913 resultados para risk-based modeling


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In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where each scenario represents a plausible realization of the uncertain parameters with an associated occurrence probability. Also, an appropriate risk measurement is considered. The proposed approach is applied on a realistic case study, based on a wind farm in Portugal. Finally, conclusions are duly drawn. (C) 2011 Elsevier Ltd. All rights reserved.

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OBJECTIVE: To propose a mathematical method for the estimation of the Basic Reproduction Number, R0, of urban yellow fever in a dengue-infested area. METHODS: The method is based on the assumption that, as the same vector (Aedes aegypti) causes both infections, all the quantities related to the mosquito, estimated from the initial phase of dengue epidemic, could be applied to yellow fever dynamics. It is demonstrated that R0 for yellow fever is, on average, 43% lower than that for dengue. This difference is due to the longer dengue viremia and its shorter extrinsic incubation period. RESULTS: In this study the analysis was expanded to the epidemiological situation of dengue in São Paulo in the year 2001. The total number of dengue cases increased from 3,582 in 2000 to 51,348 in 2001. It was then calculated R0 for yellow fever for every city which have shown R0 of dengue greater than 1. It was also estimated the total number of unprotected people living in highly risky areas for urban yellow fever. CONCLUSIONS: Currently there is a great number of non-vaccinated people living in Aedes aegypti infested area in the state of São Paulo.

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A biosensor for urea has been developed based on the observation that urea is a powerful active-site inhibitor of amidase, which catalyzes the hydrolysis of amides such as acetamide to produce ammonia and the corresponding organic acid. Cell-free extract from Pseudomonas aeruginosa was the source of amidase (acylamide hydrolase, EC 3.5.1.4) which was immobilized on a polyethersulfone membrane in the presence of glutaraldehyde; anion-selective electrode for ammonium ions was used for biosensor development. Analysis of variance was used for optimization of the biosensorresponse and showed that 30 mu L of cell-free extract containing 7.47 mg protein mL(-1), 2 mu L of glutaraldehyde (5%, v/v) and 10 mu L of gelatin (15%, w/v) exhibited the highest response. Optimization of other parameters showed that pH 7.2 and 30 min incubation time were optimum for incubation ofmembranes in urea. The biosensor exhibited a linear response in the range of 4.0-10.0 mu M urea, a detection limit of 2.0 mu M for urea, a response timeof 20 s, a sensitivity of 58.245 % per mu M urea and a storage stability of over 4 months. It was successfully used for quantification of urea in samples such as wine and milk; recovery experiments were carried out which revealed an average substrate recovery of 94.9%. The urea analogs hydroxyurea, methylurea and thiourea inhibited amidase activity by about 90%, 10% and 0%, respectively, compared with urea inhibition.

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Tuberculosis (TB) is a worldwide infectious disease that has shown over time extremely high mortality levels. The urgent need to develop new antitubercular drugs is due to the increasing rate of appearance of multi-drug resistant strains to the commonly used drugs, and the longer durations of therapy and recovery, particularly in immuno-compromised patients. The major goal of the present study is the exploration of data from different families of compounds through the use of a variety of machine learning techniques so that robust QSAR-based models can be developed to further guide in the quest for new potent anti-TB compounds. Eight QSAR models were built using various types of descriptors (from ADRIANA.Code and Dragon software) with two publicly available structurally diverse data sets, including recent data deposited in PubChem. QSAR methodologies used Random Forests and Associative Neural Networks. Predictions for the external evaluation sets obtained accuracies in the range of 0.76-0.88 (for active/inactive classifications) and Q(2)=0.66-0.89 for regressions. Models developed in this study can be used to estimate the anti-TB activity of drug candidates at early stages of drug development (C) 2011 Elsevier B.V. All rights reserved.

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The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

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This paper presents a Multi-Agent Market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agents reactions.

