Screening for pulmonary tuberculosis in Teresópolis, RJ, Brazil: the search for respiratory symptomatic patients in emergency service of "Hospital das Clínicas de Teresópolis Costantino Ottaviano, Fundação Educacional Serra dos Órgãos"

The aim of the present study was to investigate the detection percentage of tuberculosis among patients that are respiratory symptomatic (TB suspects). In this work, we present the preliminary results of research carried out at "Hospital das Clínicas de Teresópolis Costantino Ottaviano da Fundação Educacional Serra dos Órgãos (FESO)" from November 2003 to April 2004. Among the 40 respiratory symptomatic individuals identified and referred to the Tuberculosis Control Program in Teresópolis, two (5.0%) were characterized as smear-positive. These results confirm reports in the literature and underscore the need for and importance of this strategy.

Development of an Unmanned Capsule for large-scale maritime search and rescue

This paper describes the development and testing of a robotic capsule for search and rescue operations at sea. This capsule is able to operate autonomously or remotely controlled, is transported and deployed by a larger USV into a determined disaster area and is used to carry a life raft and inflate it close to survivors in large-scale maritime disasters. The ultimate goal of this development is to endow search and rescue teams with tools that extend their operational capability in scenarios with adverse atmospheric or maritime conditions.

Testing Nelder-Mead Based Repulsion Algorithms for Multiple Roots of Nonlinear Systems via a Two-Level Factorial Design of Experiments

This paper addresses the challenging task of computing multiple roots of a system of nonlinear equations. A repulsion algorithm that invokes the Nelder-Mead (N-M) local search method and uses a penalty-type merit function based on the error function, known as 'erf', is presented. In the N-M algorithm context, different strategies are proposed to enhance the quality of the solutions and improve the overall efficiency. The main goal of this paper is to use a two-level factorial design of experiments to analyze the statistical significance of the observed differences in selected performance criteria produced when testing different strategies in the N-M based repulsion algorithm. The main goal of this paper is to use a two-level factorial design of experiments to analyze the statistical significance of the observed differences in selected performance criteria produced when testing different strategies in the N-M based repulsion algorithm.

Cutting path as a rural postman problem

The Rural Postman Problem (RPP) is a particular Arc Routing Problem (ARP) which consists of determining a minimum cost circuit on a graph so that a given subset of required edges is traversed. The RPP is an NP-hard problem with significant real-life applications. This paper introduces an original approach based on Memetic Algorithms - the MARP algorithm - to solve the RPP and, also deals with an interesting Industrial Application, which focuses on the path optimization for component cutting operations. Memetic Algorithms are a class of Metaheuristics which may be seen as a population strategy that involves cooperation and competition processes between population elements and integrates “social knowledge”, using a local search procedure. The MARP algorithm is tested with different groups of instances and the results are compared with those gathered from other publications. MARP is also used in the context of various real-life applications.

Algoritmo genético para empacotamento de contentores

No âmbito da investigação operacional o problema de empacotamento de contentores é conhecido por procurar definir uma configuração de carga, de forma a otimizar a utilização de um espaço disponível para efetuar o empacotamento. Este problema pode ser apresentado em diversas formas, formas estas que variam em função das características de cada empacotamento. Estas características podem ser: o tipo de carga que se pretende carregar (homogénea ou heterogénea), a possibilidade de a carga poder sofrer rotações em todas as suas dimensões ou apenas em algumas, o lucro que está associado a cada caixa carregada ou restrições inerentes ao contentor como por exemplo dimensões. O interesse pelo estudo de problemas de empacotamento de contentores tem vindo a receber cada vez mais ênfase por várias razões, uma delas é o interesse financeiro dado que o transporte é uma prática que representa custos, sendo importante diminuir estes custos aproveitando o volume do contentor da melhor forma. Outra preocupação que motiva o estudo deste problema prende-se com fatores ambientes, onde se procura racionalizar os recursos naturais estando esta também ligada a questões financeiras. Na literatura podem ser encontradas varias propostas para solucionar este problema, cada uma destas dirigidas a uma variante do problema, estas propostas podem ser determinísticas ou não determinísticas onde utilizam heurísticas ou metaheurísticas. O estudo realizado nesta dissertação descreve algumas destas propostas, nomeadamente as metaheurísticas que são utilizadas na resolução deste problema. O trabalho aqui apresentado traz também uma nova metaheurísticas, mais precisamente um algoritmo genético que terá como objetivo, apresentar uma configuração de carga para um problema de empacotamento de um contentor. O algoritmo genético tem como objetivo a resolução do seguinte problema: empacotar várias caixas retangulares com diversos tamanhos num contentor. Este problema é conhecido como Bin-Packing. A novidade que este algoritmo genético vai introduzir nas diversas soluções apresentadas até à data, é uma nova forma de criar padrões iniciais, ou seja, é utilizada a heurística HSSI (Heurística de Suavização de Superfícies Irregulares) que tem como objetivo criar uma população inicial de forma a otimizar o algoritmo genético. A heurística HSSI tenta resolver problemas de empacotamento simulando, o comportamento da maioria das pessoas ao fazer este processo na vida real, contudo, tem um campo de busca reduzido entre as soluções possíveis e será então utilizado um algoritmo genético para ampliar este campo de busca e explorar novas soluções. No final pretende-se obter um software onde será possível configurar um dado problema de empacotamento de um contentor e obter, a solução do mesmo através do algoritmo genético. Assim sendo, o estudo realizado tem como principal objetivo contribuir com pesquisas e conclusões, sobre este problema e trazer uma nova proposta de solução para o problema de empacotamento de contentores.

