Microwave Type III Burst Group in the 1993 October 2 Flare

Microwave Type III bursts with positive frequency drifting rate were found by Stahli and Benz (1987) for first time. Type III events are especially interesting because they are well-known to be signatures of electron beams in coronal plasma, and they are effective means for diagnosting of source plasma. A microwave burst consisting of some microwave type III burst groups was registered at Beijing Astronomical Observatory with the 2545–2645–2840 MHz synchronous observing system. The distributions of frequency drifting rate, half power duration, and intensity for each impulse in the groups have been statistically analysed. From this analysis, some important parameters for the dynamic process in the flare are deduced and discussed.     

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.     

A New Solar Radio Spectrometer at 1.10-2.06 GHz and First Observational Results

An improved Solar Radio Spectrometer working at 1.10-2.06 GHz with much improved spectral and temporal resolution, has been accomplished by the National Astronomical Observatories and Hebei Semiconductor Research Institute, based on an old spectrometer at 1-2 GHz. The new spectrometer has a spectral resolution of 4 MHz and a temporal resolution of 5ms, with an instantaneous detectable range from 0.02 to 10 times of the quiet Sun flux. It can measure both left and right circular polarization with an accuracy of 10% in degree of polarization. Some results of preliminary observations that could not be recorded by the old spectrometer at 1-2 GHz are presented.     

Generalized strain probing of constitutive models

Advanced material constitutive models are used to describe complex soil behaviour. These models are often used in the solution of boundary value problems under general loading conditions. Users and developers of constitutive models need to methodically investigate the represented soil response under a wide range of loading conditions. This paper presents a systematic procedure for probing constitutive models. A general incremental strain probe, 6D hyperspherical strain probe (HSP), is introduced to examine rate‐independent model response under all possible strain loading conditions. Two special cases of HSP, the true triaxial strain probe (TTSP) and the plane‐strain strain probe (PSSP), are used to generate 3‐D objects that represent model stress response to probing. The TTSP, PSSP and general HSP procedures are demonstrated using elasto‐plastic models. The objects resulting from the probing procedure readily highlight important model characteristics including anisotropy, yielding, hardening, softening and failure. The PSSP procedure is applied to a Neural Network (NN) based constitutive model. It shows that this probing is especially useful in understanding NN constitutive models, which do not contain explicit functions for yield surface, hardening, or anisotropy. Copyright © 2004 John Wiley & Sons, Ltd.     

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.     

Evidence of climatic warming in the southern Urals region derived from borehole temperatures and meteorological data

Thirty borehole temperature–depth profiles in the central and southern Urals, Russia were scrutinized for evidence of ground surface temperature histories. We explored two inversion schemes: a simple ramp inversion in which solutions are parameterized in terms of an onset time and magnitude of change and a more sophisticated functional space inverse algorithm in which the functional form of the solution is left unspecified. To enhance and potentially identify latitudinal differences in the ground surface temperature signal, we subdivided the data into three groups based on geographic proximity and simultaneously inverted the borehole temperature–depth logs. The simultaneous inversions highlighted 13 temperature–depth logs that could not both fit a common ground surface temperature history and a priori models within reasonable bounds. Our results confirm that this is an effective way to reduce site-specific noise from an ensemble of boreholes. Each inversion scheme gives comparable results indicating locally variable warming on the order of 1°C starting between 1800 and 1900 AD. Similarly surface air temperature records from 12 nearby meteorological stations exhibit locally variable warming also on the order of 1°C of warming during the 20th century. To explore the degree to which borehole temperatures and surface air temperature (SAT) time series are responding to the same signal, we average the SAT data into the same three groups and used these averages as a forcing function at the Earth's surface to generate synthetic transient temperature profiles. Root mean square (RMS) misfits between these synthetic temperature profiles and averaged temperature–depth profiles are low, suggesting that first-order curvature in borehole temperatures and variations in SAT records are correlated.     

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.