Smart platform towards batteries analysis based on internet-of-things

This paper presents a new approach of pre-defined profiles, based in different voltage and current values, to control the charging and discharging processes of batteries in order to assess their performance. This new approach was implemented in a prototype that was specially developed for such purpose. This prototype is a smart power electronics platform that allows to perform batteries analysis and to control the charging and discharging processes through a web application using pre-defined profiles. This platform was developed aiming to test different batteries technologies. Considering the relevance of the energy storage area based in batteries, especially for the batteries applied to electric mobility systems, this platform allows to perform controlled tests to the batteries, in order to analyze the batteries performance under different scenarios of operation. Besides the results obtained with the batteries, this work also intends to produce results that can contribute to an involvement in the strengthening of the Internet-of-Things.

Search for the bb¯ decay of the Standard Model Higgs boson in associated (W/Z)H production with the ATLAS detector

A search for the bb¯ decay of the Standard Model Higgs boson is performed with the ATLAS experiment using the full dataset recorded at the LHC in Run 1. The integrated luminosities used from pp collisions at s√=7 and 8 TeV are 4.7 and 20.3 fb−1, respectively. The processes considered are associated (W/Z)H production, where W→eν/μν, Z→ee/μμ and Z→νν. The observed (expected) deviation from the background-only hypothesis corresponds to a significance of 1.4 (2.6) standard deviations and the ratio of the measured signal yield to the Standard Model expectation is found to be μ=0.52±0.32(stat.)±0.24(syst.) for a Higgs boson mass of 125.36 GeV. The analysis procedure is validated by a measurement of the yield of (W/Z)Z production with Z→bb¯ in the same final states as for the Higgs boson search, from which the ratio of the observed signal yield to the Standard Model expectation is found to be 0.74±0.09(stat.)±0.14(syst.).

Validation of the Portuguese version of the Evidence-Based Practice Questionnaire

OBJECTIVES: To describe the process of translation and linguistic and cultural validation of the Evidence Based Practice Questionnaire for the Portuguese context: Questionário de Eficácia Clínica e Prática Baseada em Evidências (QECPBE). METHOD: A methodological and cross-sectional study was developed. The translation and back translation was performed according to traditional standards. Principal Components Analysis with orthogonal rotation according to the Varimax method was used to verify the QECPBE's psychometric characteristics, followed by confirmatory factor analysis. Internal consistency was determined by Cronbach's alpha. Data were collected between December 2013 and February 2014. RESULTS: 358 nurses delivering care in a hospital facility in North of Portugal participated in the study. QECPBE contains 20 items and three subscales: Practice (α=0.74); Attitudes (α=0.75); Knowledge/Skills and Competencies (α=0.95), presenting an overall internal consistency of α=0.74. The tested model explained 55.86% of the variance and presented good fit: χ2(167)=520.009; p = 0.0001; χ2df=3.114; CFI=0.908; GFI=0.865; PCFI=0.798; PGFI=0.678; RMSEA=0.077 (CI90%=0.07-0.08). CONCLUSION: confirmatory factor analysis revealed the questionnaire is valid and appropriate to be used in the studied context.

Anxiety and depression symptoms in women and men from early pregnancy to 3-months postpartum: parity differences and effects

This study aimed to investigate both anxiety and depression symptoms from early pregnancy to 3-months postpartum, comparing women and men and first and second-time parents. Methods: A sample of 260 Portuguese couples (N=520), first or second-time parents, recruited in an Obstetrics Out-patients Unit, filled in the State-Anxiety Inventory (STAI-S) and the Edinburgh Post-Natal Depression Scale (EPDS) at the 1st, 2nd and 3rd pregnancy trimesters, childbirth, and 3-months postpartum. Results: A decrease in anxiety and depression symptoms from early pregnancy to 3-months postpartum was found in both women and men, as well as in first and second-time parents. Men presented less anxiety and depression symptoms than women, but the same pattern of symptoms over time. Second-time parents showed more anxiety and depression symptoms than first-time parents and a different pattern of symptoms over time: an increase in anxiety and depression symptoms from the 3rd trimester to childbirth was observed in first-time parents versus a decrease in second-time parents. Limitations: The voluntary nature of the participation may have lead to a selection bias; women and men who agreed to participate could be those who presented fewer anxiety and depression symptoms. Moreover, the use of self-report symptom measures does not give us the level of possible disorder in participants. Conclusions: Anxiety and depression symptoms diminish from pregnancy to the postpartum period in all parents. Patterns of anxiety and depression symptoms from early pregnancy to 3-months postpartum are similar in women and men, but somewhat different in first and second time parents. Second-time parents should also be considered while studying and intervening during pregnancy and the postpartum.

New integrative computational approaches unveil the Saccharomyces cerevisiae pheno-metabolomic fermentative profile and allow strain selection for winemaking

During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of most-correlated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains.