Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

Brief note on the variation of constants formula for fuzzy differential equations

This note gives a theory of state transition matrices for linear systems of fuzzy differential equations. This is used to give a fuzzy version of the classical variation of constants formula. A simple example of a time-independent control system is used to illustrate the methods. While similar problems to the crisp case arise for time-dependent systems, in time-independent cases the calculations are elementary solutions of eigenvalue-eigenvector problems. In particular, for nonnegative or nonpositive matrices, the problems at each level set, can easily be solved in MATLAB to give the level sets of the fuzzy solution. (C) 2002 Elsevier Science B.V. All rights reserved.

Theory and applications of fuzzy Volterra integral equations

Formulations of fuzzy integral equations in terms of the Aumann integral do not reflect the behavior of corresponding crisp models. Consequently, they are ill-adapted to describe physical phenomena, even when vagueness and uncertainty are present. A similar situation for fuzzy ODEs has been obviated by interpretation in terms of families of differential inclusions. The paper extends this formalism to fuzzy integral equations and shows that the resulting solution sets and attainability sets are fuzzy and far better descriptions of uncertain models involving integral equations. The investigation is restricted to Volterra type equations with mildly restrictive conditions, but the methods are capable of extensive generalization to other types and more general assumptions. The results are illustrated by integral equations relating to control models with fuzzy uncertainties.

The fluorolysis assay, a highly sensitive method for measuring the cytolytic activity of T cells at very low numbers

We have developed a highly sensitive cytolysis test, the fluorolysis assay, as a simple nonradioactive and inexpensive alternative to the standard Cr-51-release assay. P815 cells were stably transfected with a plasmid expressing the enhanced green fluorescent protein (EGFP) gene. These target cells were coated with or without cognate peptide or anti-CD3 Ab and then incubated with CD8(+) T cells to allow antigen-specific or nonspecific lysis. The degree of target cell lysis was measured using flow cytometry to count the percentage of viable propidium iodide(-) EGFP(+) cells, whose numbers were standardized to a reference number of fluorochrome-linked beads. By using small numbers of target cells (200-800 per reaction) and extended incubation times (up to 2 days), the antigen-specific cytolytic activity of one to two activated CD8(+) T cells of a CTL line could be detected. The redirected fluorolysis assay also measured the activity of very few ( greater than or equal to6) primary CD8(+) T cells following polyclonal activation. Importantly, antigen-specific lysis by small numbers ( greater than or equal to 25) of primary CD8(+) T cells could be directly measured ex vivo. This exquisite sensitivity of the fluorolysis assay, which was at least 8-33-folds higher than an optimized 51 Cr-release assay, allows in vitro and ex vivo studies of immune responses that would otherwise not be possible due to low CTL numbers or frequencies. (C) 2002 Elsevier Science B.V. All rights reserved.

Impact numbers: measures of risk factor impact on the whole population from case-control and cohort studies

Objective: To describe new measures of risk from case-control and cohort studies, which are simple to understand and relate to numbers of the population at risk. Design: Theoretical development of new measures of risk. Setting: Review of literature and previously described measures. Main results: The new measures are: (1) the population impact number (PIN), the number of those in the whole population among whom one case is attributable to the exposure or risk factor (this is equivalent to the reciprocal of the population attributable risk),- (2) the case impact number (CIN) the number of people with the disease or outcome for whom one case will be attributable to the exposure or risk factor (this is equivalent to the reciprocal of the population attributable fraction); (3) the exposure impact number (EIN) the number of people with the exposure among whom one excess case is attributable to the exposure (this is equivalent to the reciprocal of the attributable risk); (4) the exposed cases impact number (ECIN) the number of exposed cases among whom one case is attributable to the exposure (this is equivalent to the reciprocal of the aetiological fraction). The impact number reflects the number of people in each population (the whole population, the cases, all those exposed, and the exposed cases) among whom one case is attributable to the particular risk factor. Conclusions: These new measures should help communicate the impact on a population, of estimates of risk derived from cohort or case-control studies.

Impact numbers in health policy decisions

Objective: To outline the major methodological issues appropriate to the use of the population impact number (PIN) and the disease impact number (DIN) in health policy decision making. Design: Review of literature and calculation of PIN and DIN statistics in different settings. Setting: Previously proposed extensions to the number needed to treat (NNT): the DIN and the PIN, which give a population perspective to this measure. Main results: The PIN and DIN allow us to compare the population impact of different interventions either within the same disease or in different diseases or conditions. The primary studies used for relative risk estimates should have outcomes, time periods and comparison groups that are congruent and relevant to the local setting. These need to be combined with local data on disease rates and population size. Depending on the particular problem, the target may be disease incidence or prevalence and the effects of interest may be either the incremental impact or the total impact of each intervention. For practical application, it will be important to use sensitivity analyses to determine plausible intervals for the impact numbers. Conclusions: Attention to various methodological issues will permit the DIN and PIN to be used to assist health policy makers assign a population perspective to measures of risk.

Nutritional effects on neuron numbers

Undemutrition during early life is known to cause deficits and distortions of brain structure although it has remained uncertain whether or not this includes a diminution of the total numbers of neurons. Estimates of numerical density (e.g. number of cells per microscopic field, or number of cells per unit area of section, or number of cells per unit volume of tissue) are extremely difficult to interpret and do not provide estimates of total numbers of cells. However, advances in stereological techniques have made it possible to obtain unbiased estimates of total numbers of cells in well defined biological structures. These methods have been utilised in studies to determine the effects of varying periods of undernutrition during early life on the numbers of neurons in various regions of the rat brain. The regions examined so far have included the cerebellum, the dentate gyrus, the olfactory bulbs and the cerebral cortex. The only region to show, unequivocally, that a period of undernutrition during early life causes a deficit in the number of neurons was the dentate gyrus. These findings are discussed in the context of other morphological and functional deficits present in undernourished animals.

