25 resultados para media consumption


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Se a guerra é a continuação da política por outros meios, então certamente que nela a comunicação, em sentido lato, esteve sempre presente e desempenhou um papel chave. As guerras não implicam apenas a violência, mas também a persuasão, a contrainformação, o convencimento e o combate ideológico. Nas guerras modernas, os media têm sido um elemento fundamental para mobilizar nações moldadas por dinâmicas de desenraizamento e desterritorialização com vista a um esforço conjunto de apoio popular à ação bélica do Estado. Este artigo incide nas transformações que a própria teoria e investigação em comunicação e media sofreram no período entre as duas Guerras Mundiais do século XX. Trata-se de um contexto histórico decisivo para compreender como a institucionalização do campo da comunicação num país central como os EUA ocorreu em condições sociais e políticas que contribuíram para o seu perfil epistemológico, posições teóricas e para a configuração do poder no próprio sistema científico universitário, cujas repercussões continuam a fazer-se sentir de diversas e complexas formas.

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It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.

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Passive films were grown in potentiodynamic mode, by cyclic voltammetry on AISI 316 and AISI 304 stainless steels. The composition of these films was investigated by X-ray photoelectron spectroscopy (XPS). The electrochemical behaviour and the chemical composition of the passive films formed by cyclic voltammetry were compared to those of films grown under natural conditions (by immersion at open circuit potential, OCP) in alkaline solutions simulating concrete. The study included the effect of pH of the electrolyte and the effect of the presence of chloride ions. The XPS results revealed important changes in the passive film composition, which becomes enriched in chromium and depleted in magnetite as the pH decreases. On the other hand, the presence of chlorides promotes a more oxidised passive layer. The XPS results also showed relevant differences on the composition of the oxide layers for the films formed under cyclic voltammetry and/or under OCP.

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"It is a widely accepted fact that the consumption-based capital asset pricing model (CCAPM) fails to provide a good explanation of many important features of the behaviour of financial market returns in a large range of countries over a long period of time. However, within a representative consumer/investor model, it is hard to see how the basic structure of the consumption based model can be safely abandoned." [introdução]

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This paper discusses the results of applied research on the eco-driving domain based on a huge data set produced from a fleet of Lisbon's public transportation buses for a three-year period. This data set is based on events automatically extracted from the control area network bus and enriched with GPS coordinates, weather conditions, and road information. We apply online analytical processing (OLAP) and knowledge discovery (KD) techniques to deal with the high volume of this data set and to determine the major factors that influence the average fuel consumption, and then classify the drivers involved according to their driving efficiency. Consequently, we identify the most appropriate driving practices and styles. Our findings show that introducing simple practices, such as optimal clutch, engine rotation, and engine running in idle, can reduce fuel consumption on average from 3 to 5l/100 km, meaning a saving of 30 l per bus on one day. These findings have been strongly considered in the drivers' training sessions.

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Energy efficiency plays an important role to the CO2 emissions reduction, combating climate change and improving the competitiveness of the economy. The problem presented here is related to the use of stand-alone diesel gen-sets and its high specific fuel consumptions when operates at low loads. The variable speed gen-set concept is explained as an energy-saving solution to improve this system efficiency. This paper details how an optimum fuel consumption trajectory based on experimentally Diesel engine power map is obtained.

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Trabalho de projeto apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.

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Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.

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The cleaning of syngas is one of the most important challenges in the development of technologies based on gasification of biomass. Tar is an undesired byproduct because, once condensed, it can cause fouling and plugging and damage the downstream equipment. Thermochemical methods for tar destruction, which include catalytic cracking and thermal cracking, are intrinsically attractive because they are energetically efficient and no movable parts are required nor byproducts are produced. The main difficulty with these methods is the tendency for tar to polymerize at high temperatures. An alternative to tar removal is the complete combustion of the syngas in a porous burner directly as it leaves the particle capture system. In this context, the main aim of this study is to evaluate the destruction of the tar present in the syngas from biomass gasification by combustion in porous media. A gas mixture was used to emulate the syngas, which included toluene as a tar surrogate. Initially, CHEMKIN was used to assess the potential of the proposed solution. The calculations revealed the complete destruction of the tar surrogate for a wide range of operating conditions and indicated that the most important reactions in the toluene conversion are C6H5CH3 + OH <-> C6H5CH2 + H2O, C6H5CH3 + OH <-> C6H4CH3 + H2O, and C6H5CH3 + O <-> OC6H4CH3 + H and that the formation of toluene can occur through C6H5CH2 + H <-> C6H5CH3. Subsequently, experimental tests were performed in a porous burner fired with pure methane and syngas for two equivalence ratios and three flow velocities. In these tests, the toluene concentration in the syngas varied from 50 to 200 g/Nm(3). In line with the CHEMKIN calculations, the results revealed that toluene was almost completely destroyed for all tested conditions and that the process did not affect the performance of the porous burner regarding the emissions of CO, hydrocarbons, and NOx.

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The development of biopharmaceutical manufacturing processes presents critical constraints, with the major constraint being that living cells synthesize these molecules, presenting inherent behavior variability due to their high sensitivity to small fluctuations in the cultivation environment. To speed up the development process and to control this critical manufacturing step, it is relevant to develop high-throughput and in situ monitoring techniques, respectively. Here, high-throughput mid-infrared (MIR) spectral analysis of dehydrated cell pellets and in situ near-infrared (NIR) spectral analysis of the whole culture broth were compared to monitor plasmid production in recombinant Escherichia coil cultures. Good partial least squares (PLS) regression models were built, either based on MIR or NIR spectral data, yielding high coefficients of determination (R-2) and low predictive errors (root mean square error, or RMSE) to estimate host cell growth, plasmid production, carbon source consumption (glucose and glycerol), and by-product acetate production and consumption. The predictive errors for biomass, plasmid, glucose, glycerol, and acetate based on MIR data were 0.7 g/L, 9 mg/L, 0.3 g/L, 0.4 g/L, and 0.4 g/L, respectively, whereas for NIR data the predictive errors obtained were 0.4 g/L, 8 mg/L, 0.3 g/L, 0.2 g/L, and 0.4 g/L, respectively. The models obtained are robust as they are valid for cultivations conducted with different media compositions and with different cultivation strategies (batch and fed-batch). Besides being conducted in situ with a sterilized fiber optic probe, NIR spectroscopy allows building PLS models for estimating plasmid, glucose, and acetate that are as accurate as those obtained from the high-throughput MIR setup, and better models for estimating biomass and glycerol, yielding a decrease in 57 and 50% of the RMSE, respectively, compared to the MIR setup. However, MIR spectroscopy could be a valid alternative in the case of optimization protocols, due to possible space constraints or high costs associated with the use of multi-fiber optic probes for multi-bioreactors. In this case, MIR could be conducted in a high-throughput manner, analyzing hundreds of culture samples in a rapid and automatic mode.