Plant diversity and community structure of Brazilian <Emphasis Type="Italic">Páramos</Emphasis>

In Eastern South America, high altitude grasslands represent a mountain system that has a high number of endemic species. However, studies on the ecology of plant communities in these environments remain scarce. We aimed to evaluate the patterns of biodiversity and structure of plant communities from rocky outcrops in high altitude grasslands of three areas at the Caparaó National Park, southeastern Brazil, by sampling 300 randomly distributed plots. Then, we compared the floristic composition, relative abundance, and biological and vegetation spectra among areas. We classified species as endemic and non-endemic and verified the occurrence of endangered species. Species richness was evaluated by rarefaction analysis on the sampling units. The importance value and species abundance distribution (SAD) models were assessed. We also performed an indicator species analysis. We sampled 58 species belonging to 49 genera and 32 families. The number of species decreased with increasing altitude, with significant differences being observed among areas regarding richness, abundance, and cover. Of the total number of species, 10 are endemic to the Caparaó National Park and 17 are listed on the Brazilian Red List of endangered species. The dominant families on all peaks were Asteraceae and Poaceae. The SAD models showed lognormal and geometric distributions, corroborating the fact that 10 species that were common to all three areas were also the most dominant ones in the communities and showed the highest importance values, which ranged between 35% and 60%. Indicator species analysis revealed that 28 species (48.27%) were indicators. Of these, 42.85% had maximum specificity, meaning that they occurred only in one area. Thus, the number of species per life form ratio was similar among areas, yet vegetation spectra differed, especially for hemicryptophytes. The altimetric difference among the areas showed to be a very important driver in the community assembly, influencing the evaluated variables, however, other drivers as soil depth, slope and water could also influence the community structure on a smaller and local spatial scale.     

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.     

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.     

Modeling X-Ray Photoionized Plasmas Produced at the Sandia Z-Facility

In experiments at the high-power Z-facility at Sandia National Laboratory in Albuquerque, New Mexico, we have been able to produce a low density photoionized laboratory plasma of Fe mixed with NaF. The conditions in the experiment allow a meaningful comparison with X-ray emission from astrophysical sources. The charge state distributions of Fe, Na and F are determined in this plasma using high resolution X-ray spectroscopy. Independent measurements of the density and radiation flux indicate unprecedented values for the ionization parameter ξ = 20–25 erg cm s⁻¹ under nearly steady-state conditions. First comparisons of the measured charge state distributions with X-ray photoionization models show reasonable agreement, although many questions remain.     

The expansive nondecelerative universe

(a) Hubble's discovery of the expansion of the Universe makes it possible to choose unambiguously from the models described by Friedmann's equations of universe dynamics. (b) From the present temperature of the cosmic microwave background radiation, the specific entropy in the matter era and the model properties of the expansive nondecelerative universe, we can determine the present parameters of our Universe with deviations smaller than 2.2%.     

Testing the consistency of the Starburst scenario for active galactic nuclei

In the frame of the Starburst Model, we show that the evolution of a massive stellar cluster in a high metallicity environment can reproduce the observed emission-line spectrum and the UV-optical Spectral Energy Distribution of the Seyfert 2 galaxies and LINERs. We apply the results of our models to three objects: NGC 5506, NGC 5643 and Mk 348.