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This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

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OBJECTIVE: To evaluate fluoride and aluminum concentration in herbal, black, ready-to-drink, and imported teas available in Brazil considering the risks fluoride and aluminum pose to oral and general health, respectively. METHODS: One-hundred and seventy-seven samples of herbal and black tea, 11 types of imported tea and 21 samples of ready-to-drink tea were divided into four groups: I-herbal tea; II-Brazilian black tea (Camellia sinensis); III-imported tea (Camellia sinensis); IV-ready-to-drink tea-based beverages. Fluoride and aluminum were analyzed using ion-selective electrode and atomic absorption, respectively. RESULTS: Fluoride and aluminum levels in herbal teas were very low, but high amounts were found in black and ready-to-drink teas. Aluminum found in all samples analyzed can be considered safe to general health. However, considering 0.07 mg F/kg/day as the upper limit of fluoride intake with regard to undesirable dental fluorosis, some teas exceed the daily intake limit for children. CONCLUSIONS: Brazilian and imported teas made from Camellia sinensis as well as some tea-based beverages are sources of significant amounts of fluoride, and their intake may increase the risk of developing dental fluorosis.

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Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

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OBJECTIVE: Contribution to the discussion of the role of participation/consent of employees in working hours regulation. METHODS: Exploratory analysis of conflicts between preferences of employees and ergonomic recommendations in shift scheduling by analysing a large number of participative shift scheduling projects. RESULTS: The analysis showed that very often the pursuit of higher income played the major role in the decision making process of employees and employees preferred working hours in conflict with health and safety principles. CONCLUSIONS: First, the consent of employees or the works council alone does not ensure ergonomically sound schedules. Besides consent, risk assessment procedures seem to be a promising but difficult approach. Secondly, more research is necessary to check the applicability of recommendations under various settings, to support the risk assessment processes and to improve regulatory approaches to working hours.

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This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

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Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Qualidade e Tecnologias da Saúde.

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Mestrado em Engenharia Informática

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OBJECTIVE: To estimate the prevalence of alcohol abuse/dependence and identify associated factors among demographic, family, socioeconomic and mental health variables. METHODS: A household survey was carried out in the urban area of Campinas, southeastern Brazil, in 2003. A total of 515 subjects, aged 14 years or more were randomly selected using a stratified cluster sample. The Self-Report Questionnaire and the Alcohol Use Disorder Identification Test were used in the interview. Prevalences were calculated, and univariate and multivariate logistic analyses performed by estimating odds ratios and 95% confidence intervals. RESULTS: The estimated prevalence of alcohol abuse/dependence was 13.1% (95% CI: 8.4;19.9) in men and 4.1% (95% CI: 1.9;8.6) in women. In the final multiple logistic regression model, alcohol abuse/dependence was significantly associated with age, income, schooling, religion and illicit drug use. The adjusted odds ratios were significantly higher in following variables: income between 2,501 and 10,000 dollars (OR=10.29); income above 10,000 dollars (OR=10.20); less than 12 years of schooling (OR=13.42); no religion (OR=9.16) or religion other than Evangelical (OR=4.77); and illicit drug use during lifetime (OR=4.47). Alcohol abuse and dependence patterns were different according to age group. CONCLUSIONS: There is a significantly high prevalence of alcohol abuse/dependence in this population. The knowledge of factors associated with alcohol abuse, and differences in consumption patterns should be taken into account in the development of harm reduction strategies.

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OBJECTIVE: To analyze the association between dietary patterns and oral cancer. METHODS: The study, part of a Latin American multicenter hospital-based case-control study, was conducted in São Paulo, Southeastern Brazil, between November 1998 and March 2002 and included 366 incident cases of oral cancer and 469 controls, frequency-matched with cases by sex and age. Dietary data were collected using a food frequency questionnaire. The risk associated with the intake of food groups defined a posteriori, through factor analysis (called factors), was assessed. The first factor, labeled "prudent," was characterized by the intake of vegetables, fruit, cheese, and poultry. The second factor, "traditional," consisted of the intake of rice, pasta, pulses, and meat. The third factor, "snacks," was characterized as the intake of bread, butter, salami, cheese, cakes, and desserts. The fourth, "monotonous," was inversely associated with the intake of fruit, vegetables and most other food items. Factor scores for each component retained were calculated for cases and controls. After categorization of factor scores into tertiles according to the distribution of controls, odds ratios and 95% confidence intervals were calculated using unconditional multiple logistic regression. RESULTS: "Traditional" factor showed an inverse association with cancer (OR=0.51; 95% CI: 0.32; 0.81, p-value for trend 0.14), whereas "monotonous" was positively associated with the outcome (OR=1.78; 95% CI: 1.78; 2.85, p-value for trend <0.001). CONCLUSIONS: The study data suggest that the traditional Brazilian diet, consisting of rice and beans plus moderate amounts of meat, may confer protection against oral cancer, independently of any other risk factors such as alcohol intake and smoking.