Iniqüidade em saúde: uma extensão das desigualdades sociais no sistema público de saúde do Brasil (2003-2006)

This issue analises the unevenness in the brazilian system of public health care as an extension of socials inequities. It is a theoretical study based in a historical method, using empirical camp from academic, corporation and institution researchs, along the period 2002-2006. Equality and effectiveness in health systems are analitical basic cathegories grap in the root of the doctrine, principles and organization of the Unique Health System, in which sectorial actions are inserted. Discuss the estructural prodution and determined those inequalities through some social determiners of health system: income, land, food securitiy, nutritional situation, basic sanitation, epidemiological inequities and public management policy. Carry out a thematic review over health social production, it formlation and the goals of social policies, as well as the insertion of the equality principle in the assistance system, in the frame of the running public health regulations. It uses reflections that enlighted the correlation between the process of political-institutional actions and equity on health assistance. Analized the pertinency of sectorial reorganizational strategies on basic attendance, confronting the hipothesis that those strategies reinforce social inequities in health system, because it organize diferential assistance levels over not equal baselines. The results show up that social inequalities, even remaining, have had a small decrease; that the selectiviness of actual public policies and the duplication of the health system, increases the differences within and between the social classes and configures the assistance as inequal. The basic care system has great shortages that also appeares in middle and complex assistance levels. As conclusion, it remarks that the health assintance system, even with it integrality has limits; structural problems on material conditions of living and health system could not be reversed only with institutional legal arragements; by the contrary, in border conditions, these strategies produce policies that reinforce inequities, neglecting the equity principle of the system in which frame, they work. One patina of this tim

Estratégias de aplicações sequenciais e paralelas da metaheurística otimização por enxame de partículas ao problema do caixeiro viajante

Particle Swarm Optimization is a metaheuristic that arose in order to simulate the behavior of a number of birds in flight, with its random movement locally, but globally determined. This technique has been widely used to address non-liner continuous problems and yet little explored in discrete problems. This paper presents the operation of this metaheuristic, and propose strategies for implementation of optimization discret problems as form of execution parallel as sequential. The computational experiments were performed to instances of the TSP, selected in the library TSPLIB contenct to 3038 nodes, showing the improvement of performance of parallel methods for their sequential versions, in executation time and results

Otimização de superfícies seletivas de frequência com elementos pré-fractais utilizando rede neural MLP e algoritmos de busca populacional

This thesis describes design methodologies for frequency selective surfaces (FSSs) composed of periodic arrays of pre-fractals metallic patches on single-layer dielectrics (FR4, RT/duroid). Shapes presented by Sierpinski island and T fractal geometries are exploited to the simple design of efficient band-stop spatial filters with applications in the range of microwaves. Initial results are discussed in terms of the electromagnetic effect resulting from the variation of parameters such as, fractal iteration number (or fractal level), fractal iteration factor, and periodicity of FSS, depending on the used pre-fractal element (Sierpinski island or T fractal). The transmission properties of these proposed periodic arrays are investigated through simulations performed by Ansoft DesignerTM and Ansoft HFSSTM commercial softwares that run full-wave methods. To validate the employed methodology, FSS prototypes are selected for fabrication and measurement. The obtained results point to interesting features for FSS spatial filters: compactness, with high values of frequency compression factor; as well as stable frequency responses at oblique incidence of plane waves. This thesis also approaches, as it main focus, the application of an alternative electromagnetic (EM) optimization technique for analysis and synthesis of FSSs with fractal motifs. In application examples of this technique, Vicsek and Sierpinski pre-fractal elements are used in the optimal design of FSS structures. Based on computational intelligence tools, the proposed technique overcomes the high computational cost associated to the full-wave parametric analyzes. To this end, fast and accurate multilayer perceptron (MLP) neural network models are developed using different parameters as design input variables. These neural network models aim to calculate the cost function in the iterations of population-based search algorithms. Continuous genetic algorithm (GA), particle swarm optimization (PSO), and bees algorithm (BA) are used for FSSs optimization with specific resonant frequency and bandwidth. The performance of these algorithms is compared in terms of computational cost and numerical convergence. Consistent results can be verified by the excellent agreement obtained between simulations and measurements related to FSS prototypes built with a given fractal iteration

