Pyroelectric and second harmonic responses from LiTaO3 nanocrystallites evolved in a Li2O-B2O3-Ta2O5 glass system

Dendritic growth of trigonal and square bipyramidal structures of LiTaO3 nanocrystallites, of 19-30 nm size, was observed when 1.5Li(2)O-2B(2)O(3)-0.5Ta(2)O(5) glasses were subjected to controlled heat treatment between 530 degrees C and 560 degrees C/3 h. X-ray diffraction and Raman spectral studies carried out on the heat-treated samples confirmed the formation of a LiTaO3 phase along with a minor phase of ferroelectric Li2B4O7. The sample that was heat-treated at 550 degrees C/3 h was found to possess similar to 26 nm sized crystallites which exhibited a pyroelectric coefficient as high as 15 nC cm(-2) K-1 which is in the same range (23 nC cm(-2) K-1) as that of single crystalline LiTaO3 at room temperature. The corresponding figures of merit that were calculated for the fast pulse detector (F-i), the large area pyroelectric detector (F-v) and the pyroelectric point detector (F-D) were 0.517 x 10(-10) m V-1, 0.244 m(2) C-1 and 1.437 x 10(-5) Pa-1/2, respectively. Glass nanocrystal composites comprising similar to 30 nm sized crystallites exhibited broad Maker fringes and the second harmonic intensity emanated from these was 0.5 times that of KDP single crystals.

Mixed norm estimates for the Riesz transforms on the Heisenberg group

In this paper we prove weighted mixed norm estimates for Riesz transforms on the Heisenberg group and Riesz transforms associated to the special Hermite operator. From these results vector-valued inequalities for sequences of Riesz transforms associated to generalised Grushin operators and Laguerre operators are deduced.

MIXED NORM ESTIMATES FOR THE RIESZ TRANSFORMS ON SU (2)

In this paper we prove mixed norm estimates for Riesz transforms on the group SU(2). From these results vector valued inequalities for sequences of Riesz transforms associated to Jacobi differential operators of different types are deduced.

Using network mesures to test evolved NK-landscapes

In this paper we empirically investigate which are the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms. To this end, we evolve instances that maximize the estimation of distribution algorithm complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measures, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K.