Fuzzy modeling of broiler performance, raised from 1 to 21 days, subject to heat stress

ABSTRACT Given the need to obtain systems to better control broiler production environment, we performed an experiment with broilers from 1 to 21 days, which were submitted to different intensities and air temperature durations in conditioned wind tunnels and the results were used for validation of afuzzy model. The model was developed using as input variables: duration of heat stress (days), dry bulb air temperature (°C) and as output variable: feed intake (g) weight gain (g) and feed conversion (g.g-1). The inference method used was Mamdani, 20 rules have been prepared and the defuzzification technique used was the Center of Gravity. A satisfactory efficiency in determining productive responses is evidenced in the results obtained in the model simulation, when compared with the experimental data, where R2 values ​​calculated for feed intake, weight gain and feed conversion were 0.998, 0.981 and 0.980, respectively.

QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br.

Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva

The objective of this work was to accomplish the simultaneous determination of some chemical elements by Energy Dispersive X-ray Fluorescence (EDXRF) Spectroscopy through multivariate calibration in several sample types. The multivariate calibration models were: Back Propagation neural network, Levemberg-Marquardt neural network and Radial Basis Function neural network, fuzzy modeling and Partial Least Squares Regression. The samples were soil standards, plant standards, and mixtures of lead and sulfur salts diluted in silica. The smallest Root Mean Square errors (RMS) were obtained with Back Propagation neural networks, which solved main EDXRF problems in a better way.

Fuzzy logic applied to the modeling of water dynamics in an Oxisol in northeastern Brazil

Modeling of water movement in non-saturated soil usually requires a large number of parameters and variables, such as initial soil water content, saturated water content and saturated hydraulic conductivity, which can be assessed relatively easily. Dimensional flow of water in the soil is usually modeled by a nonlinear partial differential equation, known as the Richards equation. Since this equation cannot be solved analytically in certain cases, one way to approach its solution is by numerical algorithms. The success of numerical models in describing the dynamics of water in the soil is closely related to the accuracy with which the water-physical parameters are determined. That has been a big challenge in the use of numerical models because these parameters are generally difficult to determine since they present great spatial variability in the soil. Therefore, it is necessary to develop and use methods that properly incorporate the uncertainties inherent to water displacement in soils. In this paper, a model based on fuzzy logic is used as an alternative to describe water flow in the vadose zone. This fuzzy model was developed to simulate the displacement of water in a non-vegetated crop soil during the period called the emergency phase. The principle of this model consists of a Mamdani fuzzy rule-based system in which the rules are based on the moisture content of adjacent soil layers. The performances of the results modeled by the fuzzy system were evaluated by the evolution of moisture profiles over time as compared to those obtained in the field. The results obtained through use of the fuzzy model provided satisfactory reproduction of soil moisture profiles.

INFLUENCE OF MANUFACTURER SIGNATURE ON STORE BRANDS' LOYALTY AND PURCHASE INTENTION

ABSTRACT With today's trend toward higher store concentration, building strong store brands has become a priority for many retailing companies. This study aims to analyze the differences in store brands' purchasing likelihood between store brands with a manufacturer identification - a manufacturer signature - and store brands with no information about the manufacturer, as well as the moderating role of the manufacturer signature on store brands' purchase intention. We carried out multiple group analysis through structural equation modeling. Our findings suggest that store brand image has the most significant influence on loyalty and purchase intention for both types of store brands. Moreover, and contrary to our expectations, we did not find empirical support for the moderating role of manufacturer signature on store brands' purchasing likelihood.

Detection of the B"-GWGR variant in the southernmost region of Brazil: unveiling the complexity of the human immunodeficiency virus-1 subtype B epidemic

Typical human immunodeficiency virus-1 subtype B (HIV-1B) sequences present a GPGR signature at the tip of the variable region 3 (V3) loop; however, unusual motifs harbouring a GWGR signature have also been isolated. Although epidemiological studies have detected this variant in approximately 17-50% of the total infections in Brazil, the prevalence of B"-GWGR in the southernmost region of Brazil is not yet clear. This study aimed to investigate the C2-V3 molecular diversity of the HIV-1B epidemic in southernmost Brazil. HIV-1 seropositive patients were ana-lysed at two distinct time points in the state of Rio Grande do Sul (RS98 and RS08) and at one time point in the state of Santa Catarina (SC08). Phylogenetic analysis classified 46 individuals in the RS98 group as HIV-1B and their molecular signatures were as follows: 26% B"-GWGR, 54% B-GPGR and 20% other motifs. In the RS08 group, HIV-1B was present in 32 samples: 22% B"-GWGR, 59% B-GPGR and 19% other motifs. In the SC08 group, 32 HIV-1B samples were found: 28% B"-GWGR, 59% B-GPGR and 13% other motifs. No association could be established between the HIV-1B V3 signatures and exposure categories in the HIV-1B epidemic in RS. However, B-GPGR seemed to be related to heterosexual individuals in the SC08 group. Our results suggest that the established B"-GWGR epidemics in both cities have similar patterns, which is likely due to their geographical proximity and cultural relationship.

Application of fuzzy logic for the evaluation of livestock slaughtering

The fuzzy logic admits infinite intermediate logical values between false and true. With this principle, it developed in this study a system based on fuzzy rules, which indicates the body mass index of ruminant animals in order to obtain the best time to slaughter. The controller developed has as input the variables weight and height, and as output a new body mass index, called Fuzzy Body Mass Index (Fuzzy BMI), which may serve as a detection system at the time of livestock slaughtering, comparing one another by the linguistic variables "Very Low", "Low", "Average ", "High" and "Very High". For demonstrating the use application of this fuzzy system, an analysis was made with 147 Nellore beeves to determine Fuzzy BMI values for each animal and indicate the location of body mass of any herd. The performance validation of the system was based on a statistical analysis using the Pearson correlation coefficient of 0.923, representing a high positive correlation, indicating that the proposed method is appropriate. Thus, this method allows the evaluation of the herd comparing each animal within the group, thus providing a quantitative method of farmer decision. It was concluded that this study established a computational method based on fuzzy logic that mimics part of human reasoning and interprets the body mass index of any bovine species and in any region of the country.

Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory

The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease) and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA) and by fuzzy max-min compositions (fuzzy), and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

Thermodynamic evaluation and modeling of proton and water exchange associated with benzamidine and berenil binding to ß-trypsin

Serine-proteases are involved in vital processes in virtually all species. They are important targets for researchers studying the relationships between protein structure and activity, for the rational design of new pharmaceuticals. Trypsin was used as a model to assess a possible differential contribution of hydration water to the binding of two synthetic inhibitors. Thermodynamic parameters for the association of bovine ß-trypsin (homogeneous material, observed 23,294.4 ± 0.2 Da, theoretical 23,292.5 Da) with the inhibitors benzamidine and berenil at pH 8.0, 25ºC and with 25 mM CaCl2, were determined using isothermal titration calorimetry and the osmotic stress method. The association constant for berenil was about 12 times higher compared to the one for benzamidine (binding constants are K = 596,599 ± 25,057 and 49,513 ± 2,732 M-1, respectively; the number of binding sites is the same for both ligands, N = 0.99 ± 0.05). Apparently the driving force responsible for this large difference of affinity is not due to hydrophobic interactions because the variation in heat capacity (DCp), a characteristic signature of these interactions, was similar in both systems tested (-464.7 ± 23.9 and -477.1 ± 86.8 J K-1 mol-1 for berenil and benzamidine, respectively). The results also indicated that the enzyme has a net gain of about 21 water molecules regardless of the inhibitor tested. It was shown that the difference in affinity could be due to a larger number of interactions between berenil and the enzyme based on computational modeling. The data support the view that pharmaceuticals derived from benzamidine that enable hydrogen bond formation outside the catalytic binding pocket of ß-trypsin may result in more effective inhibitors.

