Terezinha Ferreira de Oliveira

Case study: Comparing the use of nonlinear discriminating analysis and Artificial Neural Networks in the classification of three fish species: acaras (Geophagus brasiliensis), tilapias (Tilapia rendalli) and mullets (Mugil liza)

This study used the Discriminant Analysis statistical technique and Artificial Neural Networks, multilayer perceptron, in the classification of three fish species sampled in the state of Rio de Janeiro, Brazil: Geophagus brasiliensis (acaras), Tilapia rendall (tilapias) and Mugil liza (mullets). These fish were sexed when possible, weighed, measured, and had their Gonadosomatic and Hepatosomatic Indices calculated, as well as their Condition Factor. The use of an Artificial Neural Network (ANN) presented satisfactory results, even though the groups were composed of very diverse-sized animals. Without the need for non-violation assumptions and other considerations, the Artificial Neural Network was found to be the excellent alternative to classification problems of unbalanced data, such as the one presented in this study.

Fish bile as a biomarker for metal exposure.

Fish accumulate several trace elements in muscle, gills and liver, however studies also indicate that metals can be excreted through bile. Since metal contamination leads to modifications in bile composition, biliary excretion offers an alternative way to evaluate the presence of trace-elements. Bile is easier to obtain than other organs and presents a simpler matrix, making it easier for chemical pre-treatment. To verify if bile can be useful as a biomonitoring tool for metal contamination, liver and bile trace element concentrations were determined and correlated. The Artificial Neural Networks statistical technique was used to verify if liver trace-element quantification could be substituted by bile analysis. Results show that significant correlations were obtained between trace elements in bile and liver and the ANN validated the hypothesis that certain trace-elements in bile could be utilized instead of liver trace-elements. Further studies in this field are of interest to further validate this biomarker.

Metal and physico-chemical variations at a hydroelectric reservoir analyzed by Multivariate Analyses and Artificial Neural Networks: Environmental management and policy/decision-making tools

This paper compared and evaluated seasonal variations in physico-chemical parameters and metals at a hydroelectric power station reservoir by applying Multivariate Analyses and Artificial Neural Networks (ANN) statistical techniques. A Factor Analysis was used to reduce the number of variables: the first factor was composed of elements Ca, K, Mg and Na, and the second by Chemical Oxygen Demand. The ANN showed 100% correct classifications in training and validation samples. Physico-chemical analyses showed that water pH values were not statistically different between the dry and rainy seasons, while temperature, conductivity, alkalinity, ammonia and DO were higher in the dry period. TSS, hardness and COD, on the other hand, were higher during the rainy season. The statistical analyses showed that Ca, K, Mg and Na are directly connected to the Chemical Oxygen Demand, which indicates a possibility of their input into the reservoir system by domestic sewage and agricultural run-offs. These statistical applications, thus, are also relevant in cases of environmental management and policy decision-making processes, to identify which factors should be further studied and/or modified to recover degraded or contaminated water bodies.

Categorization of the trophic status of a hydroelectric power plant reservoir in the Brazilian Amazon by statistical analyses and fuzzy approaches.

The Amazon area has been increasingly suffering from anthropogenic impacts, especially due to the construction of hydroelectric power plant reservoirs. The analysis and categorization of the trophic status of these reservoirs are of interest to indicate man-made changes in the environment. In this context, the present study aimed to categorize the trophic status of a hydroelectric power plant reservoir located in the Brazilian Amazon by constructing a novel Water Quality Index (WQI) and Trophic State Index (TSI) for the reservoir using major ion concentrations and physico-chemical water parameters determined in the area and taking into account the sampling locations and the local hydrological regimes. After applying statistical analyses (factor analysis and cluster analysis) and establishing a rule base of a fuzzy system to these indicators, the results obtained by the proposed method were then compared to the generally applied Carlson and a modified Lamparelli trophic state index (TSI), specific for trophic regions. The categorization of the trophic status by the proposed fuzzy method was shown to be more reliable, since it takes into account the specificities of the study area, while the Carlson and Lamparelli TSI do not, and, thus, tend to over or underestimate the trophic status of these ecosystems. The statistical techniques proposed and applied in the present study, are, therefore, relevant in cases of environmental management and policy decision-making processes, aiding in the identification of the ecological status of water bodies. With this, it is possible to identify which factors should be further investigated and/or adjusted in order to attempt the recovery of degraded water bodies.

Evaluation of sustainability in Eastern Amazon under proambiente program.

This study evaluates the sustainability of the innovative practices of smallholders who have extended their traditional farming and backyard gardening to other production parcels, such as agroforest systems in Eastern Amazon, Northeast Pará, under the PROAMBIENTE Program at Capim River Pole. According to these smallholders, these practices have assured food supplies and yields with the inclusion into the consumer market through produce diversity obtained by agroforest arrangement and increased purchase of material goods to the system. The smallholders’ perceptions also permit the evaluation of the sustainability of their experiences through the “Amoeba” method, which consolidates economic, social, cultural, and environmental indicators.

Evaluation via multivariate techniques of scale factor variability in the rietveld method applied to quantitative phase analysis with X ray powder diffraction

The present work uses multivariate statistical analysis as a form of establishing the main sources of error in the Quantitative Phase Analysis (QPA) using the Rietveld method. The quantitative determination of crystalline phases using x ray powder diffraction is a complex measurement process whose results are influenced by several factors. Ternary mixtures of Al2O3, MgO and NiO were prepared under controlled conditions and the diffractions were obtained using the Bragg-Brentano geometric arrangement. It was possible to establish four sources of critical variations: the experimental absorption and the scale factor of NiO, which is the phase with the greatest linear absorption coefficient of the ternary mixture; the instrumental characteristics represented by mechanical errors of the goniometer and sample displacement; the other two phases (Al2O3 and MgO); and the temperature and relative humidity of the air in the laboratory. The error sources excessively impair the QPA with the Rietveld method. Therefore it becomes necessary to control them during the measurement procedure.

Metal bioaccumulation in edible target tissues of mullet (Mugil liza) from a tropical bay in Southeastern Brazil.

The present study aimed to investigate metal bioaccumulation in mullet (M. liza) from a tropical bay located in Southeastern Brazil, comparing a previously considered reference site to a known contaminated area of the bay, as well as to conduct human health risk assessments with regard to the consumption of this species. The metal concentrations were compared to the maximum residue level (MRL) in foods established by the different national and international regulatory agencies, and the Provisional Tolerable Daily Intake (PTDI) was determined and compared to reference values. Chromium (Cr), Zinc (Zn), Copper (Cu), Manganese (Mn), Nickel (Ni), Cadmium (Cd) and Lead (Pb) concentrations were determined in the gills, muscle and liver of 28 mullet by ICP-MS after acid digestion. Certain metals exceeded MRL guidelines established by different regulatory agencies, indicating human health risks associated to these metals. PTDI values, however, did not exceed corresponding metal values proposed by the World Health Organization. The metal concentrations found in the mullet samples indicate that the previously considered reference site is now showing signs of anthropogenic contamination.

Logistic regression and fuzzy logic as a classification method for feral fish sampling sites.

This study presents a classification method combining logistic regression and fuzzy logic in the determination of sampling sites for feral fish, Nile Tilapia (Tilapia rendalli). This method statistically analyzes the variable domains involved in the problem, by using a logistic regression model. This in turn generates the knowledge necessary to construct the rule base and fuzzy clusters of the fuzzy inference system (FIS) variables. The proposed hybrid method was validated using three fish stress indices; the Fulton Condition Factor (FCF) and the gonadossomatic and hepatossomatic indices (GSI and HSI, respectively), from fish sampled at 3 different locations in the Rio de Janeiro State. A multinomial logistic regression allowed for the FIS construction of the proposed method and both statistical approaches, when combined, complemented each other satisfactorily, allowing for the construction of an efficient classification method regarding feral fish sampling sites that, in turn, has great value regarding fish captures and fishery resource management.

MODELO DE EFEITOS FIXOS COM MEDIDA REPETIDA APLICADO EM EXPERIMENTOS DE MELHORAMENTO GENÉTICO DO CUPUAÇUZEIRO

This study was conducted to evaluate vegetative performance of cupuassu progenies, in the immaturity phase, through analysis of repeated measures. The experimental area was installed in the municipality of Tome Acu, Northeast of the state of Para. We employed 25 full-sib progeny of cupuassu, who had vegetative growth (height and diameter of the plant), monitored for three years. Because of the nature of longitudinal observations it was assessed primarily through Mauchly sphericity test, which type of statistical analysis should be applied. For the variable diameter, sphericity conditionwas not violated, therefore, proceeded to review the design scheme of split plot. For the height variable, the test was significant, indicating necessarily evaluate the best structure to explain the correlation of errors, being chosen the covariance structure Heterogeneous Composed Symmetry. Differences were found between the progenies only in the third year of evaluation, and the variable diameter of the plant allowed better discriminate the progenies.

Partial discharge analysis and inspection alert generation in high power transformers: a case study of an autotransformer bank at Eletrobrás-ELETRONORTE vila do conde station

This paper presents an exploratory study using statistical and IA techniques in the partial discharge database located in Vila do Conde substation, Barcarena, Para state, Brazil, ELETRONORTE property. Through ambiental and system variables analysis, it was possible to identify that the 230kV reactive power and period of day have a strong relation to partial discharge measures. With the obtained knowlegde and specialists knowlegde, a initial fuzzy system is proposed for partial discharge classification in diferents operational situations of alert, contributing to the operational status diagnosis of power transformers and amplifying the knowledge about the theme.