Alocação eficiente de indicadores de faltas em um sistema de distribuição real usando computação evolutiva

Este trabalho propõe uma abordagem computacional evolutiva para a resolução do problema de alocação de dispositivos indicadores de faltas (IFs) em alimentadores primários de distribuição de energia elétrica. De forma mais específica, o problema de se obter o melhor local de instalação é solucionado por meio da técnica de Algoritmos Genéticos (AGs) que busca obter uma configuração eficiente de instalação de IFs no tronco principal de um alimentador de distribuição. Assim, faz-se a modelagem do mesmo na forma de um problema de otimização orientado à melhoria dos indicadores de qualidade do serviço e ao encontro de uma solução economicamente atraente. Os resultados com dados reais comprovam a eficiência da metodologia proposta.

Natural antibody response to Plasmodium falciparum merozoite antigens MSP5, MSP9 and EBA175 is associated to clinical protection in the Brazilian Amazon

BACKGROUND: Antibodies have an essential role in the acquired immune response against blood stage P. falciparum infection. Although several antigens have been identified as important antibody targets, it is still elusive which antigens have to be recognized for clinical protection. Herein, we analyzed antibodies from plasmas from symptomatic or asymptomatic individuals living in the same geographic area in the Western Amazon, measuring their recognition of multiple merozoite antigens. METHODS: Specific fragments of genes encoding merozoite proteins AMA1 and members of MSP and EBL families from circulating P. falciparum field isolates present in asymptomatic and symptomatic patients were amplified by PCR. After cloning and expression of different versions of the antigens as recombinant GST-fusion peptides, we tested the reactivity of patients' plasmas by ELISA and the presence of IgG subclasses in the most reactive plasmas. RESULTS: 11 out of 24 recombinant antigens were recognized by plasmas from either symptomatic or asymptomatic infections. Antibodies to MSP9 (X2(DF=1) = 9.26/p = 0.0047) and MSP5 (X2(DF=1) = 8.29/p = 0.0069) were more prevalent in asymptomatic individuals whereas the opposite was observed for MSP1 block 2-MAD20 (X2(DF=1) = 6.41/p = 0.0206, Fisher's exact test). Plasmas from asymptomatic individuals reacted more intensely against MSP4 (U = 210.5, p < 0.03), MSP5 (U = 212, p < 0.004), MSP9 (U = 189.5, p < 0.002) and EBA175 (U = 197, p < 0.014, Mann-Whitney's U test). IgG1 and IgG3 were predominant for all antigens, but some patients also presented with IgG2 and IgG4. The recognition of MSP5 (OR = 0.112, IC95% = 0.021-0.585) and MSP9 (OR = 0.125, IC95% = 0.030-0.529, cross tab analysis) predicted 8.9 and 8 times less chances, respectively, to present symptoms. Higher antibody levels against MSP5 and EBA175 were associated by odds ratios of 9.4 (IC95% = 1.29-69.25) and 5.7 (IC95% = 1.12-29.62, logistic regression), respectively, with an asymptomatic status. CONCLUSIONS: Merozoite antigens were targets of cytophilic antibodies and antibodies against MSP5, MSP9 and EBA175 were independently associated with decreased symptoms.

Pathogenesis of reproductive failure induced by Trypanosoma vivax in experimentally infected pregnant ewes

