Effect of size, morphology and crystallinity of seed crystal on the nucleation and growth of single grain Y-Ba-Cu-O

The effect of size, morphology and crystallinity of seed crystals on the nucleation and growth of large grain Y-Ba-Cu-O (YBCO) bulk superconductors fabricated by top seeded melt growth (TSMG) has been investigated. Seeding bulk samples with small, square shaped seed crystals leads to point nucleation and growth of the superconducting YBa2Cu3O7-y (Y-123) phase that exhibits the usual square habitual growth symmetry. The use of triangular and circular shaped seed crystals, however, modifies significantly the growth habit geometry of the grain. The use of large area seeds both increases the rate of epitaxial nucleation of the Y-123 phase and produces relatively large crystals in the incongruent melt, which decreases significantly the processing times of large grain samples. The present study is relevant to decrease processing times of samples with both preferred or no growth sectors and for multiple seeding of large grain samples which contain clean grain boundaries. © 2005 Published by Elsevier Ltd.

Bayesian classification of acoustical waveforms under environmental variability

We present in this paper a new multivariate probabilistic approach to Acoustic Pulse Recognition (APR) for tangible interface applications. This model uses Principle Component Analysis (PCA) in a probabilistic framework to classify tapping pulses with a high degree of variability. It was found that this model, achieves a higher robustness to pulse variability than simpler template matching methods, specifically when allowed to train on data containing high variability. © 2011 IEEE.

A classification of data quality assessment methods

Data quality (DQ) assessment can be significantly enhanced with the use of the right DQ assessment methods, which provide automated solutions to assess DQ. The range of DQ assessment methods is very broad: from data profiling and semantic profiling to data matching and data validation. This paper gives an overview of current methods for DQ assessment and classifies the DQ assessment methods into an existing taxonomy of DQ problems. Specific examples of the placement of each DQ method in the taxonomy are provided and illustrate why the method is relevant to the particular taxonomy position. The gaps in the taxonomy, where no current DQ methods exist, show where new methods are required and can guide future research and DQ tool development.