Neutron scattering study of the field-dependent ground state and the spin dynamics in S=1/2 NH4CuCl3

Elastic and inelastic neutron scattering experiments have been performed on the dimer spin system NH4CuCl3, which shows plateaus in the magnetization curve at m=1/4 and m=3/4 of the saturation value. Two structural phase transitions at T1≈156  K and at T2=70  K lead to a doubling of the crystallographic unit cell along the b direction and as a consequence a segregation into different dimer subsystems. Long-range magnetic ordering is reported below TN=1.3  K. The magnetic field dependence of the excitation spectrum identifies successive quantum phase transitions of the dimer subsystems as the driving mechanism for the unconventional magnetization process in agreement with a recent theoretical model

Species-specific field testing of Entamoeba spp. in an area of high endemicity

Entamoeba histolytica has been separated in recent years into 2 morphologically identical species: the apathogenic E. dispar and the pathogenic E. histolytica, only the latter being pathogenic. Although various laboratory techniques allow discrimination between the 2 species there is a lack of field data about the suitability of available diagnostic tests for use in epidemiological studies and few epidemiological studies using species-specific diagnosis have been performed at community level in endemic areas, especially in sub-Saharan Africa. We conducted a repeated cross-sectional study of 967 schoolchildren in central Côte d'Ivoire to compare and evaluate light microscopy, 2 different antigen detection assays, and one polymerase chain reaction (PCR) assay. Microscopy and a non-specific antigen capture Entamoeba enzyme-linked immunosorbent assay (ELISA) were used for the primary screening of all children (time t0). The prevalence of the E. histolytica/E. dispar species complex at t0 was 18 · 8% by single microscopical examination and 31 · 4% using the non-specific ELISA. Approximately 2 months after the initial screening, fresh stool specimens were collected on 2 consecutive days (t1, and t2) from (i) all the children who were positive by microscopy at t0 (n = 182) and (ii) 155 randomly selected children who were negative at the primary screening. These samples were tested with a second antigen detection ELISA specific for E. histolytica (n = 238) and with a species-specific PCR assay (n = 193). The second and third examinations (t1, and t2) revealed an additional 43 infections with the species complex E. histolytica/E. dispar, so that the cumulative microscopical prevalence for t1 and t2 was 27 · 7%. The overall prevalence of E. histolytica by species-specific ELISA antigen detection was low (0 · 83%), while the prevalence of E. dispar was 15%. When analysing only microscopically positive samples by PCR (n = 129), the ratio E. histolytica: E. dispar was very low (1:46), suggesting that the vast majority of Entamoeba infections in this area were apathogenic. Both species-specific tests performed well but the ELISA was easier to use for large-scale field screenin

Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data

Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimisation problems from disparate domains. Many such problems arising from the commercial world are permeated by data uncertainty. Existing CP approaches that accommodate uncertainty are less suited to uncertainty arising due to incomplete and erroneous data, because they do not build reliable models and solutions guaranteed to address the user's genuine problem as she perceives it. Other fields such as reliable computation offer combinations of models and associated methods to handle these types of uncertain data, but lack an expressive framework characterising the resolution methodology independently of the model. We present a unifying framework that extends the CP formalism in both model and solutions, to tackle ill-defined combinatorial problems with incomplete or erroneous data. The certainty closure framework brings together modelling and solving methodologies from different fields into the CP paradigm to provide reliable and efficient approches for uncertain constraint problems. We demonstrate the applicability of the framework on a case study in network diagnosis. We define resolution forms that give generic templates, and their associated operational semantics, to derive practical solution methods for reliable solutions.Comment: Revised versio

Upper Bounding in Inner Regions for Global Optimization under Inequality Constraints

International audienceIn deterministic continuous constrained global optimization, upper bounding the objective function generally resorts to local minimization at several nodes/iterations of the branch and bound. We propose in this paper an alternative approach when the constraints are inequalities and the feasible space has a non-null volume. First, we extract an inner region , i.e., an entirely feasible convex polyhedron or box in which all points satisfy the constraints. Second, we select a point inside the extracted inner region and update the upper bound with its cost. We describe in this paper two original inner region extraction algorithms implemented in our interval B&B called IbexOpt. They apply to nonconvex constraints involving mathematical operators like +,x,power,sqrt,exp,log,sin. This upper bounding shows very good performance obtained on medium-sized systems proposed in the COCONUT suite

A Contractor Based on Convex Interval Taylor

International audienceInterval Taylor has been proposed in the sixties by the interval analysis community for relaxing continuous non-convex constraint systems. However, it generally produces a non-convex relaxation of the solution set. A simple way to build a convex polyhedral relaxation is to select a corner of the studied domain/box as expansion point of the interval Taylor form, instead of the usual midpoint. The idea has been proposed by Neumaier to produce a sharp range of a single function andby Lin and Stadtherr to handle n × n (square) systems of equations. This paper presents an interval Newton-like operator, called X-Newton, that iteratively calls this interval convexification based on an endpoint interval Taylor. This general-purpose contractor uses no preconditioning and can handle any system of equality and inequality constraints. It uses Hansen's variant to compute the interval Taylor form and uses two opposite corners of the domain for every constraint. The X-Newton operator can be rapidly encoded, and produces good speedups in constrained global optimization and constraint satisfaction. First experiments compare X-Newton with affine arithmetic

Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century

Assessment of climate change impacts on crops in regions of complex orography such as the Iberian Peninsula (IP) requires climate model output which is able to describe accurately the observed climate. The high resolution of output provided by Regional Climate Models (RCMs) is expected to be a suitable tool to describe regional and local climatic features, although their simulation results may still present biases. For these reasons, we compared several post-processing methods to correct or reduce the biases of RCM simulations from the ENSEMBLES project for the IP. The bias-corrected datasets were also evaluated in terms of their applicability and consequences in improving the results of a crop model to simulate maize growth and development at two IP locations, using this crop as a reference for summer cropping systems in the region. The use of bias-corrected climate runs improved crop phenology and yield simulation overall and reduced the inter-model variability and thus the uncertainty. The number of observational stations underlying each reference observational dataset used to correct the bias affected the correction performance. Although no single technique showed to be the best one, some methods proved to be more adequate for small initial biases, while others were useful when initial biases were so large as to prevent data application for impact studies. An initial evaluation of the climate data, the bias correction/reduction method and the consequences for impact assessment would be needed to design the most robust, reduced uncertainty ensemble for a specific combination of location, crop, and crop management

Set optimization - a rather short introduction

Recent developments in set optimization are surveyed and extended including various set relations as well as fundamental constructions of a convex analysis for set- and vector-valued functions, and duality for set optimization problems. Extensive sections with bibliographical comments summarize the state of the art. Applications to vector optimization and financial risk measures are discussed along with algorithmic approaches to set optimization problems