Energy-level statistics of interacting trapped bosons

It is a well-established fact that statistical properties of energy-level spectra are the most efficient tool to characterize nonintegrable quantum systems. The statistical behavior of different systems such as complex atoms, atomic nuclei, two-dimensional Hamiltonians, quantum billiards, and noninteracting many bosons has been studied. The study of statistical properties and spectral fluctuations in interacting many-boson systems has developed interest in this direction. We are especially interested in weakly interacting trapped bosons in the context of Bose-Einstein condensation (BEC) as the energy spectrum shows a transition from a collective nature to a single-particle nature with an increase in the number of levels. However this has received less attention as it is believed that the system may exhibit Poisson-like fluctuations due to the existence of an external harmonic trap. Here we compute numerically the energy levels of the zero-temperature many-boson systems which are weakly interacting through the van der Waals potential and are confined in the three-dimensional harmonic potential. We study the nearest-neighbor spacing distribution and the spectral rigidity by unfolding the spectrum. It is found that an increase in the number of energy levels for repulsive BEC induces a transition from a Wigner-like form displaying level repulsion to the Poisson distribution for P(s). It does not follow the Gaussian orthogonal ensemble prediction. For repulsive interaction, the lower levels are correlated and manifest level-repulsion. For intermediate levels P(s) shows mixed statistics, which clearly signifies the existence of two energy scales: external trap and interatomic interaction, whereas for very high levels the trapping potential dominates, generating a Poisson distribution. Comparison with mean-field results for lower levels are also presented. For attractive BEC near the critical point we observe the Shnirelman-like peak near s = 0, which signifies the presence of a large number of quasidegenerate states.

Thioredoxin interacting protein (TXNIP) rs7212 polymorphism is associated with arterial stiffness in the Brazilian general population

Thioredoxin interacting protein plays a pivotal role in several important processes of cardiovascular homeostasis by functioning as a biological sensor for biomechanical and oxidative stress. However, the effects of genetic variants in the modulation of arterial stiffness are unknown. In this scenario, the present study evaluated the relationship between the TXNIP rs7212 polymorphism and arterial stiffness. In the overall sample and in the diabetic group, individuals carrying CG + GG genotypes had higher PWV values compared with CC genotype group ( 10.0 vs 9.8 ms(-1), P = 0.03; 12.3 vs 11.2 ms(-1), P = 0.01; respectively). Our findings indicated that the G allele may contribute to increased arterial stiffness in the Brazilian general population and suggest a possible interaction with diabetes.

Black hole formation with an interacting vacuum energy density

We discuss the gravitational collapse of a spherically symmetric massive core of a star in which the fluid component is interacting with a growing vacuum energy density. The influence of the variable vacuum in the collapsing core is quantified by a phenomenological beta parameter as predicted by dimensional arguments and the renormalization group approach. For all reasonable values of this free parameter, we find that the vacuum energy density increases the collapsing time, but it cannot prevent the formation of a singular point. However, the nature of the singularity depends on the value of beta. In the radiation case, a trapped surface is formed for beta <= 1/2, whereas for beta >= 1/2, a naked singularity is developed. In general, the critical value is beta = 1-2/3(1 + omega) where omega is the parameter describing the equation of state of the fluid component.

The nature of extremely red galaxies in the local universe

We investigate the nature of extremely red galaxies (ERGs), objects whose colours are redder than those found in the red sequence present in colour–magnitude diagrams of galaxies. We selected from the Sloan Digital Sky Survey Data Release 7 a volume-limited sample of such galaxies in the redshift interval 0.010 < z < 0.030, brighter than Mr = −17.8 (magnitudes dereddened, corrected for the Milky Way extinction) and with (g − r) colours larger than those of galaxies in the red sequence. This sample contains 416 ERGs, which were classified visually. Our classification was cross-checked with other classifications available in the literature. We found from our visual classification that the majority of objects in our sample are edge-on spirals (73 per cent). Other spirals correspond to 13 per cent, whereas elliptical galaxies comprise only 11 per cent of the objects. After comparing the morphological mix and the distributions of Hα/Hβ and axial ratios of ERGs and objects in the red sequence, we suggest that dust, more than stellar population effects, is the driver of the red colours found in these extremely red galaxies.

A semi-supervised classification technique based on interacting forces

Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed technique achieves good classification results on artificial data sets and is comparable to well-known semi-supervised techniques using benchmark data sets.