Rule-based reservoir operation considering long range forecast

A model for long range and real time reservoir operations is developed, considering the medium and long range weather forecast provided by the meteorological agency. The reasoning employed by the reservoir operator to make the appropriate decision on the reservoir operations, in the presence of uncertainty and inevitable errors in the forecast, is modeled through a rule-based scheme. A fuzzy inference procedure is used to evaluate the rules and produce the control output. The forecast inputs are of medium and long range inflow rates and trends. The operations are conducted according to "control levels" that are related to control actions designed to keep the reservoir state as near as possible to the target one. The simulation of the operation of a single reservoir throughout the year is performed for water utilization, hydropower and river preservation purposes. The focus is on drought management, and the results show that the model behaviour is coherent with the model formulation

Estudo das propriedades dos concretos confeccionados com cimento CP V - ARI e CP II - F32, sob diferentes temperaturas de mistura e métodos de cura

Orientador: Prof. Dr. Kleber F. PortellaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Curso de Pós-Graduação em Engenharia de Materiais e ProcessosInclui referências: p. 90-95Área de concentração: Engenharia e Ciência de MateriaisResumo: O estudo das propriedades do concreto é imprescindível na busca da melhoria da qualidade do material. Sabe-se que o concreto está sujeito a sofrer modificações em suas propriedades em função de uma enorme gama de variáveis. O entendimento de como estão relacionadas essas variáveis e as modificações no material se faz necessário. Nesse trabalho são analisadas as influências da temperatura de mistura, do tipo de cimento e o método de cura nas propriedades do concreto. Para avaliar as alterações do material são utilizadas metodologias como: ensaios físico-químicos dos materiais, inspeção visual, ensaios de resistência à compressão, propriedades elétricas, comportamento térmico, análises microscópicas e de difração de raios X. Com o uso dessas metodologias pode-se verificar que os fatores estudados influenciam de forma significativa nas propriedades do concreto. Concretos misturados nas temperaturas estudadas, 10, 20 e 60 °C, apresentaram alterações na resistência à compressão em função destas temperaturas. Misturas a temperaturas mais elevadas proporcionam concretos com maior resistência mecânica nas primeiras idades, podendo esses resultados serem modificados no decorrer do tempo. A quantidade e distribuição dos poros na microestrutura variaram conforme o caso estudado, onde cp's misturados nas maiores temperaturas apresentaram maior incidência de poros. Diferentes fases do material foram identificadas em função da temperatura de mistura e do método de cura. No método de cura à temperatura elevada identificou-se que os cp's analisados apresentam maior probabilidade corrosiva, se comparados aos cp's curados em câmara úmida a 23 °C.Abstract: The study of concrete properties is essential in the search for its quality improvement. It's known that the concrete is subjected to property modifications in terms of an enormous range of variables. Understanding how variables are related to the modifications in the material is necessary. In this work, the influences of the mixing temperature, as well as of the cement type and the curing method in the properties of the concrete are analyzed. To evaluate the alterations in the material, the following methodologies are used: physical-chemical analysis of the materials, visual inspection, compressive strength, electrical properties tests, thermal behavior, microscopical analyses and x-ray diffraction. With the use of these methodologies it can be verified that the studied factors have significant influence in the properties of the concrete. Concrete mixed at different temperatures 10, 20 and 60 °C, presented alterations in the compressive strength. The mixtures made at the highest temperatures provided concretes with higher mechanical resistance in the early ages, with a varying behavior in time. The amount and distribution of pores in the microstructure varied according to the studied case, where specimens mixed at the highest temperatures presented a greater incidence of pores. Different phases of the material were identified as function of the mixing temperature and the curing method. In the curing at high temperatures it was identified that analyzed specimens presented greater probability of corrosion

