Determining the Adjustment Baseline Parameters to Define an Accurate Customer Baseline Load

Demand response is an energy resource that has gained increasing importance in the context of competitive electricity markets and of smart grids. New business models and methods designed to integrate demand response in electricity markets and of smart grids have been published, reporting the need of additional work in this field. In order to adequately remunerate the participation of the consumers in demand response programs, improved consumers’ performance evaluation methods are needed. The methodology proposed in the present paper determines the characterization of the baseline approach that better fits the consumer historic consumption, in order to determine the expected consumption in absent of participation in a demand response event and then determine the actual consumption reduction. The defined baseline can then be used to better determine the remuneration of the consumer. The paper includes a case study with real data to illustrate the application of the proposed methodology.

Early physiological and biochemical responses of rice seedlings to low concentration of microcystin-LR

Microcystin-leucine and arginine (microcystin- LR) is a cyanotoxin produced by cyanobacteria like Microcystis aeruginosa, and it’s considered a threat to water quality, agriculture, and human health. Rice (Oryzasativa) is a plant of great importance in human food consumption and economy, with extensive use around the world. It is therefore important to assess the possible effects of using water contaminated with microcystin-LR to irrigate rice crops, in order to ensure a safe, high quality product to consumers. In this study, 12 and 20-day-old plants were exposed during 2 or 7 days to a M. aeruginosa extract containing environmentally relevant microcystin-LR concentrations, 0.26–78 lg/L. Fresh and dry weight of roots and leaves, chlorophyll fluorescence, glutathione S-transferase and glutathione peroxidase activities, and protein identification by mass spectrometry through two-dimensional gel electrophoresis from root and leaf tissues, were evaluated in order to gauge the plant’s physiological condition and biochemical response after toxin exposure. Results obtained from plant biomass, chlorophyll fluorescence, and enzyme activity assays showed no significant differences between control and treatment groups. How- ever, proteomics data indicates that plants respond to M. aeruginosa extract containing environmentally relevant microcystin-LR concentrations by changing their metabolism, responding differently to different toxin concentrations. Biological processes most affected were related to protein folding and stress response, protein biosynthesis, cell signalling and gene expression regulation, and energy and carbohydrate metabolism which may denote a toxic effect induced by M. aeruginosa extract and microcystin- LR. Theimplications of the metabolic alterations in plant physiology and growth require further elucidation.

Generalized Extraction of Real-Time Parameters for Homogeneous Synchronous Dataflow Graphs

23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2015). 4 to 6, Mar, 2015. Turku, Finland.

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.

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.

Comparação dos Perfis Lipídicos de Plasma de uma População da Região Norte

Os ácidos gordos desempenham um papel fisiológico importante como componentes indispensáveis na estrutura celular, bem como fontes de energia. Nas últimas décadas, tem havido um aumento notável do interesse público nos ácidos gordos polinsaturados ómegas 3 e 6 e no seu impacto sobre a saúde humana, especialmente em doenças metabólicas e cardiovasculares. Estes ácidos gordos específicos podem prevenir e/ou tratar várias patologias metabólicas, atuando nomeadamente como compostos anti-inflamatórios. A menopausa é um fator de risco para doença cardiovascular, a diminuição de estrogénio, que ocorre neste estado fisiológico, provoca disfunção endotelial e stresse oxidativo. Consequentemente há uma redução dos níveis de ácidos gordos polinsaturados ómegas 3, o que contribui para o aparecimento de aterosclerose e doença cardiovascular. Neste contexto, o objetivo deste estudo foi avaliar e caracterizar o perfil lipídico de ácidos gordos de uma amostra de mulheres pós-menopausa e com este, estudar as associações entre o perfil lipídico determinado e parâmetros metabólicos de risco (parâmetros clínicos e bioquímicos). Inicialmente, os ácidos gordos foram extraídos da matriz plasmática através da derivatização destes e a sua composição percentual no plasma foi determinada com recurso a cromatografia gasosa com deteção de ionização de chama. De seguida, através do software IBM SPSS Statistics 21, foram estabelecidas associações entre os parâmetros clínicos e bioquímicos e o perfil lipídico determinado. A população em estudo foi divida em dois grupos consoante o período de entrada na menopausa (há menos de 7 anos e há 7 anos ou mais). Não há conhecimento de estudos semelhantes ao apresentado, que relacionem todo o perfil de ácidos gordos com parâmetros metabólicos de risco considerando o estado menopausal. Os resultados obtidos mostram que o perfil lipídico influencia vários marcadores metabólicos / endócrinos com relevância clínica que devem ser explorados em futuros ensaios clínicos. Para as mulheres na menopausa há menos de 7 anos foram estabelecidas as seguintes relações: i) entre os ácidos gordos saturados e insaturados cis e os níveis de ALP; ii) entre os ácidos gordos mono e polinsaturados cis e os níveis de GGT, IL10 e estradiol; iii) entre os ácidos gordos polinsaturados trans e o IMC e os níveis de IL6; iv) entre os ómegas 3 e os níveis de IL10 e ácido úrico; v) entre os ómegas 6 e os níveis de estradiol, ALP e GGT; vi) entre os ómegas 9 e os níveis de estradiol e GGT; vi) entre os ácidos gordos de curta cadeia e os níveis de colesterol total, LDL, triglicerídeos e IL10; vii) entre os ácidos gordos saturados de cadeia longa e o ΣÁcido láurico, mirístico, palmítico e esteárico e os níveis de triglicerídeos, ALP e GGT; viii) os níveis de IL10 podem ser simultaneamente associados com os ácidos gordos de curta cadeia e os ómegas 3. Para as mulheres na menopausa há 7 anos ou mais foram estabelecidas relações: i) entre os ómegas 3 e o IMC e os níveis de triglicerídeos; ii) entre os ácidos gordos monoinsaturados cis e os ómegas 9 com os níveis de ALT. Relações independentes do estado menopausal também foram estabelecidas, nomeadamente: i) entre os ácidos gordos polinsaturados cis e ómegas 6 e os níveis de ALT, triglicerídeos e AST; ii) entre os níveis de ácidos gordos monoinsaturados cis e ómegas 9 e os níveis de AST e triglicerídeos. O perfil lipídico de ácidos gordos pode ser considerado um biomarcador para a condição de saúde da mulher na menopausa.