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

Maximização da penetração da geração distribuída através do algoritmo de otimização nuvem de partículas

This work develops a methodology for defining the maximum active power being injected into predefined nodes in the studied distribution networks, considering the possibility of multiple accesses of generating units. The definition of these maximum values is obtained from an optimization study, in which further losses should not exceed those of the base case, i.e., without the presence of distributed generation. The restrictions on the loading of the branches and voltages of the system are respected. To face the problem it is proposed an algorithm, which is based on the numerical method called particle swarm optimization, applied to the study of AC conventional load flow and optimal load flow for maximizing the penetration of distributed generation. Alternatively, the Newton-Raphson method was incorporated to resolution of the load flow. The computer program is performed with the SCILAB software. The proposed algorithm is tested with the data from the IEEE network with 14 nodes and from another network, this one from the Rio Grande do Norte State, at a high voltage (69 kV), with 25 nodes. The algorithm defines allowed values of nominal active power of distributed generation, in percentage terms relative to the demand of the network, from reference values

Avaliacao de dados de secagem de suspensoes de polpas de frutas em leito de jorro com alimentacao intermitente

The objective of this work was the development and improvement of the mathematical models based on mass and heat balances, representing the drying transient process fruit pulp in spouted bed dryer with intermittent feeding. Mass and energy balance for drying, represented by a system of differential equations, were developed in Fortran language and adapted to the condition of intermittent feeding and mass accumulation. Were used the DASSL routine (Differential Algebraic System Solver) for solving the differential equation system and used a heuristic optimization algorithm in parameter estimation, the Particle Swarm algorithm. From the experimental data food drying, the differential models were used to determine the quantity of water and the drying air temperature at the exit of a spouted bed and accumulated mass of powder in the dryer. The models were validated using the experimental data of drying whose operating conditions, air temperature, flow rate and time intermittency, varied within the limits studied. In reviewing the results predicted, it was found that these models represent the experimental data of the kinetics of production and accumulation of powder and humidity and air temperature at the outlet of the dryer

Projections Onto Convex Sets through Particle Swarm Optimization and its application for remote sensing image restoration

Image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smoothed regions without increasing the noise. Techniques based on Projections Onto Convex Sets (POCS) have been extensively used in the context of image restoration by projecting the solution onto hyperspaces until some convergence criteria be reached. It is expected that an enhanced image can be obtained at the final of an unknown number of projections. The number of convex sets and its combinations allow designing several image restoration algorithms based on POCS. Here, we address two convex sets: Row-Action Projections (RAP) and Limited Amplitude (LA). Although RAP and LA have already been used in image restoration domain, the former has a relaxation parameter (A) that strongly depends on the characteristics of the image that will be restored, i.e., wrong values of A can lead to poorly restoration results. In this paper, we proposed a hybrid Particle Swarm Optimization (PS0)-POCS image restoration algorithm, in which the A value is obtained by PSO to be further used to restore images by POCS approach. Results showed that the proposed PSO-based restoration algorithm outperformed the widely used Wiener and Richardson-Lucy image restoration algorithms. (C) 2010 Elsevier B.V. All rights reserved.