Utilização da lógica fuzzy na geração de zonas de manejo

Este trabalho teve como objetivo utilizar a lógica fuzzy para geração de zonas de manejo, na área agrária e ambiental. Uma das aplicações consistiu da utilização do método fuzzy C-means, para geração de zonas de manejo para a cultura do mamoeiro, em um plantio comercial localizado em São Mateus-ES, com base em determinações realizadas através de amostragens e análises químicas do solo, considerando os atributos: P, K, Ca, Mg, e Saturação por bases (V%). Aplicou-se também a lógica fuzzy para desenvolver e executar um procedimento para dar suporte ao processo de tomada de decisões, envolvendo análise multicritério, gerando mapas de adequabilidade ao uso público e a conservação no Parque Estadual da Cachoeira da Fumaça, no município de Alegre-ES, considerando como fatores a localização da cachoeira, o uso do solo, os recursos hídricos, as trilhas, os locais de acessos, a infraestrutura, a declividade da área, e utilizando a abordagem de Sistema de Informações Geográficas para análise e combinação da base de dados. A partir das zonas de manejo geradas, foi possível explicar a variabilidade espacial dos atributos do solo na área de estudo da cultura do mamoeiro, e observa-se que as similaridades entre as zonas geradas, a partir de diferentes atributos, mostrou variação, mas observa-se uma influência nos dados, principalmente pelos atributos P e V. A partir do zoneamento da Unidade de Conservação foi possível selecionar áreas mais aptas ao ecoturismo, sendo encontradas próximas da cachoeira, trilhas em zonas de reflorestamento e de Mata Atlântica. Quanto às áreas propensas a medidas de conservação localizam-se próximas à cachoeira e às estruturas do parque, devido à maior pressão antrópica exercida nesses locais. Outras áreas que se destacaram, foram as áreas de pastagem, por estarem em estágio de regeneração natural. Os resultados indicam áreas de mesmo potencial de produção do mamoeiro, ou quando aplicado à área ambiental, áreas que devem receber maior cuidado para utilização por ecoturismo e para preservação e servem de base para a tomada de decisões, visando melhor aproveitamento da área.

Um Modelo Fuzzy Comportamental para análise de sobre-reação e sub-reação no mercado de ações brasileiro

Neste artigo, são apresentados testes empíricos para a investigação de ocorrência de fenômenos de sobre-reação e sub-reação no mercado de ações brasileiro. Para esses testes, é proposto um modelo baseado na teoria de conjuntos Fuzzy, que possui forte relação com as heurísticas de representatividade e ancoramento, estabelecidas na teoria de finanças comportamentais. O modelo proposto é empregado para a formação de carteiras e utiliza indicadores financeiros de companhias abertas. Para as análises são utilizados dois conjuntos de ações, um do setor de petróleo e petroquímica e outro do setor têxtil, com indicadores financeiros relativos ao período de 1994 a 2005.

UM MODELO FUZZY-DEA-GAME PARA ESTRATÉGIAS DE PRODUÇÃO SOB INCERTEZA

Este trabalho desenvolve um novo modelo Fuzzy-DEA-Game (FDG) para apoiar o estabelecimento de estratégias de produção. Esse modelo combina a Análise Envoltória de Dados (DEA) com conceitos da Teoria dos Conjuntos Fuzzy e do Jogo da Barganha de Nash. O modelo permite uma avaliação da eficiência produtiva e econômica dos produtos, o que pode resultar num portfólio de produtos mais rentáveis e de interesse do mercado consumidor. O modelo foi aplicado em uma empresa do segmento de energia. Os resultados obtidos com a aplicação do modelo FDG mostraram-se aderentes à realidade da empresa estudada e forneceram metas para a redução dos níveis de recursos (entradas) necessários para a fabricação dos produtos e para aumento dos níveis de resultados (saídas) oriundos da comercialização desses produtos. Como resultado adicional importante, o modelo FDG permitiu a identificação dos produtos do portfólio que são mais sensíveis à ocorrência de incerteza.

Fuzzy linguistic model for evaluating the risk of neonatal death

OBJECTIVE: To introduce a fuzzy linguistic model for evaluating the risk of neonatal death. METHODS: The study is based on the fuzziness of the variables newborn birth weight and gestational age at delivery. The inference used was Mamdani's method. Neonatologists were interviewed to estimate the risk of neonatal death under certain conditions and to allow comparing their opinions and the model values. RESULTS: The results were compared with experts' opinions and the Fuzzy model was able to capture the expert knowledge with a strong correlation (r=0.96). CONCLUSIONS: The linguistic model was able to estimate the risk of neonatal death when compared to experts' performance.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Investment optimization in distribution network based on fuzzy outage parameters

This paper presents a methodology that aims to increase the probability of delivering power to any load point of the electrical distribution system by identifying new investments in distribution components. The methodology is based on statistical failure and repair data of the distribution power system components and it uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A mixed integer non-linear optimization technique is developed to identify adequate investments in distribution networks components that allow increasing the availability level for any customer in the distribution system at minimum cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a real distribution network.