Response Time Analysis for Fixed-Priority Tasks with Multiple Probabilistic Parameters

Demo presented in 12th Workshop on Models and Algorithms for Planning and Scheduling Problems (MAPSP 2015). 8 to 12, Jun, 2015. La Roche-en-Ardenne, Belgium. Extended abstract.

Fractional Dynamics in Forest Fires

Every year forest fires consume large areas, being a major concern in many countries like Australia, United States and Mediterranean Basin European Countries (e.g., Portugal, Spain, Italy and Greece). Understanding patterns of such events, in terms of size and spatiotemporal distributions, may help to take measures beforehand in view of possible hazards and decide strategies of fire prevention, detection and suppression. Traditional statistical tools have been used to study forest fires. Nevertheless, those tools might not be able to capture the main features of fires complex dynamics and to model fire behaviour [1]. Forest fires size-frequency distributions unveil long range correlations and long memory characteristics, which are typical of fractional order systems [2]. Those complex correlations are characterized by self-similarity and absence of characteristic length-scale, meaning that forest fires exhibit power-law (PL) behaviour. Forest fires have also been proved to exhibit time-clustering phenomena, with timescales of the order of few days [3]. In this paper, we study forest fires in the perspective of dynamical systems and fractional calculus (FC). Public domain forest fires catalogues, containing data of events occurred in Portugal, in the period 1980 up to 2011, are considered. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses. The frequency spectra of such signals are determined using Fourier transforms, and approximated through PL trendlines. The PL parameters are then used to unveil the fractional-order dynamics characteristics of the data. To complement the analysis, correlation indices are used to compare and find possible relationships among the data. It is shown that the used approach can be useful to expose hidden patterns not captured by traditional tools.

QuEChERS: a sample preparation for extraction of carbaryl from rat feces

Carbamate compounds are an important group of cholinesterase inhibitors. There is a need for creating awareness regarding the risks of the inadequate carbamate use in the residential areas due to potential adverse human effects. Carbaryl is a commonly used pesticide worldwide. A simple, fast, and high throughput method was developed employing liquid chromatography with fluorescence detector to determine carbaryl residues in rat feces. The extraction was performed by using a rapid, easy, cheap, effective, reliable, and safe (QuEChERS) method, using acetonitrile as the extracting solvent. The parameters for the performance of the extraction method were optimized, such as ratio of mass of sample per volume of extraction solvent, QuEChERS content, and cleanup columns. Linear response was obtained for all calibration curves (solven and matrix-matched) over the established concentration range (5 500 mg/L) with a correlation coefficients higher than 0.999. The achieved recovery was 97.9% with relative standard deviation values of 1.1% (n D 4) at 167 mg/kg fortified concentration level and the limits of detection and quantification were 27.7 and 92.3 mg/kg respectively.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

In health related research it is common to have multiple outcomes of interest in a single study. These outcomes are often analysed separately, ignoring the correlation between them. One would expect that a multivariate approach would be a more efficient alternative to individual analyses of each outcome. Surprisingly, this is not always the case. In this article we discuss different settings of linear models and compare the multivariate and univariate approaches. We show that for linear regression models, the estimates of the regression parameters associated with covariates that are shared across the outcomes are the same for the multivariate and univariate models while for outcome-specific covariates the multivariate model performs better in terms of efficiency.

Aplicação de um método de correlação de imagem para a determinação da tenacidade à fratura em corte de adesivos estruturais

Qualquer estrutura hoje em dia deve ser resistente, robusta e leve, o que aumentou o interesse industrial e investigação nas ligações adesivas, nomeadamente pela melhoria das propriedades de resistência e fratura dos materiais. Com esta técnica de união, o projeto de estruturas pode ser orientado para estruturas mais leves, não só em relação à economia direta de peso relativamente às juntas aparafusas ou soldadas, mas também por causa da flexibilidade para ligar materiais diferentes. Em qualquer área da indústria, a aplicação em larga escala de uma determinada técnica de ligação supõe que estão disponíveis ferramentas confiáveis para o projeto e previsão da rotura. Neste âmbito, Modelos de Dano Coesivo (MDC) são uma ferramenta essencial, embora seja necessário estimar as leis MDC do adesivo à tração e corte para entrada nos modelos numéricos. Este trabalho avalia o valor da tenacidade ao corte (GIIC) de juntas coladas para três adesivos com ductilidade distinta. O trabalho experimental consiste na caracterização à fratura ao corte da ligação adesiva por métodos convencionais e pelo Integral-J. Além disso, pelo integral-J, é possível definir a forma exata da lei coesiva. Para o integral-J, é utilizado um método de correlação de imagem digital anteriormente desenvolvido para a avaliação do deslocamento ao corte do adesivo na extremidade da fenda (δs) durante o ensaio, acoplado a uma sub-rotina em Matlab® para a extração automática de δs. É também apresentado um trabalho numérico para avaliar a adequabilidade de leis coesivas triangulares aproximadas em reproduzir as curvas força-deslocamento (P-δ) experimentais dos ensaios ENF. Também se apresenta uma análise de sensibilidade para compreender a influência dos parâmetros coesivos nas previsões numéricas. Como resultado deste trabalho, foram estimadas experimentalmente as leis coesivas de cada adesivo pelo método direto, e numericamente validadas, para posterior previsão de resistência em juntas adesivas. Em conjunto com a caraterização à tração destes adesivos, é possível a previsão da rotura em modo-misto.