Previsões climáticas sazonais sobre o Brasil: avaliação do RegCM3 aninhado no modelo global CPTEC/COLA

Este trabalho avalia o desempenho de previsões sazonais do modelo climático regional RegCM3, aninhado ao modelo global CPTEC/COLA. As previsões com o RegCM3 utilizaram 60 km de resolução horizontal num domínio que inclui grande parte da América do Sul. As previsões do RegCM3 e CPTEC/COLA foram avaliadas utilizando as análises de chuva e temperatura do ar do Climate Prediction Center (CPC) e National Centers for Enviromental Prediction (NCEP), respectivamente. Entre maio de 2005 e julho de 2007, 27 previsões sazonais de chuva e temperatura do ar (exceto a temperatura do CPTEC/COLA, que possui 26 previsões) foram avaliadas em três regiões do Brasil: Nordeste (NDE), Sudeste (SDE) e Sul (SUL). As previsões do RegCM3 também foram comparadas com as climatologias das análises. De acordo com os índices estatísticos (bias, coeficiente de correlação, raiz quadrada do erro médio quadrático e coeficiente de eficiência), nas três regiões (NDE, SDE e SUL) a chuva sazonal prevista pelo RegCM3 é mais próxima da observada do que a prevista pelo CPTEC/COLA. Além disto, o RegCM3 também é melhor previsor da chuva sazonal do que da média das observações nas três regiões. Para temperatura, as previsões do RegCM3 são superiores às do CPTEC/COLA nas áreas NDE e SUL, enquanto o CPTEC/COLA é superior no SDE. Finalmente, as previsões de chuva e temperatura do RegCM3 são mais próximas das observações do que a climatologia observada. Estes resultados indicam o potencial de utilização do RegCM3 para previsão sazonal, que futuramente deverá ser explorado através de previsão por conjunto.

Um modelo estocástico combinado de previsão sazonal para a precipitação no Brasil

Este artigo discute um modelo de previsão combinada para a realização de prognósticos climáticos na escala sazonal. Nele, previsões pontuais de modelos estocásticos são agregadas para obter as melhores projeções no tempo. Utilizam-se modelos estocásticos autoregressivos integrados a médias móveis, de suavização exponencial e previsões por análise de correlações canônicas. O controle de qualidade das previsões é feito através da análise dos resíduos e da avaliação do percentual de redução da variância não-explicada da modelagem combinada em relação às previsões dos modelos individuais. Exemplos da aplicação desses conceitos em modelos desenvolvidos no Instituto Nacional de Meteorologia (INMET) mostram bons resultados e ilustram que as previsões do modelo combinado, superam na maior parte dos casos a de cada modelo componente, quando comparadas aos dados observados.

Dynamical models for computer viruses propagation

Nowadays, digital computer systems and networks are the main engineering tools, being used in planning, design, operation, and control of all sizes of building, transportation, machinery, business, and life maintaining devices. Consequently, computer viruses became one of the most important sources of uncertainty, contributing to decrease the reliability of vital activities. A lot of antivirus programs have been developed, but they are limited to detecting and removing infections, based on previous knowledge of the virus code. In spite of having good adaptation capability, these programs work just as vaccines against diseases and are not able to prevent new infections based on the network state. Here, a trial on modeling computer viruses propagation dynamics relates it to other notable events occurring in the network permitting to establish preventive policies in the network management. Data from three different viruses are collected in the Internet and two different identification techniques, autoregressive and Fourier analyses, are applied showing that it is possible to forecast the dynamics of a new virus propagation by using the data collected from other viruses that formerly infected the network. Copyright (c) 2008 J. R. C. Piqueira and F. B. Cesar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

New reconciliation model for gold industry

Reconciliation can be divided into stages, each stage representing the performance of a mining operation, such as: long-term estimation, short-term estimation, planning, mining and mineral processing. The gold industry includes another stage which is the budget, when the company informs the financial market of its annual production forecast. The division of reconciliation into stages increases the reliability of the annual budget informed by the mining companies, while also detecting and correcting the critical steps responsible for the overall estimation error by the optimization of sampling protocols and equipment. This paper develops and validates a new reconciliation model for the gold industry, which is based on correct sampling practices and the subdivision of reconciliation into stages, aiming for better grade estimates and more efficient control of the mining industry`s processes, from resource estimation to final production.

