To what extent are CO2 emissions from intra-urban shopping trips by cars affected by drivers’ travel behaviour and store location?

Transportation is seen as one of the major sources of CO2 pollutants nowadays. The impact of increased transport in retailing should not be underestimated. Most previous studies have focused on transportation and underlying trips, in general, while very few studies have addressed the specific affects that, for instance, intra-city shopping trips generate. Furthermore, most of the existing methods used to estimate emission are based on macro-data designed to generate national or regional inventory projections. There is a lack of studies using micro-data based methods that are able to distinguish between driver behaviour and the locational effects induced by shopping trips, which is an important precondition for energy efficient urban planning. The aim of this study is to implement a micro-data method to estimate and compare CO2 emission induced by intra-urban car travelling to a retail destination of durable goods (DG), and non-durable goods (NDG). We estimate the emissions from aspects of travel behaviour and store location. The study is conducted by means of a case study in the city of Borlänge, where GPS tracking data on intra-urban car travel is collected from 250 households. We find that a behavioural change during a trip towards a CO2 optimal travelling by car has the potential to decrease emission to 36% (DG), and to 25% (NDG) of the emissions induced by car-travelling shopping trips today. There is also a potential of reducing CO2 emissions induced by intra-urban shopping trips due to poor location by 54%, and if the consumer selected the closest of 8 existing stores, the CO2 emissions would be reduced by 37% of the current emission induced by NDG shopping trips.

Técnicas de computação natural para segmentação de imagens médicas

Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth

Transmission system expansion planning by an extended genetic algorithm

The paper presents an extended genetic algorithm for solving the optimal transmission network expansion planning problem. Two main improvements have been introduced in the genetic algorithm: (a) initial population obtained by conventional optimisation based methods; (b) mutation approach inspired in the simulated annealing technique, the proposed method is general in the sense that it does not assume any particular property of the problem being solved, such as linearity or convexity. Excellent performance is reported in the test results section of the paper for a difficult large-scale real-life problem: a substantial reduction in investment costs has been obtained with regard to previous solutions obtained via conventional optimisation methods and simulated annealing algorithms; statistical comparison procedures have been employed in benchmarking different versions of the genetic algorithm and simulated annealing methods.

Genotyping of AR and PSA polymorphisms in a patient with Klinefelter syndrome, non-Hodgkin lymphoma, and adenocarcinoma of the prostate

It has been hypothesized that the AR (androgen receptor) gene binds the two PSA (prostate-specific antigen) alleles with differing affinities and may differentially influence prostate cancer risk. In this article, we report a case of adenocarcinoma of the prostate in a 56-year-old man with Klinefelter syndrome (47,XXY) and non-Hodgkin lymphoma, as well as the AR and PSA genotype. AR and PSA gene polymorphisms were analyzed by polymerase chain reaction-based methods using DNA from peripheral white blood cells and the prostate cancer. We determined the methylation status of the AR gene on the X chromosome. The patient presents with the AG genotype for the ARE-I (androgen response element) region of the PSA gene. We detect the presence of two short AR alleles with 19 and 11CAG repeats each. Unmethylated alleles were demonstrated for both. The shorter allele was inactive in more than 60% of total DNA in both control blood and prostate cancer cells. The presence of short AR alleles and the G allele of the PSA gene may contribute to the development of prostate cancer in a 47,XXY patient. (C) 2004 Elsevier B.V. All rights reserved.

Detection of Babesia bigemina in cattle of different genetic groups and in Rhipicephalus (Boophilus) microplus tick

Babesia bigemina infections were investigated in four genetic groups of beef cattle and in Rhipicephalus (Boophilus) microplus engorged female ticks. Blood samples and engorged female ticks were collected from 15 cows and 15 calves from each of the following genetic groups: Nelore, Angus x Nelore, Canchim x Nelore, and Simmental x Nelore. Microscopic examination of blood smears and tick hemolymph revealed that merozoites of B. bigemina (6/60) as well as kinetes of Babesia spp. (9/549) were only detected in samples (blood and ticks, respectively) originated from calves. PCR-based methods using primers for specific detection of B. bigemina revealed 100% infection in both calves and cows, regardless the genetic group. Tick infection was detected by nested-PCR amplifications showing that the frequency of B. bigemina was higher (P 0.01) in female ticks collected from calves (134/549) than in those collected from cows (52/553). The frequency of B. bigemina was similar in ticks collected from animals, either cows or calves, of the four genetic groups (P > 0.05). (C) 2008 Elsevier B.V. All rights reserved.

Delimiting genetic units in Neotropical toads under incomplete lineage sorting and hybridization

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Epstein-Barr Virus (EBV) detection and typing by PCR: a contribution to diagnostic screening of EBV-positive Burkitt's lymphoma

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Application of dissociation curve analysis to radiation hybrid panel marker scoring: generation of a map of river buffalo (B-bubalis) chromosome 20

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Context-sensitive analysis of obfuscated x86 executables

A method for context-sensitive analysis of binaries that may have obfuscated procedure call and return operations is presented. Such binaries may use operators to directly manipulate stack instead of using native call and ret instructions to achieve equivalent behavior. Since definition of context-sensitivity and algorithms for context-sensitive analysis have thus far been based on the specific semantics associated to procedure call and return operations, classic interprocedural analyses cannot be used reliably for analyzing programs in which these operations cannot be discerned. A new notion of context-sensitivity is introduced that is based on the state of the stack at any instruction. While changes in 'calling'-context are associated with transfer of control, and hence can be reasoned in terms of paths in an interprocedural control flow graph (ICFG), the same is not true of changes in 'stack'-context. An abstract interpretation based framework is developed to reason about stack-contexts and to derive analogues of call-strings based methods for the context-sensitive analysis using stack-context. The method presented is used to create a context-sensitive version of Venable et al.'s algorithm for detecting obfuscated calls. Experimental results show that the context-sensitive version of the algorithm generates more precise results and is also computationally more efficient than its context-insensitive counterpart. Copyright © 2010 ACM.

Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

Alterações genômicas em tumores de glândulas salivares

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A transformada wavelet na fusão de imagens de sensoriamento remoto

Pós-graduação em Matematica Aplicada e Computacional - FCT

Fusão de métodos baseados em minúcias e em cristas para reconhecimento de impressões digitais

Pós-graduação em Ciência da Computação - IBILCE

Context-sensitive analysis of x86 obfuscated executables

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)