Thermodynamic and topological instability approaches for forecasting glass-forming ability in the ternary Al-Ni-Y system

A thermodynamic approach to predict bulk glass-forming compositions in binary metallic systems was recently proposed. In this approach. the parameter gamma* = Delta H-amor/(Delta H-inter - Delta H-amor) indicates the glass-forming ability (GFA) from the standpoint of the driving force to form different competing phases, and Delta H-amor and Delta H-inter are the enthalpies for-lass and intermetallic formation, respectively. Good glass-forming compositions should have a large negative enthalpy for glass formation and a very small difference for intermetallic formation, thus making the glassy phase easily reachable even under low cooling rates. The gamma* parameter showed a good correlation with GFA experimental data in the Ni-Nb binary system. In this work, a simple extension of the gamma* parameter is applied in the ternary Al-Ni-Y system. The calculated gamma* isocontours in the ternary diagram are compared with experimental results of glass formation in that system. Despite sonic misfitting, the best glass formers are found quite close to the highest gamma* values, leading to the conclusion that this thermodynamic approach can lie extended to ternary systems, serving as a useful tool for the development of new glass-forming compositions. Finally the thermodynamic approach is compared with the topological instability criteria used to predict the thermal behavior of glassy Al alloys. (C) 2007 Elsevier B. V. All rights reserved.

Electric Power Forecasting Methodologies of Some South American Countries: A Comparative Analysis

In this paper, a comparative analysis of the long-term electric power forecasting methodologies used in some South American countries, is presented. The purpose of this study is to compare and observe if such methodologies have some similarities, and also examine the behavior of the results when they are applied to the Brazilian electric market. The abovementioned power forecasts were performed regarding the main four consumption classes (residential, industrial, commercial and rural) which are responsible for approximately 90% of the national consumption. The tool used in this analysis was the SAS (c) program. The outcome of this study allowed identifying various methodological similarities, mainly those related to the econometric variables used by these methods. This fact strongly conditioned the comparative results obtained.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

A hybrid commodity price-forecasting model applied to the sugar-alcohol sector

Accurate price forecasting for agricultural commodities can have significant decision-making implications for suppliers, especially those of biofuels, where the agriculture and energy sectors intersect. Environmental pressures and high oil prices affect demand for biofuels and have reignited the discussion about effects on food prices. Suppliers in the sugar-alcohol sector need to decide the ideal proportion of ethanol and sugar to optimise their financial strategy. Prices can be affected by exogenous factors, such as exchange rates and interest rates, as well as non-observable variables like the convenience yield, which is related to supply shortages. The literature generally uses two approaches: artificial neural networks (ANNs), which are recognised as being in the forefront of exogenous-variable analysis, and stochastic models such as the Kalman filter, which is able to account for non-observable variables. This article proposes a hybrid model for forecasting the prices of agricultural commodities that is built upon both approaches and is applied to forecast the price of sugar. The Kalman filter considers the structure of the stochastic process that describes the evolution of prices. Neural networks allow variables that can impact asset prices in an indirect, nonlinear way, what cannot be incorporated easily into traditional econometric models.

Forecasting fuel ethanol consumption in Brazil by time series models: 2006-2012

This article analysed scenarios for Brazilian consumption of ethanol for the period 2006 to 2012. The results show that if the country`s GDP sustains a 4.6% a year growth, domestic consumption of fuel ethanol could increase to 25.16 billion liters in this period, which is a volume relatively close to the forecasted gasoline consumption of 31 billion liters. At a lower GDP growth of 1.22% a year, gasoline consumption would be reduced and domestic ethanol consumption in Brazil would be no higher than 18.32 billion liters. Contrary to the current situation, forecasts indicated that hydrated ethanol consumption could become much higher than anhydrous consumption in Brazil. The former is being consumed in cars moved exclusively by ethanol and flex-fuel cars, successfully introduced in the country at 2003. Flex cars allow Brazilian consumers to choose between gasoline and hydrated ethanol and immediately switch to whichever fuel presents the most favourable relative price.

Opportunistic and partisan election cycles in Brazil: new evidence at the municipal level

This article tests the hypothesis of opportunistic and partisan cycle models using a new large data set of Brazilian municipalities over the 1989-2005 period. The results show an increase in total and current expenditures and a decrease in municipal investments, local tax revenues, and budget surplus in election years. They also show that partisan ideology exerts a relative influence on the performance of the local public accounts. These results confirm that both opportunistic and partisan cycles have occurred in the management of the budgets of Brazilian municipalities after the end of the military government.

