DC Fuzzy Multicriteria Approach to Increase the Probability of Delivering Power in Distribution Networks

This paper proposes a methodology to increase the probability of delivering power to any load point through the identification of new investments. The methodology uses a fuzzy set approach to model the uncertainty of outage parameters, load and generation. A DC fuzzy multicriteria optimization model considering the Pareto front and based on mixed integer non-linear optimization programming is developed in order to identify the adequate investments in distribution networks components which allow increasing the probability of delivering power to all customers in the distribution network at the minimum possible cost for the system operator, while minimizing the non supplied energy cost. To illustrate the application of the proposed methodology, the paper includes a case study which considers an 33 bus distribution network.

A study on parallel versus sequential relational fuzzy clustering methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

DETECTION OF HTLV-I ANTIBODIES AND DNA IN BLOOD SAMPLE OF A PATIENT WITH MYELOPATHY IN NIGERIA

We describe a case of human T-lymphotropic virus type I associated myelopathy in a 50-year old woman in Nigeria. The patient presented with progressive loss of tone to the two lower limbs and later inability to walk. The HTLV-I antibody presence in the plasma collected from the patient was repeatedly detected by enzyme immunoassays (Abbott HTLV-I EIA and Coulter SELECT-HTLV I/II) and confirmed by Western blot technique. In addition, HTLV-I DNA was amplified from the genomic DNA isolated from the peripheral blood mononuclear cells of the patient by the polymerase chain reaction technique. This finding is significant being the first report of association of HTLV-I with myelopathy in Nigeria.

PREVALENCE OF ANTI- Toxocara ANTIBODIES IN A RANDOM SAMPLE OF INPATIENTS AT A CHILDREN'S HOSPITAL IN VITÓRIA, ESPÍRITO SANTO, BRAZIL

In the streets of Vitória, in the State of Espírito Santo, Brazil, are large number of stray dogs, many of which are infected with Toxocara canis, suggesting a high risk for human infection. In order to investigate the prevalence of Toxocara infection in children in Espírito Santo we studied the prevalence of anti-Toxocara antibodies in 100 random inpatients over one year of age, at the Children's Hospital N.S. da Glória, the reference children's hospital for the State.All the sera were collected during the period between October 1996 and January 1997. The mean age was 6.6±4.1 yrs. (1 to 14 yrs., median 6yrs.) and there were patients from all of the different wards of the hospital. Sixty-eigth patients came from the metropolitan area of Vitória and the other 32 from 17 other municipalities. The anti-Toxocara antibodies were investigated by ELISA-IgG using a secretory-excretory antigen obtained from second stage larvae. All sera were adsorbed with Ascaris suum antigen before the test. Thirty-nine sera (39%) were positive, predominantly from boys, but the gender difference was not statistically significant (boys:25/56 or 44.6%; girls:14/44 or 31.8%; p=0.311). The prevalence of positive sera was higher, but not statistically significant, in children from the urban periphery of metropolitan Vitória (formed by the cities of Vitória, Cariacica, Vila Velha, Serra and Viana) than in children from 17 other municipalities (44.1% and 28.1% respectively, p=0.190). Although the samples studied do not represent all children living in the State of Espírito Santo, since the Children's Hospital N.S. da Glória admits only patients from the state health system, it is probable that these results indicate a high frequency of Toxocara infection in children living in Espírito Santo. Further studies of population samples are necessary to ascertain the prevalence of Toxocara infection in our country.

Development of new methodologies in sample treatment for proteomics workflow based on enzymatic probe sonication technology and mass spectrometry

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia, for the degree of Doctor of Philosophy in Biochemistry

The effect of method, standard and sample components on the total antioxidant capacity of commercial waters assessed by optical conventional assays

The total antioxidant capacity (TAC) of 28 flavoured water samples was assessed by ferric reducing antioxidant potential (FRAP), oxygen radical absorbance capacity (ORAC), trolox equivalent antioxidant capacity (TEAC) and total reactive antioxidant potential (TRAP) methods. It was observed that flavoured waters had higher antioxidant activity than the corresponding natural ones. The observed differences were attributed to flavours, juice and vitamins. Generally, higher TAC contents were obtained on lemon waters and lower values on guava and raspberry flavoured waters. Lower and higher TACs were obtained by TRAP and ORAC method, respectively. Statistical analysis suggested that vitamins and flavours increased the antioxidant content of the commercial waters.