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.

Measuring the eco-efficiency of cement use

At present, the cement industry generates approximately 5% of the world`s anthropogenic CO(2) emissions. This share is expected to increase since demand for cement based products is forecast to multiply by a factor of 2.5 within the next 40 years and the traditional strategies to mitigate emissions, focused on the production of cement, will not be capable of compensating such growth. Therefore, additional mitigation strategies are needed, including an increase in the efficiency of cement use. This paper proposes indicators for measuring cement use efficiency, presents a benchmark based on literature data and discusses potential gains in efficiency. The binder intensity (bi) index measures the amount of binder (kg m(-3)) necessary to deliver 1 MPa of mechanical strength, and consequently express the efficiency of using binder materials. The CO(2) intensity index (ci) allows estimating the global warming potential of concrete formulations. Research benchmarks show that bi similar to 5 kg m(-3) MPa(-1) are feasible and have already been achieved for concretes >50 MPa. However, concretes with lower compressive strengths have binder intensities varying between 10 and 20 kg m(-3) MPa(-1). These values can be a result of the minimum cement content established in many standards and reveal a significant potential for performance gains. In addition, combinations of low bi and ci are shown to be feasible. (c) 2010 Elsevier Ltd. All rights reserved.

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

El Nino and arboviral disease prediction

Recent El Nino events have stimulated interest in the development of modeling techniques to forecast extremes of climate and related health events. Previous studies have documented associations between specific climate variables (particularly temperature and rainfall) and outbreaks of arboviral disease. In some countries, such diseases are sensitive to Fl Nino. Here we describe a climate-based model for the prediction of Ross River virus epidemics in Australia. From a literature search and data on case notifications, we determined in which years there were epidemics of Ross River virus in southern Australia between 1928 and 1998. Predictor variables were monthly Southern Oscillation index values for the year of an epidemic or lagged by 1 year. We found that in southeastern states, epidemic years were well predicted by monthly Southern Oscillation index values in January and September in the previous year. The model forecasts that there is a high probability of epidemic Ross River virus in the southern states of Australia in 1999. We conclude that epidemics of arboviral disease can, at least in principle, be predicted on the basis of climate relationships.

Forecasting regional crop production using SOI phases: an example for the Australian peanut industry

Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing bodies. The industry could profitably use forecasts of likely production to adjust their operations strategically. Significant, physically based lag-relationships exist between an index of ocean/atmosphere El Nino/Southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

The ICT use in the implementation of Directive 2011/24/EU

This article has as main objective to evaluate the role of information and communication technologies (ICTs), in particular the eHealth (electronic health), in the implementation of the directive 2011/24/EU, of the European Parliament and of the Council of March 9th, on the exercise of patients' rights in cross-border healthcare within Member States of European Union. Being currently underway the deadline for transposition of the Directive, it is important to analyze the probable results for national health systems. Innovatively, the Directive specifically proposes the implementation of a European network of eHealth in the provision of cross-border healthcare. Within ICT, we focus on telemedicine as a key tool for the implementation, on a context of public budgets constrains. In this context, it is assumed that the EU will support and promote cooperation and the exchange of scientific information between member states within the framework of a voluntary network composed by the national authorities responsible for health (or eHealth). We apply the S.W.O.T. (strengths and weaknesses, opportunities and threats) analysis to forecast the main points that should be focused on deeper research. We discuss the technological, economic and social aspects of the use of ICT on the implementation of the directive. It is thus important to evaluate the context of ICT by S.W.O.T. tool to define strategies to sensitize policy-makers, health managers, and citizens, in order to be able to turn threats into opportunities and mitigating the weaknesses in the implementation of the Directive and to promote a better healthcare access for citizens, ensuring safe, effective healthcare and with different quality.

O trabalho em equipas docentes e o desenvolvimento da Educação Física

RESUMO: O sucesso das respostas aos novos desafios, exigências e expectativas que, nos dias de hoje, recaem sobre a escola, parece depender da concepção das estratégias organizacionais que globalmente a escola define, tendo em conta as características do contexto e das populações que serve. Depende por isso do modo como os actores chave se organizam e trabalham. Os modos de entender o trabalho docente em equipa prenunciam formas de entender a acção educativa e de conduzir a aprendizagem dos alunos. Tomando como ponto de partida as nossas experiências profissionais, este trabalho constitui uma reflexão sobre as possibilidades de desenvolvimento da Educação Física, baseada na escola e sustentada na organização do trabalho dos seus professores. Elaborando uma síntese dos principais problemas do seu desenvolvimento discutem-se as possibilidades e as vantagens da organização do trabalho dos professores em equipas, num contexto organizacional marcado por avanços e retrocessos nos processos de descentralização e atribuição de autonomia às escolas. Enquadra-se a organização do trabalho docente no âmbito das culturas de escola. Revisita-se o conceito de trabalho em equipa, distinguindo-o da simples participação nas estruturas da escola, realçando a sua importância na construção e gestão de conhecimento profissional através do estabelecimento de parcerias sinérgicas, onde o desempenho de papéis de liderança tem uma enorme importância. ABSTRACT: Successful responses to the new challenges, demands and expectations that currently are ascribed to schools seem to depend on the design of organizational strategies that broadly define the school, taking into account the characteristics of its context and the population it serves. Therefore, their success depends on how the key players are organized and work. The ways of perceiving the teaching team work forecast the ways of understanding the educational activity and how to lead learning. Starting from our own professional experience, this study is a reflection on the school based development of physical education, sustained by the organization of their teachers’ work. On organizing a summary of the major problems found in the development of this subject, we discuss the feasibilities and the gains got from the organization of teachers' teamwork, in an organizational context marked by promises and setbacks in the processes of decentralization and schools autonomy. The organization of teaching is to be seen as part of the school culture. We review the concept of teamwork as distinct from mere participation in the structures of the school, and its importance in the construction and management of professional knowledge will be highlighted by the establishment of synergistic partnerships, in which the performance of leadership roles is of the greatest importance. RÉSUMÉ: Le succès des réponses aux nouveaux défis, les exigences et les attentes que dans ces jours, s’accomplissent sur l'école, semble dépendre de la conception des stratégies d'organisation qui définit de façon générale l'école, en tenant compte des caractéristiques du contexte et des populations qu'elle dessert. Tout cela dépend donc de la façon dont les acteurs principaux sont organisés et travaillent. Les façons de comprendre l'équipe pédagogique démontrent des formes de comprendre l'activité éducative et de conduire à l'apprentissage des élèves. En prenant comme point de départ nos expériences professionnelles, ce travail est une réflexion sur le potentiel de développement de l'Éducation Physique, dépendante de l'école et soutenue dans l'organisation du travail de leurs enseignants. En préparant une synthèse des principaux problèmes de leur développement, on discute les possibilités et les avantages de l'organisation du travail des enseignants dans les équipes, dans un contexte organisationnel marqué par des avancées et des reculs dans le processus de décentralisation et l'accomplissement de l'autonomie aux écoles. On encadre l'organisation du travail des enseignants dans le champ des cultures de l’école. On revoit le concept du travail en équipe, en le distinguant de la simple participation dans les structures de l'école, en soulignant son importance dans la construction et gestion des connaissances professionnelles à travers l'établissement de partenariats synergiques, où les rôles de leadership joue un rôle d'une grande importance.

Design of a sea-level tsunami detection network for the Gulf of Cadiz

The devastating impact of the Sumatra tsunami of 26 December 2004, raised the question for scientists of how to forecast a tsunami threat. In 2005, the IOC-UNESCO XXIII assembly decided to implement a global tsunami warning system to cover the regions that were not yet protected, namely the Indian Ocean, the Caribbean and the North East Atlantic, the Mediterranean and connected seas (the NEAM region). Within NEAM, the Gulf of Cadiz is the more sensitive area, with an important record of devastating historical events. The objective of this paper is to present a preliminary design for a reliable tsunami detection network for the Gulf of Cadiz, based on a network of sea-level observatories. The tsunamigenic potential of this region has been revised in order to define the active tectonic structures. Tsunami hydrodynamic modeling and GIS technology have been used to identify the appropriate locations for the minimum number of sea-level stations. Results show that 3 tsunameters are required as the minimum number of stations necessary to assure an acceptable protection to the large coastal population in the Gulf of Cadiz. In addition, 29 tide gauge stations could be necessary to fully assess the effects of a tsunami along the affected coasts of Portugal, Spain and Morocco.