The present study was aimed at investigating the effect of experimental infection by Trypanosoma vivax in different stages of pregnancy, determining the pathogenesis of reproductive failure, and confirming transplacental transmission. We used 12 pregnant ewes distributed into four experimental groups: G1, was formed by three ewes infected with T. vivax in the first third of pregnancy (30 days); G2 comprised three infected ewes in the final third of pregnancy (100 days); G3 and G4 were composed of three non-infected ewes with the same gestational period, respectively. Each ewe of G1 and G2 was inoculated with 1.25 × 105 tripomastigotes. Clinical examination, determination of parasitemia, serum biochemistry (albumin, total protein, glucose, cholesterol, and urea), packed cell volume (PCV), serum progesterone, and pathological examination were performed. Placenta, amniotic fluid, blood and tissues from the fetuses and stillbirths were submitted to PCR. Two ewes of G1 (Ewe 1 and 3) presented severe infection and died in the 34th and 35th days post-infection (dpi), respectively; but both fetuses were recovered during necropsy. In G2, Ewe 5 aborted two fetuses on the 130th day (30 dpi) of pregnancy; and Ewe 6 aborted one fetus in the 140th day (40 dpi) of gestation. Ewes 2 and 4 delivered two weak lambs that died five days after birth. Factors possibly involved with the reproductive failure included high parasitemia, fever, low PCV, body score, serum glucose, total protein, cholesterol, and progesterone. Hepatitis, pericarditis, and encephalitis were observed in the aborted fetuses. The presence of T. vivax DNA in the placenta, amniotic fluid, blood, and tissues from the fetuses confirms the transplacental transmission of the parasite. Histological lesion in the fetuses and placenta also suggest the involvement of the parasite in the etiopathogenesis of reproductive failure in ewes.

Ethanol fuel analysis by time-domain reflectometry

The technique of Time Domain Reflectometry (TDR) is applied for qualifying ethanol adulterated with water and / or methanol. We used the commercial TDR model VG400, which was originally developed for determining soil moisture, making this study an original approach for qualifying fuels. Several samples of alcohol with the addition of its main contaminants (water and methanol) were prepared and measured with the TDR sensor. The results indicate good response linearity, showing the TDR technique is a promising technique for fuel qualification

Combining fractal and deterministic walkers for texture analysis and classification

In this paper,we present a novel texture analysis method based on deterministic partially self-avoiding walks and fractal dimension theory. After finding the attractors of the image (set of pixels) using deterministic partially self-avoiding walks, they are dilated in direction to the whole image by adding pixels according to their relevance. The relevance of each pixel is calculated as the shortest path between the pixel and the pixels that belongs to the attractors. The proposed texture analysis method is demonstrated to outperform popular and state-of-the-art methods (e.g. Fourier descriptors, occurrence matrix, Gabor filter and local binary patterns) as well as deterministic tourist walk method and recent fractal methods using well-known texture image datasets.

Dynamic texture segmentation based on deterministic partially self-avoiding walks

Recently there has been a considerable interest in dynamic textures due to the explosive growth of multimedia databases. In addition, dynamic texture appears in a wide range of videos, which makes it very important in applications concerning to model physical phenomena. Thus, dynamic textures have emerged as a new field of investigation that extends the static or spatial textures to the spatio-temporal domain. In this paper, we propose a novel approach for dynamic texture segmentation based on automata theory and k-means algorithm. In this approach, a feature vector is extracted for each pixel by applying deterministic partially self-avoiding walks on three orthogonal planes of the video. Then, these feature vectors are clustered by the well-known k-means algorithm. Although the k-means algorithm has shown interesting results, it only ensures its convergence to a local minimum, which affects the final result of segmentation. In order to overcome this drawback, we compare six methods of initialization of the k-means. The experimental results have demonstrated the effectiveness of our proposed approach compared to the state-of-the-art segmentation methods.

Dynamic texture analysis and segmentation using deterministic partially self-avoiding walks

Dynamic texture is a recent field of investigation that has received growing attention from computer vision community in the last years. These patterns are moving texture in which the concept of selfsimilarity for static textures is extended to the spatiotemporal domain. In this paper, we propose a novel approach for dynamic texture representation, that can be used for both texture analysis and segmentation. In this method, deterministic partially self-avoiding walks are performed in three orthogonal planes of the video in order to combine appearance and motion features. We validate our method on three applications of dynamic texture that present interesting challenges: recognition, clustering and segmentation. Experimental results on these applications indicate that the proposed method improves the dynamic texture representation compared to the state of the art.