Multidimensional Scaling Visualization using Parametric Similarity Indices

In this paper, we apply multidimensional scaling (MDS) and parametric similarity indices (PSI) in the analysis of complex systems (CS). Each CS is viewed as a dynamical system, exhibiting an output time-series to be interpreted as a manifestation of its behavior. We start by adopting a sliding window to sample the original data into several consecutive time periods. Second, we define a given PSI for tracking pieces of data. We then compare the windows for different values of the parameter, and we generate the corresponding MDS maps of ‘points’. Third, we use Procrustes analysis to linearly transform the MDS charts for maximum superposition and to build a global MDS map of “shapes”. This final plot captures the time evolution of the phenomena and is sensitive to the PSI adopted. The generalized correlation, the Minkowski distance and four entropy-based indices are tested. The proposed approach is applied to the Dow Jones Industrial Average stock market index and the Europe Brent Spot Price FOB time-series.

Integer and Fractional Order Entropy Analysis of Earthquake Data-series

This paper studies the statistical distributions of worldwide earthquakes from year 1963 up to year 2012. A Cartesian grid, dividing Earth into geographic regions, is considered. Entropy and the Jensen–Shannon divergence are used to analyze and compare real-world data. Hierarchical clustering and multi-dimensional scaling techniques are adopted for data visualization. Entropy-based indices have the advantage of leading to a single parameter expressing the relationships between the seismic data. Classical and generalized (fractional) entropy and Jensen–Shannon divergence are tested. The generalized measures lead to a clear identification of patterns embedded in the data and contribute to better understand earthquake distributions.

Entropy Analysis of Industrial Accident Data Series

Complex industrial plants exhibit multiple interactions among smaller parts and with human operators. Failure in one part can propagate across subsystem boundaries causing a serious disaster. This paper analyzes the industrial accident data series in the perspective of dynamical systems. First, we process real world data and show that the statistics of the number of fatalities reveal features that are well described by power law (PL) distributions. For early years, the data reveal double PL behavior, while, for more recent time periods, a single PL fits better into the experimental data. Second, we analyze the entropy of the data series statistics over time. Third, we use the Kullback–Leibler divergence to compare the empirical data and multidimensional scaling (MDS) techniques for data analysis and visualization. Entropy-based analysis is adopted to assess complexity, having the advantage of yielding a single parameter to express relationships between the data. The classical and the generalized (fractional) entropy and Kullback–Leibler divergence are used. The generalized measures allow a clear identification of patterns embedded in the data.

QuEChERS: a sample preparation for extraction of carbaryl from rat feces

Carbamate compounds are an important group of cholinesterase inhibitors. There is a need for creating awareness regarding the risks of the inadequate carbamate use in the residential areas due to potential adverse human effects. Carbaryl is a commonly used pesticide worldwide. A simple, fast, and high throughput method was developed employing liquid chromatography with fluorescence detector to determine carbaryl residues in rat feces. The extraction was performed by using a rapid, easy, cheap, effective, reliable, and safe (QuEChERS) method, using acetonitrile as the extracting solvent. The parameters for the performance of the extraction method were optimized, such as ratio of mass of sample per volume of extraction solvent, QuEChERS content, and cleanup columns. Linear response was obtained for all calibration curves (solven and matrix-matched) over the established concentration range (5 500 mg/L) with a correlation coefficients higher than 0.999. The achieved recovery was 97.9% with relative standard deviation values of 1.1% (n D 4) at 167 mg/kg fortified concentration level and the limits of detection and quantification were 27.7 and 92.3 mg/kg respectively.

Adaptive Penalty and Barrier function based on Fuzzy Logic

Optimization methods have been used in many areas of knowledge, such as Engineering, Statistics, Chemistry, among others, to solve optimization problems. In many cases it is not possible to use derivative methods, due to the characteristics of the problem to be solved and/or its constraints, for example if the involved functions are non-smooth and/or their derivatives are not know. To solve this type of problems a Java based API has been implemented, which includes only derivative-free optimization methods, and that can be used to solve both constrained and unconstrained problems. For solving constrained problems, the classic Penalty and Barrier functions were included in the API. In this paper a new approach to Penalty and Barrier functions, based on Fuzzy Logic, is proposed. Two penalty functions, that impose a progressive penalization to solutions that violate the constraints, are discussed. The implemented functions impose a low penalization when the violation of the constraints is low and a heavy penalty when the violation is high. Numerical results, obtained using twenty-eight test problems, comparing the proposed Fuzzy Logic based functions to six of the classic Penalty and Barrier functions are presented. Considering the achieved results, it can be concluded that the proposed penalty functions besides being very robust also have a very good performance.

Evaluation of Spatial interpolation techniques for mapping climate variables with low sample density: a case study using a new gridded dataset of Bangladesh

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Development of a fuzzy qualitative risk assessment model applied to construction industry

Dissertação para obtenção do Grau de Doutor em Engenharia Industrial

Recursive neuro fuzzy techniques for online identification and control

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores