15,227 research outputs found
Group Mentoring: A Study of Mentoring Groups in Three Programs
In an effort to provide more youth with mentors, mentoring programs are implementing several promising new approaches. This report describes the strengths and challenges of group mentoring-an approach that is gaining popularity. Findings suggest that group mentoring is reaching youth and volunteers who are unlikely to participate in traditional one-on-one mentoring, and that the approach may provide youth with important benefits, especially the development of social skills. On the other hand, mentoring groups vary widely in their size, structure and focus, and in the extent to which they foster strong mentoring relationships and benefits for youth. Implications for the mentoring field and for future research are discussed
Charging Interacting Rotating Black Holes in Heterotic String Theory
We present a formulation of the stationary bosonic string sector of the whole
toroidally compactified effective field theory of the heterotic string as a
double Ernst system which, in the framework of General Relativity describes, in
particular, a pair of interacting spinning black holes; however, in the
framework of low--energy string theory the double Ernst system can be
particularly interpreted as the rotating field configuration of two interacting
sources of black hole type coupled to dilaton and Kalb--Ramond fields. We
clarify the rotating character of the --component of the
antisymmetric tensor field of Kalb--Ramond and discuss on its possible torsion
nature. We also recall the fact that the double Ernst system possesses a
discrete symmetry which is used to relate physically different string vacua.
Therefore we apply the normalized Harrison transformation (a charging symmetry
which acts on the target space of the low--energy heterotic string theory
preserving the asymptotics of the transformed fields and endowing them with
multiple electromagnetic charges) on a generic solution of the double Ernst
system and compute the generated field configurations for the 4D effective
field theory of the heterotic string. This transformation generates the
vector field content of the whole low--energy heterotic string
spectrum and gives rise to a pair of interacting rotating black holes endowed
with dilaton, Kalb--Ramond and multiple electromagnetic fields where the charge
vectors are orthogonal to each other.Comment: 15 pages in latex, revised versio
Charged Cylindrical Collapse of Anisotropic Fluid
Following the scheme developed by Misner and Sharp, we discuss the dynamics
of gravitational collapse. For this purpose, an interior cylindrically
symmetric spacetime is matched to an exterior charged static cylindrically
symmetric spacetime using the Darmois matching conditions. Dynamical equations
are obtained with matter dissipating in the form of shear viscosity. The effect
of charge and dissipative quantities over the cylindrical collapse are studied.
Finally, we show that homogeneity in energy density and conformal flatness of
spacetime are necessary and sufficient for each other.Comment: 19 pages, accepted for publication in Gen. Relativ. Gra
Polydisperse fluid mixtures of adhesive colloidal particles
We investigate polydispersity effects on the average structure factor of
colloidal suspensions of neutral particles with surface adhesion. A sticky hard
sphere model alternative to Baxter's one is considered. The choice of
factorizable stickiness parameters in the potential allows a simple analytic
solution, within the ``mean spherical approximation'', for any number of
components and arbitrary stickiness distribution. Two particular cases are
discussed: i) all particles have different sizes but equal stickiness (Model
I), and ii) each particle has a stickiness proportional to its size (Model II).
The interplay between attraction and polydispersity yields a markedly different
behaviour for the two Models in regimes of strong coupling (i.e. strong
adhesive forces and low temperature) and large polydispersity. These results
are then exploited to reanalyze experimental scattering data on sterically
stabilized silica particles.Comment: 9 pages, 2 figures (included), Physica A (2001) to appea
Cosmological Neutrino Background Revisited
We solve the Boltzmann equation for cosmological neutrinos around the epoch
of the electron-positron annihilation in order to verify the freeze-out
approximation and to compute accurately the cosmological neutrino distribution
function. We find the radiation energy density to be about 0.3% higher than the
one predicted by the freeze-out approximation. As a result, the spectrum of the
Cosmic Microwave Background anisotropies changes by 0.3-05%, depending on the
angular scale, and the amplitude of the mass fluctuations on scales below about
100 h^{-1} Mpc decreases by about 0.2-0.3%.Comment: An error is corrected, figure revised; submitted to Ap
XTHs from Fragaria vesca: Genomic structure and transcriptomic analysis in ripening fruit and other tissues
Indexación: Scopus.Background: Fragaria vesca or 'woodland strawberry' has emerged as an attractive model for the study of ripening of non-climacteric fruit. It has several advantages, such as its small genome and its diploidy. The recent availability of the complete sequence of its genome opens the possibility for further analysis and its use as a reference species. Fruit softening is a physiological event and involves many biochemical changes that take place at the final stages of fruit development; among them, the remodeling of cell walls by the action of a set of enzymes. Xyloglucan endotransglycosylase/hydrolase (XTH) is a cell wall-associated enzyme, which is encoded by a multigene family. Its action modifies the structure of xyloglucans, a diverse group of polysaccharides that crosslink with cellulose microfibrills, affecting therefore the functional structure of the cell wall. The aim of this work is to identify the XTH-encoding genes present in F. vesca and to determine its transcription level in ripening fruit. Results: The search resulted in identification of 26 XTH-encoding genes named as FvXTHs. Genetic structure and phylogenetic analyses were performed allowing the classification of FvXTH genes into three phylogenetic groups: 17 in group I/II, 2 in group IIIA and 4 in group IIIB. Two sequences were included into the ancestral group. Through a comparative analysis, characteristic structural protein domains were found in FvXTH protein sequences. In complement, expression analyses of FvXTHs by qPCR were performed in fruit at different developmental and ripening stages, as well as, in other tissues. The results showed a diverse expression pattern of FvXTHs in several tissues, although most of them are highly expressed in roots. Their expression patterns are not related to their respective phylogenetic groups. In addition, most FvXTHs are expressed in ripe fruit, and interestingly, some of them (FvXTH 18 and 20, belonging to phylogenic group I/II, and FvXTH 25 and 26 to group IIIB) display an increasing expression pattern as the fruit ripens. Conclusion: A discrete group of FvXTHs (18, 20, 25 and 26) increases their expression during softening of F. vesca fruit, and could take part in cell wall remodeling required for softening in collaboration with other cell wall degrading enzymes.https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-4255-
Modeling the Temporal Nature of Human Behavior for Demographics Prediction
Mobile phone metadata is increasingly used for humanitarian purposes in
developing countries as traditional data is scarce. Basic demographic
information is however often absent from mobile phone datasets, limiting the
operational impact of the datasets. For these reasons, there has been a growing
interest in predicting demographic information from mobile phone metadata.
Previous work focused on creating increasingly advanced features to be modeled
with standard machine learning algorithms. We here instead model the raw mobile
phone metadata directly using deep learning, exploiting the temporal nature of
the patterns in the data. From high-level assumptions we design a data
representation and convolutional network architecture for modeling patterns
within a week. We then examine three strategies for aggregating patterns across
weeks and show that our method reaches state-of-the-art accuracy on both age
and gender prediction using only the temporal modality in mobile metadata. We
finally validate our method on low activity users and evaluate the modeling
assumptions.Comment: Accepted at ECML 2017. A previous version of this paper was titled
'Using Deep Learning to Predict Demographics from Mobile Phone Metadata' and
was accepted at the ICLR 2016 worksho
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