No abstract

We study D1-brane in AdS 3 × S 3 κ-deformed background with non-trivial dilaton and Ramond-Ramond fields. We consider purely time-dependent and spatiallydependent ansatz where we study the solutions of the equations of motion for D1-brane in given background. We find that the behavior of these solutions crucially depends on the value of the parameter a that was introduced in [7].

We study rigidly rotating and pulsating (m, n) strings in Ad S 3 × S 3 with mixed three form flux. The Ad S 3 ×S 3 background with mixed three form flux is obtained in the near horizon limit of SL(2, Z )-transformed solution, corresponding to the bound state of NS5-branes and fundamental strings. We study the probe (m, n)-string in this background by solving the manifest SL(2, Z )-covariant form of the action. We find the dyonic giant magnon and single spike solutions corresponding to the equations of motion of a probe string in this background and find various relationships among the conserved charges. We further study a class of pulsating (m, n) string in Ad S 3 with mixed three form flux.

Estimating the error in the merged reflection intensities requires a full understanding of all the possible sources of error arising from the measurements. Most diffraction-spot integration methods focus mainly on errors arising from counting statistics for the estimation of uncertainties associated with the reflection intensities. This treatment may be incomplete and partly inadequate. In an attempt to fully understand and identify all the contributions to these errors, three methods are examined for the correction of estimated errors of reflection intensities in electron diffraction data. For a direct comparison, the three methods are applied to a set of organic and inorganic test cases. It is demonstrated that applying the corrections of a specific model that include terms dependent on the original uncertainty and the largest intensity of the symmetry-related reflections improves the overall structure quality of the given data set and improves the final R all factor. This error model is implemented in the data reduction software PETS2.