Neurodynamics of an election

Variables influencing decision-making in real settings, as in the case of voting decisions, are uncontrollable and in many times even unknown to the experimenter. In this case, the experimenter has to study the intention to decide (vote) as close as possible in time to the moment of the real decision (election day). Here, we investigated the brain activity associated with the voting intention declared 1 week before the election day of the Brazilian Firearms Control Referendum about prohibiting the commerce of firearms. Two alliances arose in the Congress to run the campaigns for YES (for the prohibition of firearm commerce) and NO (against the prohibition of firearm commerce) voting. Time constraints imposed by the necessity of studying a reasonable number (here, 32) of voters during a very short time (5 days) made the EEG the tool of choice for recording the brain activity associated with voting decision. Recent fMRI and EEG studies have shown decision-making as a process due to the enrollment of defined neuronal networks. In this work, a special EEG technique is applied to study the topology of the voting decision-making networks and is compared to the results of standard ERP procedures. The results show that voting decision-making enrolled networks in charge of calculating the benefits and risks of the decision of prohibiting or allowing firearm commerce and that the topology of such networks was vote-(i.e., YES/NO-) sensitive. (C) 2010 Elsevier B.V. All rights reserved.

Estimativas de insegurança alimentar grave nos municípios Brasileiros

Prevalence of severe food insecurity was estimated for Brazilian municipalities based on the 2004 National Household Sample Survey (PNAD). First, a logistic regression model was developed and tested with this database. The model was then applied to the 2000 census data, generating severe food insecurity estimates for the Brazilian municipalities, which were subsequently analyzed according to the proportion of families exposed to severe food insecurity. Severe food insecurity was mainly concentrated in the North and Northeast regions, where 46.1% and 65.3% of municipalities showed high prevalence of severe food insecurity, respectively. Most municipalities in the Central West region showed intermediate prevalence of severe food insecurity. There was wide intra-regional variation in severe food insecurity, while the South of Brazil showed the most uniform distribution. In conclusion, Brazil displays wide inter and intra-regional variations in the occurrence of severe food insecurity. Such variations should be identified and analyzed in order to plan appropriate public policies.

Estimativas de insegurança alimentar grave nos municípios brasileiros

Estimou-se a prevalência de insegurança alimentar grave para os municípios brasileiros, com base na Pesquisa Nacional por Amostra de Domicílios (PNAD) 2004. Inicialmente, foi gerado e testado um modelo por regressão logística multivariada com base nesse banco de dados. O modelo foi aplicado à amostra do Censo Demográfico de 2000, gerando estimativas de prevalências de insegurança alimentar grave para os municípios brasileiros, que foram analisadas de acordo com o percentual de famílias em condição de insegurança alimentar grave. Essa insegurança alimentar está mais concentrada nas regiões Norte e Nordeste, onde 46,1 por cento e 65,3 por cento dos municípios, respectivamente, apresentam altas prevalências de insegurança alimentar grave. Predominam nas regiões Sudeste e Sul municípios com baixa exposição à insegurança alimentar grave. No Centro-oeste a maior parte dos municípios mostra estimativas de insegurança alimentar grave classificadas como médias. Verificou-se grande variação intra-regional na ocorrência da insegurança alimentar, sendo a Região Sul a mais uniforme. Conclui-se que o Brasil apresenta grandes variações inter e intra-regionais na ocorrência da insegurança alimentar, sendo importante realçá-las e analisá-las, no intuito de subsidiar políticas públicas

System Identification and Prediction of Dengue Fever Incidence in Rio de Janeiro

Identification, prediction, and control of a system are engineering subjects, regardless of the nature of the system. Here, the temporal evolution of the number of individuals with dengue fever weekly recorded in the city of Rio de Janeiro, Brazil, during 2007, is used to identify SIS (susceptible-infective-susceptible) and SIR (susceptible-infective-removed) models formulated in terms of cellular automaton (CA). In the identification process, a genetic algorithm (GA) is utilized to find the probabilities of the state transition S -> I able of reproducing in the CA lattice the historical series of 2007. These probabilities depend on the number of infective neighbors. Time-varying and non-time-varying probabilities, three different sizes of lattices, and two kinds of coupling topology among the cells are taken into consideration. Then, these epidemiological models built by combining CA and GA are employed for predicting the cases of sick persons in 2008. Such models can be useful for forecasting and controlling the spreading of this infectious disease.

