Effect of ternary solute interaction on interfacial segregation and grain boundary embrittlement

Effect of ternary solute interaction on interfacial segregation and grain boundary embrittlement in an M–I–J system is modeled on the basis of combined Guttmann and Rice–Wang approaches. It is clearly shown that repulsive I–J interaction strengthens interfacial segregation of the impurity I, suppresses segregation of the solute J, and substantially enhances intergranular embrittlement. Attractive interaction exhibits an opposite effect. Generally, the effect of the ternary interaction is weaker than that of the binary one. Although there are only rare experimental data in this respect, their comparison to model calculations shows a very good agreement.     

Influence of addition of nanosize barium cerate on the microstructure and properties of top-seeded melt growth YBCO bulk superconductors

Barium cerate (BaCeO₃) is one of the possible additions to bulk YBa₂Cu₃O₇ single-grain superconductors to suppress the growth of Y₂BaCuO₅ (Y211) particles. This paper investigates the synthesis of barium cerate powder and its use in YBa₂Cu₃O₇ bulk superconductors. Crystalline barium cerate was synthesized by solid-state reaction, by co-precipitation of oxalates and by sol-gel method. Final calcination was held in air or in vacuum. It is shown that the most efficient in refining Y211 is nanocrystalline barium cerate prepared by sol-gel method calcined in vacuum. The effective refinement of Y211 particles occurred over the entire interval of nanocrystalline BaCeO₃ addition from 0.38 to 1.90 wt%. The optimal concentration of nanosize barium cerate was determined, microstructure and superconducting properties were characterized. The effect of Y211 content on trapped field in YBCO bulks with addition of nanocrystalline barium cerate is shown.     

Simplified scoring methods for HMM-based speech recognition

Most of the contemporary speech recognition systems exploit complex algorithms based on Hidden Markov Models (HMMs) to achieve high accuracy. However, in some cases rich computational resources are not available, and even isolated words recognition becomes challenging task. In this paper, we present two ways to simplify scoring in HMM-based speech recognition in order to reduce its computational complexity. We focus on core HMM procedure—forward algorithm, which is used to find the probability of generating observation sequence by given HMM, applying methods of dynamic programming. All proposed approaches were tested on Russian words recognition and the results were compared with those demonstrated by conventional forward algorithm.