Point-Coupling Models from Mesonic Hypermassive Limit and Mean-Field Approaches

In this work we show how nonlinear point-coupling models, described by a Lagrangian density that presents only terms up to fourth order in the fermion condensate $(\bar{\psi}\psi)$, are derived from a modified meson-exchange nonlinear Walecka model. The derivation can be done through two distinct methods, namely, the hypermassive meson limit within a functional integral approach, and the mean-field approximation in which equations of state at zero temperature of the nonlinear point-coupling models are directly obtained.Comment: 18 pages. Accepted for publication in Braz. J. Phy

Yield and Quality of Annual Ryegrass Grown in Pure Stand and in Mixtures with Squarrosum Clover

The objective of this study was to evaluate the importance of growing annual ryegrass in mixtures instead of ryegrass alone in order to reduce nitrogen application and thereby lowering production costs, and environmental pollution

Rocking and kinematic analysis of two masonry church façades

The paper deals with the application of two methods of local analysis on masonry structures. Rocking and kinematic analysis are applied to two cases study: a gable of the Ica Cathedral that survived the 2007 Pisco earthquake and a church façade connected to transverse walls, which collapsed in the 2012 Emilia Romagna earthquake. The critical aspects of both analysis are discussed and the differences in the outputs commented. Being the two rigid blocks at height different from zero, an amplification factor of the seismic record was calculated for performing the rocking analysis. The gable is treated as free-standing block whereas the upper part of the church façade is analyzed in the rocking analysis by accounting for the rebound effect caused by the transverse walls, through the stiffness of a bed spring.The authors wish to thank Ing. Luciano Bellesia for his helpful cooperation. The activity is cosponsored by Consortium RELUIS – Masonry 2014

The Effect of Plant Population on the Yield and Quality of Annual Rye-Grass

The primary objectives of this study were to evaluate the effect of three plant population levels (350, 750 and 1150 plants m-2) on dry matter yield and forage quality (crude protein and dry matter digestibility) of four rye-grass genotypes (Barspectra, Billion, Clipper and Pollanum) used in two harvests (March and May). The results for dry matter yield means by year, genotype, and harvest were always higher in the second harvest than in the first, and the highest total mean value was reached in the first year (5853 Kg ha-1). The genotype Billion was the most stable over years. Concerning to plant population there was a trend for the highest level to conduct to the best results only in the first year, for most of the genotypes. Protein concentration was greater in the first harvest (206 g kg-1) than in the second (124 g kg-1). It was also found that the best value was reached at the lowest plant population level and that Billion genotype showed the lowest content, 161 g kg-1, but not very much different from the others. For dry matter digestibility the highest values were found in the second year (740 g kg- 1) and in the first harvest (854 g kg-1). The genotype Clipper presented the greatest value (740 g kg-1) and so did the intermediate level of plant population. As a general conclusion it can be stated that, for practical purposes, the intermediate population level (750 pl m-2), especially in dry years, and the genotype Billion should be recommended

Seismic vulnerability: from building evaluation to a typology generalization

Outlining the best strategies for seismic risk mitigation requires that both benefits and costs of retrofitting are known in advance. The assessment of the vulnerability of building typologies is a first step of a more extensive effort, concerning the analysis of the viability of seismic risk mitigation and taking into account retrofitting costs. The methodology adopted to obtain the seismic vulnerability of some classes of residential buildings existing in mainland Portugal is presented. This methodology is based on a structural analysis of individual buildings belonging to the same typology. An application example is presented to illustrate the methodology. Fragility curves of “boxed” building typology are also presented and broken down into three height classes: low-rise, medium-rise and high-rise. These curves are based on average capacity spectra derived from several individual buildings belonging to the same typology

Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions

Background. Drug-drug interaction (DDI) is a major cause of morbidity and mortality. DDI research includes the study of different aspects of drug interactions, from in vitro pharmacology, which deals with drug interaction mechanisms, to pharmaco-epidemiology, which investigates the effects of DDI on drug efficacy and adverse drug reactions. Biomedical literature mining can aid both kinds of approaches by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology. Experiments. We implemented a set of classifiers for identifying published articles relevant to experimental pharmacokinetic DDI evidence. These documents are important for identifying causal mechanisms behind putative drug-drug interactions, an important step in the extraction of large numbers of potential DDIs. We evaluate performance of several linear classifiers on PubMed abstracts, under different feature transformation and dimensionality reduction methods. In addition, we investigate the performance benefits of including various publicly-available named entity recognition features, as well as a set of internally-developed pharmacokinetic dictionaries. Results. We found that several classifiers performed well in distinguishing relevant and irrelevant abstracts. We found that the combination of unigram and bigram textual features gave better performance than unigram features alone, and also that normalization transforms that adjusted for feature frequency and document length improved classification. For some classifiers, such as linear discriminant analysis (LDA), proper dimensionality reduction had a large impact on performance. Finally, the inclusion of NER features and dictionaries was found not to help classification.IU -Indiana Universit