MT Ser, a binary blue subdwarf

We have classified and determined the parameters of the evolved close binary MT Ser. Our moderate-resolution spectra covering various phases of the orbital period were taken with the 6-m telescope of the Special Astrophysical Observatory. The spectra of MT Ser freed from the contribution of the surrounding nebula Abell 41 contained no emission lines due to the reflection effect. The radial velocities measured from lines of different elements showed them to be constant on a time scale corresponding to the orbital period. At the same time, we find effects of broadening for the HeII absorption lines, due to the orbital motion of two hot stars of similar types. As a result, we classify MT Ser as a system with two blue subdwarfs after the common-envelope stage. We estimate the component masses and the distance to the object from the Doppler broadening of the HeII lines. We demonstrate that the age of the ambient nebula, Abell 41, is about 35 000 years.     

Rotational and magnetic corrections to the g-mode frequencies in a one-dimensional MHD model

The central magnetic field and rotation of the solar radiative zone are responsible for corrections to the g-mode frequencies. Magnetogravitational spectra are calculated analytically in a simple one-dimensional MHD model that goes beyond the WKB approximation and avoid any cusp resonances that trap the wave within the radiative zone in the presence of a weak magnetic background. The calculations are compared with spacecraft observations of the 1% frequency shifts for candidate g-modes found in the SOHO GOLF experiment. The magnetic correction is the main contribution for a strong magnetic field satisfying the approximation used. It is shown that a constant magnetic field of 700 kG in the radiative zone provides the required frequency shift for the n = ?10 g-mode. The rotational correction, which is due to the Coriolis force in the one-dimensional model used, is much less than a percent (α_Ω ≤ 0.003).     

Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.