Abstract

Abstract
Background: Handgrip strength (HGS) is a useful indicator of nutrition status in adults, but evidence is lacking in pediatric patients. The aim of this study was to describe the association between undernutrition and HGS in pediatric patients at hospital admission, quantifying the modifying effect of disease severity, anthropometrics, and other patient characteristics on HGS. Materials and Methods: Eighty-nine inpatients aged >= 6 years consecutively admitted were recruited in a longitudinal study. Nutrition status was evaluated using body mass index (BMI) z scores, and HGS was evaluated at admission and discharge. Results: In the total sample, 30.3% of patients were undernourished at admission, and 64% lost HGS during the hospital stay. This study showed that HGS at admission was independently associated with undernutrition defined by BMI z scores (beta = 0.256, P = .037). In this multivariate analysis, sex, age, height, and BMI z scores explained 67.1% of HGS at hospital admission. Conclusion: Lower HGS may be a potential marker of undernutrition in hospitalized pediatric patients, although HGS data should be interpreted according to sex, age, and height of the patient.

Abstract

Abstract
In this paper we describe the models and the simulations that were conducted in order to access the impact of feed-in subsidized generation in the market price in Portugal in the context of the Iberian Electricity Market. In Portugal and Spain feed-in generation (namely wind power) has a large share both in terms of installed capacity and generated energy and the presence of this energy in the hourly balance originates the reduction of the market price and of the number of hours during which traditional generation (namely coal and CCGT stations) are scheduled. This paper aims at evaluating this impact both in the short term (using the real market curves) and in the long term (using a long term generation expansion planning model). The paper includes results for the Iberian power system currently having a total installed capacity above 120 GW and a total demand of 310 TWh by the end of 2013.

Abstract

Abstract
The research presented in this paper builds on previous work that lead to the definition of a family of semantic relatedness algorithms that compute a proximity given as input a pair of concept labels. The algorithms depends on a semantic graph, provided as RDF data, and on a particular set of weights assigned to the properties of RDF statements (types of arcs in the RDF graph). The current research objective is to automatically tune the weights for a given graph in order to increase the proximity quality. The quality of a semantic relatedness method is usually measured against a benchmark data set. The results produced by the method are compared with those on the benchmark using the Spearman's rank coefficient. This methodology works the other way round and uses this coefficient to tune the proximity weights. The tuning process is controlled by a genetic algorithm using the Spearman's rank coefficient as the fitness function. The genetic algorithm has its own set of parameters which also need to be tuned. Bootstrapping is based on a statistical method for generating samples that is used in this methodology to enable a large number of repetitions of the genetic algorithm, exploring the results of alternative parameter settings. This approach raises several technical challenges due to its computational complexity. This paper provides details on the techniques used to speedup this process. The proposed approach was validated with the WordNet 2.0 and the WordSim-353 data set. Several ranges of parameters values were tested and the obtained results are better than the state of the art methods for computing semantic relatedness using the WordNet 2.0, with the advantage of not requiring any domain knowledge of the ontological graph. © José Paulo Leal and Teresa Costa.