Massive stars and globular cluster formation

We first present chemodynamical simulations to investigate how stellar winds of massive stars influence early dynamical and chemical evolution of forming globular clusters (GCs). In our numerical models, GCs form in turbulent,high-density giant molecular clouds (GMCs), which are embedded in a massive dark matter halo at high redshifts. We show how high-density, compact stellar systems are formed from GMCs influenced both by physical processes associated with star formation and by tidal fields of their host halos. We also show that chemical pollution of GC-forming GMCs by stellar winds from massive stars can result in star-to-star abundance inhomogeneities among light elements (e.g., C, N, and O) of stars in GCs. The present model with a canonical initial mass function (IMF) also shows a C-N anticorrelation that stars with smaller [C/Fe] have larger [N/Fe] in a GC. Although these results imply that ``self-pollution'' of GC-forming GMCs by stellar winds from massive stars can cause abundance inhomogeneities of GCs, the present models with different parameters and canonical IMFs can not show N-rich stars with [N/Fe] ~ 0.8 observed in some GCs (e.g., NGC 6752). We discuss this apparent failure in the context of massive star formation preceding low-mass one within GC-forming GMCs (``bimodal star formation scenario''). We also show that although almost all stars (~97%) show normal He abundances (Y) of ~0.24 some stars later formed in GMCs can have Y as high as ~0.3 in some models. The number fraction of He-rich stars with Y >0.26 is however found to be small (~10^-3) for most models.Comment: 10 pages, 8 figures, accepted by Ap

Light controlled magnetoresistance and magnetic field controlled photoresistance in CoFe film deposited on BiFeO3

We present a magnetoresistive-photoresistive device based on the interaction of a piezomagnetic CoFe thin film with a photostrictive BiFeO3 substrate that undergoes light-induced strain. The magnitude of the resistance and magnetoresistance in the CoFe film can be controlled by the wavelength of the incident light on the BiFeO3. Moreover, a light-induced decrease in anisotropic magnetoresistance is detected due to an additional magnetoelastic contribution to magnetic anisotropy of the CoFe film. This effect may find applications in photo-sensing systems, wavelength detectors and can possibly open a research development in light-controlled magnetic switching properties for next generation magnetoresistive memory devices.Comment: 5 pages, 4 figures, journal pape

Exploração mecanizada da floresta tropical úmida sem babaçu.

bitstream/item/33767/1/CPATU-CirTec9.pd

Hierarchical Spatial Organization of Geographical Networks

In this work we propose the use of a hirarchical extension of the polygonality index as a means to characterize and model geographical networks: each node is associated with the spatial position of the nodes, while the edges of the network are defined by progressive connectivity adjacencies. Through the analysis of such networks, while relating its topological and geometrical properties, it is possible to obtain important indications about the development dynamics of the networks under analysis. The potential of the methodology is illustrated with respect to synthetic geographical networks.Comment: 3 page, 3 figures. A wokring manuscript: suggestions welcome

Far Infrared Slab Lensing and Subwavelength Imaging in Crystal Quartz

We examine the possibility of using negative refraction stemming from the phonon response in an anisotropic crystal to create a simple slab lens with plane parallel sides, and show that imaging from such a lens should be possible at room temperature despite the effects of absorption that are inevitably present due to phonon damping. In particular, we consider the case of crystal quartz, a system for which experimental measurements consistent with all-angle negative refraction have already been demonstrated. Furthermore, we investigate the possibility of subwavelength imaging from such materials, and show that it should be possible for certain configurations.Comment: 12 pages, 10 figure

A Complex Network Approach to Topographical Connections

The neuronal networks in the mammals cortex are characterized by the coexistence of hierarchy, modularity, short and long range interactions, spatial correlations, and topographical connections. Particularly interesting, the latter type of organization implies special demands on the evolutionary and ontogenetic systems in order to achieve precise maps preserving spatial adjacencies, even at the expense of isometry. Although object of intensive biological research, the elucidation of the main anatomic-functional purposes of the ubiquitous topographical connections in the mammals brain remains an elusive issue. The present work reports on how recent results from complex network formalism can be used to quantify and model the effect of topographical connections between neuronal cells over a number of relevant network properties such as connectivity, adjacency, and information broadcasting. While the topographical mapping between two cortical modules are achieved by connecting nearest cells from each module, three kinds of network models are adopted for implementing intracortical connections (ICC), including random, preferential-attachment, and short-range networks. It is shown that, though spatially uniform and simple, topographical connections between modules can lead to major changes in the network properties, fostering more effective intercommunication between the involved neuronal cells and modules. The possible implications of such effects on cortical operation are discussed.Comment: 5 pages, 5 figure

Relações hipsométricas para plantios adensados de castanheira-do-brasil (Bertholletia excelsa) na Amazônia Central.

Neste trabalho, foram ajustadas 23 equações matemáticas com o objetivo de selecionar a que melhor descreva a relação hipsométrica em plantios adensados de castanheira

Knowledge Acquisition by Networks of Interacting Agents in the Presence of Observation Errors

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erd\H{o}s-Renyi or Barabasi-Albert type. First we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall behavior of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.Comment: 10 pages, 7 figures. A working manuscrip