The phosphatidylinositol synthase of proximal tubule cells

Phosphatidylinositol (PI) is a precursor for an important class of phospholipids, the phosphatidylinositol polyphosphates. Because renal myo-inositol levels may vary under both physiological (e.g., antidiuretic) and pathophysiological (e.g., diabetic) conditions, the formation of PI from CDP-diacylglyceroI (CDP-DG) and myo-inositol via phosphatidylinositol synthase and the regulation of this enzyme have important implications for the cellular biology of renal epithelia. We sought to understand the role of PI synthase by determining its subcellular localization, kinetic properties and regulation in rabbit proximal tubule cells. Proximal tubule cells were isolated from New Zealand White rabbits. The subcellular synthesis of PI was assessed by [32P]orthophosphate labelling with subsequent subcellular fractionation. Labelling of PI was time-dependent and consistent with the rapid incorporation of 32PO4 into basolateral, brush-border, microsomal and nuclear fractions. Pulse-chase labelling of proximal tubule cells was consistent with the formation of PI in microsomal fraction of the proximal tubule cells in addition to both brush-border and basolateral membranes. Conversely, phosphatidylcholine, phosphatidylethanolamine and phosphatidylglycerol displayed radiolabelling patterns consistent with microsomal synthesis alone. The in situ formation of phosphatidylinositol was substantiated by the direct measurement of phosphatidylinositol synthase activity in basolateral, brush-border and microsomal fractions. The apparent Km values for myo-inositol were 0.32 +/- 0.19, 0.39 +/- 0.21 and 0.23 +/- 0.05 mM, and for CDP-DG were 0.12 +/- 0.02, 0.14 +/- 0.05 and 0.12 +/- 0.02 mM in basolateral, brush-border and microsomal fractions, respectively. Vmax values for phosphatidylinositol formation were slightly, but not significantly greater, in microsomal than for plasma membrane fractions. Moreover, based on enzymatic enrichment data, plasma membrane PI synthase activity could not be explained by microsomal cross-contamination alone. PI synthase activity was inhibited by co-incubation with PI without differences among the cellular fractions. Intracellular myo-inositol concentration in the proximal tubule cells as measured by gas-liquid chromatography was 20.5 mM, significantly greater than the apparent Km values for myo-inositol. In conclusion, the in situ synthesis of phosphatidylinositol occurs in several membrane fractions; the kinetic properties of phosphatidylinositol synthase appear to be similar in each fraction; and phosphatidylinositol synthase in proximal tubule cells is inhibited by its own formation product. These data suggest that myo-inositol concentration alone is unlikely to be an important regulator of the chemical mass of phosphatidylinositol at the levels of this polyol observed in rabbit kidney.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28582/1/0000389.pd

Bosch's industry 4.0 advanced Data Analytics: historical and predictive data integration for decision support

Industry 4.0, characterized by the development of automation and data exchanging technologies, has contributed to an increase in the volume of data, generated from various data sources, with great speed and variety. Organizations need to collect, store, process, and analyse this data in order to extract meaningful insights from these vast amounts of data. By overcoming these challenges imposed by what is currently known as Big Data, organizations take a step towards optimizing business processes. This paper proposes a Big Data Analytics architecture as an artefact for the integration of historical data - from the organizational business processes - and predictive data - obtained by the use of Machine Learning models -, providing an advanced data analytics environment for decision support. To support data integration in a Big Data Warehouse, a data modelling method is also proposed. These proposals were implemented and validated with a demonstration case in a multinational organization, Bosch Car Multimedia in Braga. The obtained results highlight the ability to take advantage of large amounts of historical data enhanced with predictions that support complex decision support scenarios.This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UIDB/00319/2020, the Doctoral scholarships PD/BDE/135100/2017 and PD/BDE/135105/2017, and European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n degrees 039479; Funding Reference: POCI-01-0247-FEDER039479]. The authors also wish to thank the automotive electronics company staff involved with this project for providing the data and valuable domain feedback. This paper uses icons made by Freepik, from www.flaticon.com