55,814 research outputs found

    Large-scale QRPA calculation of E1-strength and its impact on the neutron capture cross section

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    Large-scale QRPA calculations of the E1-strength are performed as a first attempt to microscopically derive the radiative neutron capture cross sections for the whole nuclear chart. A folding procedure is applied to the QRPA strength distribution to take the damping of the collective motion into account. It is shown that the resulting E1-strength function based on the SLy4 Skyrme force is in close agreement with photoabsorption data as well as the available experimental E1-strength at low energies. The increase of the E1-strength at low energies for neutron-rich nuclei is qualitatively analyzed and shown to affect the corresponding radiative neutron capture cross section significantly. A complete set of E1-strength function is made available for practical applications in a table format for all 7<Z<111 nuclei lying between the proton and the neutron drip lines.Comment: 17 pages, 8 figures, accepted for publication in Nucl. Phys.

    Use of labour induction and risk of cesarean delivery: A systematic review and meta-analysis

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    Background: Induction of labour is common, and cesarean delivery is regarded as its major complication. We conducted a systematic review and meta-analysis to investigate whether the risk of cesarean delivery is higher or lower following labour induction compared with expectant management. Methods: We searched 6 electronic databases for relevant articles published through April 2012 to identify randomized controlled trials (RCTs) in which labour induction was compared with placebo or expectant management among women with a viable singleton pregnancy. We assessed risk of bias and obtained data on rates of cesarean delivery. We used regression analysis techniques to explore the effect of patient characteristics, induction methods and study quality on risk of cesarean delivery. Results: We identified 157 eligible RCTs (n = 31 085). Overall, the risk of cesarean delivery was 12% lower with labour induction than with expectant management (pooled relative risk [RR] 0.88, 95% confidence interval [CI] 0.84-0.93; I2 = 0%). The effect was significant in term and post-term gestations but not in preterm gestations. Meta-regression analysis showed that initial cervical score, indication for induction and method of induction did not alter the main result. There was a reduced risk of fetal death (RR 0.50, 95% CI 0.25-0.99; I2 = 0%) and admission to a neonatal intensive care unit (RR 0.86, 95% CI 0.79-0.94), and no impact on maternal death (RR 1.00, 95% CI 0.10-9.57; I2 = 0%) with labour induction. Interpretation: The risk of cesarean delivery was lower among women whose labour was induced than among those managed expectantly in term and post-term gestations. There were benefits for the fetus and no increased risk of maternal death. © 2014 Canadian Medical Association or its licensors

    The Giant Monopole Resonance in Pb isotopes

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    The extraction of the nuclear incompressibility from the isoscalar giant monopole resonance (GMR) measurements is analysed. Both pairing and mutually enhanced magicity (MEM) effects play a role in the shift of the GMR energy between the doubly closed shell 208^{208}Pb nucleus and other Pb isotopes. Pairing effects are microscopically predicted whereas the MEM effect is phenomenologically evaluated. Accurate measurements of the GMR in open-shell Pb isotopes are called for.Comment: 4 page

    Microscopic HFB+QRPA predictions of dipole strength for astrophysics applications

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    Large-scale QRPA calculations of the E1 strength are performed on top of HFB calculations in order to derive the radiative neutron capture cross sections for the whole nuclear chart. The spreading width of the GDR is taken into account by analogy with the second-RPA (SRPA) method. The accuracy of HFB+QRPA model based on various Skyrme forces with different pairing prescription and parameterization is analyzed. It is shown that the present model allows to constrain the effective nucleon-nucleon interaction with the GDR data and to provide quantitative predictions of dipole strengths.Comment: 21 pages, 9 figure

    Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

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    With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks
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