Selection of patients for myocardial perfusion scintigraphy based on fuzzy sets theory applied to clinical-epidemiological data and treadmill test results

Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly classify the patients. Since the seminal work of Lotfi Zadeh, fuzzy logic has been applied in numerous areas. In the present study, we proposed and tested a model to select patients for MPS based on fuzzy sets theory. A group of 1053 patients was used to develop the model and another group of 1045 patients was used to test it. Receiver operating characteristic curves were used to compare the performance of the fuzzy model against expert physician opinions, and showed that the performance of the fuzzy model was equal or superior to that of the physicians. Therefore, we conclude that the fuzzy model could be a useful tool to assist the general practitioner in the selection of patients for MPS.

Modeling pulmonary fibrosis by abnormal expression of telomerase/apoptosis/collagen V in experimental usual interstitial pneumonia

Limitations on tissue proliferation capacity determined by telomerase/apoptosis balance have been implicated in pathogenesis of idiopathic pulmonary fibrosis. In addition, collagen V shows promise as an inductor of apoptosis. We evaluated the quantitative relationship between the telomerase/apoptosis index, collagen V synthesis, and epithelial/fibroblast replication in mice exposed to butylated hydroxytoluene (BHT) at high oxygen concentration. Two groups of mice were analyzed: 20 mice received BHT, and 10 control mice received corn oil. Telomerase expression, apoptosis, collagen I, III, and V fibers, and hydroxyproline were evaluated by immunohistochemistry, in situ detection of apoptosis, electron microscopy, immunofluorescence, and histomorphometry. Electron microscopy confirmed the presence of increased alveolar epithelial cells type 1 (AEC1) in apoptosis. Immunostaining showed increased nuclear expression of telomerase in AEC type 2 (AEC2) between normal and chronic scarring areas of usual interstitial pneumonia (UIP). Control lungs and normal areas from UIP lungs showed weak green birefringence of type I and III collagens in the alveolar wall and type V collagen in the basement membrane of alveolar capillaries. The increase in collagen V was greater than collagens I and III in scarring areas of UIP. A significant direct association was found between collagen V and AEC2 apoptosis. We concluded that telomerase, collagen V fiber density, and apoptosis evaluation in experimental UIP offers the potential to control reepithelization of alveolar septa and fibroblast proliferation. Strategies aimed at preventing high rates of collagen V synthesis, or local responses to high rates of cell apoptosis, may have a significant impact in pulmonary fibrosis.

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.

Detection of psychiatric morbidity in the primary medical care setting in Brazil

The aims of this study were a) to assess the ability of primary care doctors to make accurate ratings of psychiatric disturbance and b) to evaluate the use of a case-finding questionnaire in the detection of psychiatric morbidity. The estudy took place in three primary care clinics in the city of São Paulo, Brazil, during a six-month survey. A time sample of consecutive adult attenders were asked to complete a case-finding questionnaire for psychiatric disorders (the Self Report Questionnaire - SRQ) and a subsample were selected for a semi-structured psychiatric interview (the Clinical Interview Schedule - CIS). At the end of the consultation the primary care doctors were asked to assess, in a standardized way, the presence or absence of psychiatric disorder; these assessments were then compared with that ratings obtained in the psychiatric interview. A considerable proportion of minor psychiatric morbidity remained undetected by the three primary care doctors: the hidden morbidity ranged from 22% to 79%. When these were compared to those of the case-finding questionnaire, they were consistently lower, indicating that the use of these instruments can enhance the recognition of psychiatric disorders in primary care settings. Four strategies for adopting the questionnaire are described, and some of the clinical consequences of its use are discussed.

Metodological issues in measuring the detection of emotional disorders by primary care physicians: a review of the literature

A series of studies in the field of Epidemiological Psychiatry have been performed over the last two decades, and these have focused on the ability of primary care physicians to detect emotional disorders in the patients that attend their practices. The scientific methodology utilized in these studies is the subject of this review, which contains a discussion concerning: a) interviewer awareness bias; b) accuracy of the instruments and c) medical and psychological concepts involved in defining minor emotional disorders. Suggestions for change in the methodology are made in each of the sections of the review.