Methodology to forecast the harvest date of banana bunches

Introduction. This method is used to forecast the harvest date of banana bunches from as early as the plant shooting stage. It facilitates the harvest of bunches with the same physiological age. The principle, key advantages, time required and expected results are presented. Materials and methods. Details of the four steps of the method ( installation of the temperature sensor, tagging bunches at the flowering stage, temperature sum calculation and estimation of bunch harvest date) are described. Possible problems are discussed. Results. The application of the method allows drawing a curve of the temperature sum accumulated by the bunches which have to be harvested at exactly 900 degree-days physiological age.

Definindo a arena política local: sistemas partidários municipais na federação brasileira

This article analyzes the Brazilian political system from the local perspective. Following Cox (1997), we review the problems with electoral coordination that emerge from a given institutional framework. Due to the characteristics of the Brazilian Federal system and its electoral rules, linkage between the three levels of government is not guaranteed a priori, but demands a coordinating effort by the parties' leadership. According to our hypothesis, the parties are capable of coordinating their election strategies at different levels in the party system. Regression models based on two-stage least squares (2SLS) and TOBIT, analyzing a panel of Brazilian municipalities with data from the 1994 and 2000 elections, show that the proportion of votes received by a party in a given election correlates closely with its previous votes in majoritarian elections. Despite institutional incentives, the Brazilian party system shows evidence that it is organized nationally to the extent that it links the competition for votes at the three levels of government (National, State, and Municipal).

THE IMPORTANCE OF DISAGREEING: CONTRARIANS AND EXTREMISM IN THE CODA MODEL

In this paper, we study the effects of introducing contrarians in a model of Opinion Dynamics where the agents have internal continuous opinions, but exchange information only about a binary choice that is a function of their continuous opinion, the CODA model. We observe that the hung election scenario that arises when contrarians are introduced in discrete opinion models still happens. However, it is weaker and it should not be expected in every election. Finally, we also show that the introduction of contrarians make the tendency towards extremism of the original model weaker, indicating that the existence of agents that prefer to disagree might be an important aspect and help society to diminish extremist opinions.

Integrating technology roadmapping and portfolio management at the front-end of new product development

Many authors point out that the front-end of new product development (NPD) is a critical success factor in the NPD process and that numerous companies face difficulties in carrying it out appropriately. Therefore, it is important to develop new theories and proposals that support the effective implementation of this earliest phase of NPD. This paper presents a new method to support the development of front-end activities based on integrating technology roadmapping (TRM) and project portfolio management (PPM). This new method, called the ITP Method, was implemented at a small Brazilian high-tech company in the nanotechnology industry to explore the integration proposal. The case study demonstrated that the ITP Method provides a systematic procedure for the fuzzy front-end and integrates innovation perspectives into a single roadmap, which allows for a better alignment of business efforts and communication of product innovation goals. Furthermore, the results indicated that the method may also improve quality, functional integration and strategy alignment. (C) 2010 Elsevier Inc. All rights reserved.

Model to predict the chloride penetration in concrete with profile forming peak: verification of its precision

Chloride attack in marine environments or in structures where deicing salts are used will not always show profiles with concentrations that decrease from the external surface to the interior of the concrete. Some profiles show an increase in chloride concentrations from when a peak is formed. This type of profile must be analyzed in a different way from the traditional model of Fick`s second law to generate more precise service life models. A model for forecasting the penetration of chloride ions as a function of time for profiles having formed a peak. To confirm the efficiency of this model, it is necessary to observe the behavior of a chloride profile with peak in a specific structure over a period of time. To achieve this, two chloride profiles with different ages (22 and 27 years) were extracted from the same structure. The profile obtained from the 22-year sample was used to estimate the chloride profile at 27 years using three models: a) the traditional model using Fick`s second law and extrapolating the value of C(S)-external surface chloride concentration; b) the traditional model using Fick`s second law and shifting the x-axis to the peak depth; c) the previously proposed model. The results from these models were compared with the actual profile measured in the 27-year sample and the results were analyzed. The model was presented with good precision for this study of case, requiring to be tested with other structures in use.