that BIGRU-CNN with attention gives good performance with an accuracy of 84.5 than BI-LSTM with attention.