A filter inexact-restoration method for nonlinear programming

A new iterative algorithm based on the inexact-restoration (IR) approach combined with the filter strategy to solve nonlinear constrained optimization problems is presented. The high level algorithm is suggested by Gonzaga et al. (SIAM J. Optim. 14:646–669, 2003) but not yet implement—the internal algorithms are not proposed. The filter, a new concept introduced by Fletcher and Leyffer (Math. Program. Ser. A 91:239–269, 2002), replaces the merit function avoiding the penalty parameter estimation and the difficulties related to the nondifferentiability. In the IR approach two independent phases are performed in each iteration, the feasibility and the optimality phases. The line search filter is combined with the first one phase to generate a “more feasible” point, and then it is used in the optimality phase to reach an “optimal” point. Numerical experiences with a collection of AMPL problems and a performance comparison with IPOPT are provided.

Active search for leprosy cases in Midwestern Brazil: a serological evaluation of asymptomatic household contacts before and after prophylaxis with bacillus Calmette-Guérin

Leprosy is a disease caused by Mycobacterium leprae that carries a high risk of disability, making early diagnosis mandatory. This study aimed to determine the applicability of anti-PGL-1 IgM antibody detection, using the ML FLOW technique, as an assistant tool for the detection of leprosy infection in asymptomatic household contacts (AHHC) of multibacillary leprosy index cases from Midwest Brazil. Serological changes induced by the prophylaxis of these household contacts with Bacillus Calmette-Guérin (BCG) were also verified. A total of 91 AHHC were assessed, among which, 18.68% (n = 17) presented both positive bacilloscopy and positive anti-PGL-1 IgM serology. Positivity concordance between these two laboratorial exams (Kappa Index = 1; p < 0.001) was indicated, however, one case did not demonstrate concordance between the semiquantitative assessment of anti-PGL-1 IgM and the bacilloscopy index (Kappa Index = 0.96; p < 0.001). Among the 17 AHHC with positive bacilloscopy, eight were reassessed after prophylaxis with BCG and two of them presented negative anti-PGL-1 IgM serology, being these patients who had presented a bacilloscopy index of < 2[+] in the initial assessment. This study shows that anti-PGL-1 IgM detection may be used as a tool to determine the bacillary load in AHHC and to detect immune changes related to prophylaxis by nonspecific vaccination.

Host-induced changes in the cell surface N-linked glycoproteins, from Aspergillus fumigatus. Search for specific targets with potential for clinical therapy and/or diagnosis.

This dissertation is presented to obtain a Master degree in Structural and Functional Biochemistry

Localização de Precisão Usando Redes Sem Fios Enterradas

As Redes Sem Fios Enterradas (Wireless Underground Networks - WUN) são formadas por nós que comunicam entre si através de ligações sem fios e têm como meio de propagação o solo. Os sistemas de localização mais utilizados atualmente têm desvantagens ao nível da precisão e o custo. Nesta tese é proposta uma solução de localização de precisão que recorre à utilização de redes sem fios enterradas e um algoritmo de posicionamento baseados em Wi-Fi. O objetivo é estimar a localização de objetos, utilizando dispositivos Wi-Fi de baixo custo. Os resultados experimentais obtidos demonstram que o erro de localização é inferior a 0,40 m, e que esta solução é viável para, por exemplo, localizar jogadores num campo de futebol ou localizar um objeto num campo agrícola.

Automated segmentation, tracking and evaluation of bacteria in microscopy images

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Improving search engines with open Web-based SKOS vocabularies

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Facing unemployment after being at the top: impact, job search strategies and crucial competences

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Chagas' disease: an algorithm for donor screening and positive donor counseling

Classical serological screening assays for Chagas' disease are time consuming and subjective. The objective of the present work is to evaluate the enzyme immuno-assay (ELISA) methodology and to propose an algorithm for blood banks to be applied to Chagas' disease. Seven thousand, nine hundred and ninety nine blood donor samples were screened by both reverse passive hemagglutination (RPHA) and indirect immunofluorescence assay (IFA). Samples reactive on RPHA and/or IFA were submitted to supplementary RPHA, IFA and complement fixation (CFA) tests. This strategy allowed us to create a panel of 60 samples to evaluate the ELISA methodology from 3 different manufacturers. The sensitivity of the screening by IFA and the 3 different ELISA's was 100%. The specificity was better on ELISA methodology. For Chagas disease, ELISA seems to be the best test for blood donor screening, because it showed high sensitivity and specificity, it is not subjective and can be automated. Therefore, it was possible to propose an algorithm to screen samples and confirm donor results at the blood bank.

Constraint reasoning with local search for continuous optimization

Optimization is a very important field for getting the best possible value for the optimization function. Continuous optimization is optimization over real intervals. There are many global and local search techniques. Global search techniques try to get the global optima of the optimization problem. However, local search techniques are used more since they try to find a local minimal solution within an area of the search space. In Continuous Constraint Satisfaction Problems (CCSP)s, constraints are viewed as relations between variables, and the computations are supported by interval analysis. The continuous constraint programming framework provides branch-and-prune algorithms for covering sets of solutions for the constraints with sets of interval boxes which are the Cartesian product of intervals. These algorithms begin with an initial crude cover of the feasible space (the Cartesian product of the initial variable domains) which is recursively refined by interleaving pruning and branching steps until a stopping criterion is satisfied. In this work, we try to find a convenient way to use the advantages in CCSP branchand- prune with local search of global optimization applied locally over each pruned branch of the CCSP. We apply local search techniques of continuous optimization over the pruned boxes outputted by the CCSP techniques. We mainly use steepest descent technique with different characteristics such as penalty calculation and step length. We implement two main different local search algorithms. We use “Procure”, which is a constraint reasoning and global optimization framework, to implement our techniques, then we produce and introduce our results over a set of benchmarks.