SMAC multi-hop mesh routing protocol using IEEE 802.15.4

This paper discusses some aspects related to Wireless Sensor Networks over the IEEE 802.15.4 standard, and proposes, for the very first time, a mesh network topology with geographic routing integrated to the open Freescale protocol (SMAC - Simple Medium Access Control). For this is proposed the SMAC routing protocol. Before this work the SMAC protocol was suitable to perform one hop communications only. However, with the developed mechanisms, it is possible to use multi-hop communication. Performance results from the implemented protocol are presented and analyzed in order to define important requirements for wireless sensor networks, such as robustness, self-healing property and low latency. (c) 2011 Elsevier Ltd. All rights reserved.

PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.

Column generation for a multitrip vehicle routing problem with time windows, driver work hours, and heterogeneous fleet

This study addresses a vehicle routing problem with time windows, accessibility restrictions on customers, and a fleet that is heterogeneous with regard to capacity and average speed. A vehicle can performmultiple routes per day, all starting and ending at a single depot, and it is assigned to a single driverwhose totalwork hours are limited.Acolumn generation algorithmis proposed.The column generation pricing subproblem requires a specific elementary shortest path problem with resource constraints algorithm to address the possibility for each vehicle performingmultiple routes per day and to address the need to set the workday’s start time within the planning horizon. A constructive heuristic and a metaheuristic based on tabu search are also developed to find good solutions.

Homens, saúde e políticas públicas: a equidade de gênero em questão

O artigo tem por objetivo estabelecer questões acerca do tema Homens, Saúde e Políticas Públicas para a viabilização do debate sobre o assunto, com base em referências teóricas e empíricas relacionadas a essas questões. Inicialmente, alguns marcos históricos de temática são apresentados para que melhor se situe o debate. Em seguida, apresenta-se panorama da agenda de gênero nas políticas públicas para se introduzir a discussão acerca da inserção dessa perspectiva no âmbito das políticas de saúde. Após essa discussão, aborda-se o questionamento sobre o fato de as políticas de saúde dos homens promoverem ou não a equidade de gênero. Nas considerações finais, aponta-se para a complexidade que envolve a elaboração, a implementação e a avaliação das políticas de saúde que visam à equidade de gênero, bem como se destaca a necessidade de a política brasileira voltada para a saúde dos homens articular-se com outras políticas para que a matriz de gênero seja transversal no campo da saúde.

The SB-index and the HSB-index: efficient indices for spatial data warehouses

Spatial data warehouses (SDWs) allow for spatial analysis together with analytical multidimensional queries over huge volumes of data. The challenge is to retrieve data related to ad hoc spatial query windows according to spatial predicates, avoiding the high cost of joining large tables. Therefore, mechanisms to provide efficient query processing over SDWs are essential. In this paper, we propose two efficient indices for SDW: the SB-index and the HSB-index. The proposed indices share the following characteristics. They enable multidimensional queries with spatial predicate for SDW and also support predefined spatial hierarchies. Furthermore, they compute the spatial predicate and transform it into a conventional one, which can be evaluated together with other conventional predicates by accessing a star-join Bitmap index. While the SB-index has a sequential data structure, the HSB-index uses a hierarchical data structure to enable spatial objects clustering and a specialized buffer-pool to decrease the number of disk accesses. The advantages of the SB-index and the HSB-index over the DBMS resources for SDW indexing (i.e. star-join computation and materialized views) were investigated through performance tests, which issued roll-up operations extended with containment and intersection range queries. The performance results showed that improvements ranged from 68% up to 99% over both the star-join computation and the materialized view. Furthermore, the proposed indices proved to be very compact, adding only less than 1% to the storage requirements. Therefore, both the SB-index and the HSB-index are excellent choices for SDW indexing. Choosing between the SB-index and the HSB-index mainly depends on the query selectivity of spatial predicates. While low query selectivity benefits the HSB-index, the SB-index provides better performance for higher query selectivity.

MOOGLE: a metamodel-based model search engine

Models are becoming increasingly important in the software development process. As a consequence, the number of models being used is increasing, and so is the need for efficient mechanisms to search them. Various existing search engines could be used for this purpose, but they lack features to properly search models, mainly because they are strongly focused on text-based search. This paper presents Moogle, a model search engine that uses metamodeling information to create richer search indexes and to allow more complex queries to be performed. The paper also presents the results of an evaluation of Moogle, which showed that the metamodel information improves the accuracy of the search.

Analytical Processing Over XML and XLink

Current commercial and academic OLAP tools do not process XML data that contains XLink. Aiming at overcoming this issue, this paper proposes an analytical system composed by LMDQL, an analytical query language. Also, the XLDM metamodel is given to model cubes of XML documents with XLink and to deal with syntactic, semantic and structural heterogeneities commonly found in XML documents. As current W3C query languages for navigating in XML documents do not support XLink, XLPath is discussed in this article to provide features for the LMDQL query processing. A prototype system enabling the analytical processing of XML documents that use XLink is also detailed. This prototype includes a driver, named sql2xquery, which performs the mapping of SQL queries into XQuery. To validate the proposed system, a case study and its performance evaluation are presented to analyze the impact of analytical processing over XML/XLink documents.

A multi-locus approach to barcoding in the Anopheles strodei subgroup (Diptera: Culicidae)

Abstract Background The ability to successfully identify and incriminate pathogen vectors is fundamental to effective pathogen control and management. This task is confounded by the existence of cryptic species complexes. Molecular markers can offer a highly effective means of species identification in such complexes and are routinely employed in the study of medical entomology. Here we evaluate a multi-locus system for the identification of potential malaria vectors in the Anopheles strodei subgroup. Methods Larvae, pupae and adult mosquitoes (n = 61) from the An. strodei subgroup were collected from 21 localities in nine Brazilian states and sequenced for the COI, ITS2 and white gene. A Bayesian phylogenetic approach was used to describe the relationships in the Strodei Subgroup and the utility of COI and ITS2 barcodes was assessed using the neighbor joining tree and “best close match” approaches. Results Bayesian phylogenetic analysis of the COI, ITS2 and white gene found support for seven clades in the An. strodei subgroup. The COI and ITS2 barcodes were individually unsuccessful at resolving and identifying some species in the Subgroup. The COI barcode failed to resolve An. albertoi and An. strodei but successfully identified approximately 92% of all species queries, while the ITS2 barcode failed to resolve An. arthuri and successfully identified approximately 60% of all species queries. A multi-locus COI-ITS2 barcode, however, resolved all species in a neighbor joining tree and successfully identified all species queries using the “best close match” approach. Conclusions Our study corroborates the existence of An. albertoi, An. CP Form and An. strodei in the An. strodei subgroup and identifies four species under An. arthuri informally named A-D herein. The use of a multi-locus barcode is proposed for species identification, which has potentially important utility for vector incrimination. Individuals previously found naturally infected with Plasmodium vivax in the southern Amazon basin and reported as An. strodei are likely to have been from An. arthuri C identified in this study.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.