Dynamic modelling of retrogressive landslides with emphasis on the role of clay sensitivity

This paper presents a detailed numerical study of the retrogressive failure of landslides in sensitive clays. The dynamic modelling of the landslides is carried out using a novel continuum approach, the particle finite element method, complemented with an elastoviscoplastic constitutive model. The multiwedge failure mode in the collapse is captured successfully, and the multiple retrogressive failures that have been widely observed in landslides in sensitive clays are reproduced with the failure mechanism, the kinematics, and the deposition being discussed in detail. Special attention has been paid to the role of the clay sensitivity on each retrogressive failure as well as on the final retrogression distance and the final run‐out distance via parametric studies. Moreover, the effects of the viscosity of sensitive clays on the failure are also investigated for different clay sensitivities.     

On the Nonlinear Thermal Evolution of Solar Corona Structures

The thermal evolution of structures is investigated for different values of the size of structure. A simplified cooling function and a constant heating mechanism are assumed. Analytical criteria for thermal instability are obtained. It is found that the response of the thermal structure not only depends on the amplitude of the disturbances, but also on whether the disturbance increases or decreases the initial steady temperature. Additionally, the evolution of the structure is examined numerically by using a time-dependent code under the assumption that the inertia terms are small. In particular, the analytical criteria obtained for thermal instability are verified.     

Is dark matter created in the gravitation field of galaxies?

Using the heuristic arguments of quantum physics we describe a new mechanism of the creation of short-living particles from the virtual ones in a stationary gravitation field. The mass of these particles is a function of the intensity of gravitation field. We suppose that the particles created in the gravitation field form a part of the non-baryonic dark matter. Having the intensity of gravitation field in a galaxy we can calculate the density of dark matter created in it by the vacuum quantum fluctuation. We calculate the distribution of this dark matter in a model galaxy and show that its total mass is comparable with the visible mass of the galaxy.     

Alignment between luminous red galaxies and surrounding structures at z∼ 0.5

We analyse a high-redshift sample (0.4 < z < 0.5) of luminous red galaxies (LRGs) extracted from the Sloan Digital Sky Survey data release 4 and their surrounding structures to explore the presence of alignment effects of these bright galaxies with neighbour objects. In order to avoid projection effects, we compute photometric redshifts for galaxies within 3  h ⁻¹ Mpc in projection of LRGs and calculate the relative angle between the LRG major axis and the direction to neighbours within 1000 km s⁻¹. We find a clear signal of alignment between LRG orientations and the distribution of galaxies within 1.5  h ⁻¹ Mpc. The alignment effects are present only for the red population of tracers; LRG orientation is uncorrelated to the blue population of neighbour galaxies. These results add evidence to the alignment effects between primaries and satellites detected at low redshifts. We conclude that such alignments were already present at z ∼ 0.5.     

Interactions and the Evolution of Cluster Galaxies

We have developed model predictions for the morphological distribution of cluster galaxies as a function of the cluster-centric distance and the local galaxy density, using a semi-analytical code. This code allows us to obtain magnitudes and colours for cluster galaxies at different redshifts, and thus to study in detail the evolution of the colour–magnitude relation of specific distant clusters. This revised version was published online in July 2006 with corrections to the Cover Date.     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.