Cathodoluminescence of nanocrystalline Y2O3:Eu3+ with various Eu3+ concentrations

© The Author(s) 2014. Published by ECS. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 License (CC BY, http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse of the work in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.Herein a study on the preparation and cathodoluminescence of monosized spherical nanoparticles of Y2O3:Eu3+ having a Eu3+ concentration that varies between 0.01 and 10% is described. The luminous efficiency and decay time have been determined at low a current density, whereas cathodoluminescence-microscopy has been carried out at high current density, the latter led to substantial saturation of certain spectral transitions. A novel theory is presented to evaluate the critical distance for energy transfer from Eu3+ ions in S6 to Eu3+ ions in C2 sites. It was found that Y2O3:Eu3+ with 1–2% Eu3+ has the highest luminous efficiency of 16lm/w at 15keV electron energy. Decay times of the emission from 5D0 (C2) and 5D1 (C2) and 5D0 (S6) levels were determined. The difference in decay time from the 5D0 (C2) and 5D1 (C2) levels largely explained the observed phenomena in the cathodoluminescence-micrographs recorded with our field emission scanning electron microscope

Cathodoluminescence of Double Layers of Phosphor Particles

This article has been made available through the Brunel Open Access Publishing Fund.We present radiance measurements of particle layers of ZnO:Zn, Y2O3:Eu and Y2O2S:Eu bombarded with electrons at anode voltages between 1 and 15 kV. The layers described in this work refer to single component layers, double layers and two component mixtures. The phosphor layers are deposited on ITO-coated glass slides by settling; the efficiency of the cathodoluminescence is determined by summing the radiances and luminances in the reflected and transmitted modes respectively. The efficiency of a double layer of Y2O3:Eu on top of ZnO:Zn at high electron energy is significantly larger than the efficiency of a corresponding layer in which the two components are mixed. This result is interpreted in terms of the penetration-model, which predicts a larger efficiency for a high-voltage phosphor on top of a low-voltage phosphor. When a layer of the low-voltage phosphor ZnO:Zn is on top of the high-voltage phosphor Y2O3:Eu, we also observe a higher efficiency than that of the corresponding layer with both components mixed. In this case the efficiency increases due to suppression of charging in the Y2O3:Eu layer. Double layers of ZnO:Zn and Y2O2S:Eu did not show enhanced efficiency, because the size of the Y2O2S:Eu particles was too large to evoke the penetration effect. © The Author(s) 2014. Published by ECS

Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer Policies

RoboCup soccer competitions are considered among the most challenging multi-robot adversarial environments, due to their high dynamism and the partial observability of the environment. In this paper we introduce a method based on a combination of Monte Carlo search and data aggregation (MCSDA) to adapt discrete-action soccer policies for a defender robot to the strategy of the opponent team. By exploiting a simple representation of the domain, a supervised learning algorithm is trained over an initial collection of data consisting of several simulations of human expert policies. Monte Carlo policy rollouts are then generated and aggregated to previous data to improve the learned policy over multiple epochs and games. The proposed approach has been extensively tested both on a soccer-dedicated simulator and on real robots. Using this method, our learning robot soccer team achieves an improvement in ball interceptions, as well as a reduction in the number of opponents' goals. Together with a better performance, an overall more efficient positioning of the whole team within the field is achieved

Hi-Val: Iterative Learning of Hierarchical Value Functions for Policy Generation

Task decomposition is effective in manifold applications where the global complexity of a problem makes planning and decision-making too demanding. This is true, for example, in high-dimensional robotics domains, where (1) unpredictabilities and modeling limitations typically prevent the manual specification of robust behaviors, and (2) learning an action policy is challenging due to the curse of dimensionality. In this work, we borrow the concept of Hierarchical Task Networks (HTNs) to decompose the learning procedure, and we exploit Upper Confidence Tree (UCT) search to introduce HOP, a novel iterative algorithm for hierarchical optimistic planning with learned value functions. To obtain better generalization and generate policies, HOP simultaneously learns and uses action values. These are used to formalize constraints within the search space and to reduce the dimensionality of the problem. We evaluate our algorithm both on a fetching task using a simulated 7-DOF KUKA light weight arm and, on a pick and delivery task with a Pioneer robot

Cathodoluminescence studies of phosphors in a scanning electron microscope

Cathodoluminescence studies are reported of phosphors in a field emission scanning electron microscope (FESEM). A number of phosphor materials have been studied and exhibited a pronounced comet-like structure at high scan rates, because the particle continued to emit light after the beam had moved onto subsequent pixels. Image analysis has been used to study the loss of brightness along the tail and hence to determine the decay time of the materials. This technique provides a simple and convenient way to study the decay times of individual particles

The Quasi-Class Action Method of Managing Multi-District Litigations: Problems and a Proposal

This Article uses three recent multi-district litigations ( MDLs ) that produced massive settlements-Guidant ($240 million), Vioxx ($4.85 billion), and Zyprexa ($700 million)-to study the emerging quasi-class action approach to MDL management. This approach has four components: (1) judicial selection of lead attorneys, (2) judicial control of lead attorneys\u27 compensation, (3) forced fee transfers from non-lead lawyers to cover lead attorneys\u27 fees, and (4) judicial reduction of non-lead lawyers\u27 fees to save claimants money. These procedures have serious downsides. They make lawyers financially dependent on judges and, therefore, loyal to judges rather than clients. They compromise judges\u27 independence by involving them heavily on the plaintiffs\u27 side and making them responsible for plaintiffs\u27 success. They allocate moneys in ways that likely overcompensate some attorneys and undercompensate others, with predictable impacts on service levels. The procedures used in Guidant, Vioxx, and Zyprexa also lack needed grounding in substantive law because the common fund doctrine, which supports fee awards in class actions, does not apply in MDLs. Academics have not previously noted these shortcomings; this is the first scholarly assessment of the quasi-class action approach. This Article proposes an alternative method of MDL management. It recommends implementation of a default rule that would vest control of an MDL in a plaintiffs\u27 management committee ( PMC ) composed of the attorney or attorney-group with the most valuable client inventory, as determined objectively by the trial judge. The PMC, which would have a large interest in the success of an MDL, would then select and retain other lawyers to perform common benefit work ( CBW ) for all claimants. The PMC would also monitor the other lawyers\u27 performance. The new approach would thus use micro-incentives, rather than judicial control and oversight, as the means for organizing the production of CBW in MDLs

Epigenetics, Nutrition, and Infant Health

The field of epigenetics is currently garnering a great deal of interest, exploring how our very molecular makeup in the form of modifications to the genome can be altered by factors as diverse as aging, disease, nutrition, stress, alcohol, and exposure to pollutants. Epigenetic changes have previously been implicated in the etiology of a variety of diseases, notably in the development of certain cancers, and inherited growth disorder syndromes, but the exploration of epigenetics’ role in fetal programming is still in its infancy. This chapter focuses on how nutritional exposures during pregnancy may affect the infant epigenome, and the impact that such modifications may have on the long-term health of the child. We start by describing some keys concepts in epigenetics and discuss windows of epigenetic plasticity in the context of the developmental origins of health and disease (DOHaD) hypothesis. We then review some of the key mechanisms by which nutrition can affect the epigenome, with a particular focus on the role of one-carbon metabolism. We finish by outlining some of the child health outcomes that have been linked to epigenetic dysregulation, and discuss possible next steps that need to be realized if insights into the basic science of epigenetics are to be translated into tangible public health benefits