A percepção de suporte organizacional dos novos servidores da Universidade Federal do Rio Grande do Norte

This work deals with organizational support perception-OSP. This is done considering staff global beliefs, value of their contributions, care and well being related of individuals in an organization. These issues has been have been present in many researches since the 80s. It is important to analyze this matter considering all the changes that occurred nationally and internationally in the academic environment, characterized as being diversified in academic and managerial level. This occurs especially in Brazil since Federal Universities and their agents have suffered impact and restructuring. The research has emphasized the technical-managerial as well as professor staff level at Universidade Federal do Rio Grande do Norte aiming to deal with their unsatisfaction upon social political support. Thus, there was participation of 259 staff that were admitted from 2003 to 2005 in many sectors and units of the university. These subjects were submitted to a social and demographical query through an application of a scale of organizational support perception-OSP. Descriptive and multivariate analysis was done considering factors (management style of higher command), material support, wages, work overload, social support at work and ascending levels). Results show that there a hierarchy of the analyzed factors, the factor social support at work as being the first one with 3,31%, the factor management style of the higher command in second with 3,30%, then followed by the factor social support at work with 2,92%, in a sequence the factor material support, factor overload with 2,49% in the fourth position, and in the fifth place, the factor ascending with an average of 2,33% and in last the factor wages with 2,20%. It is concluded that the factors related to the more positive attributions of the OSP (social support at work), in a sequence material support opposing from the most negative factors of the OSP (wages, overload, ascending) reveal a reality that points out that the OSP is very much related to the individualized actions of the members of the organization (leader-member-group) rather than on structure and institutionalized actions carried out at UFRN

Arquitetura multiagente baseada em nuvem de partículas para hibridização de metaheurísticas

This thesis proposes an architecture of a new multiagent system framework for hybridization of metaheuristics inspired on the general Particle Swarm Optimization framework (PSO). The main contribution is to propose an effective approach to solve hard combinatory optimization problems. The choice of PSO as inspiration was given because it is inherently multiagent, allowing explore the features of multiagent systems, such as learning and cooperation techniques. In the proposed architecture, particles are autonomous agents with memory and methods for learning and making decisions, using search strategies to move in the solution space. The concepts of position and velocity originally defined in PSO are redefined for this approach. The proposed architecture was applied to the Traveling Salesman Problem and to the Quadratic Assignment Problem, and computational experiments were performed for testing its effectiveness. The experimental results were promising, with satisfactory performance, whereas the potential of the proposed architecture has not been fully explored. For further researches, the proposed approach will be also applied to multiobjective combinatorial optimization problems, which are closer to real-world problems. In the context of applied research, we intend to work with both students at the undergraduate level and a technical level in the implementation of the proposed architecture in real-world problems

Algorítmo evolucionário para a distribuição de produtos de petróleo por redes de polidutos

The distribution of petroleum products through pipeline networks is an important problem that arises in production planning of refineries. It consists in determining what will be done in each production stage given a time horizon, concerning the distribution of products from source nodes to demand nodes, passing through intermediate nodes. Constraints concerning storage limits, delivering time, sources availability, limits on sending or receiving, among others, have to be satisfied. This problem can be viewed as a biobjective problem that aims at minimizing the time needed to for transporting the set of packages through the network and the successive transmission of different products in the same pipe is called fragmentation. This work are developed three algorithms that are applied to this problem: the first algorithm is discrete and is based on Particle Swarm Optimization (PSO), with local search procedures and path-relinking proposed as velocity operators, the second and the third algorithms deal of two versions based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed algorithms are compared to other approaches for the same problem, in terms of the solution quality and computational time spent, so that the efficiency of the developed methods can be evaluated

Feature selection through gravitational search algorithm

In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.

Use of radiograph optical densitometry to monitor bone quality in broiler breeders

This study was carried out at Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista, Botucatu, SP, Brazil, and evaluated bone quality in broiler breeders. Twenty-three families of Ross broiler breeders were housed in 5.0-m² pens. The families were comprised of 13 females and one male at the onset of the experimental period. The mean number of females per family was 9.34 at the end of the trial. The feeding program and management followed strain guidelines (Agroceres Ross, 2003). Bone analyses were performed in the right tibia and femur using optical radiographic densitometry at 4, 8, 12, 15, 20, 24, 30, 35, 42, 47 and 52 weeks of rearing. Trap nests were used to collect eggs from the breeders two weeks before and after the evaluation weeks. At each evaluation day, five birds were sacrificed after radiographs were taken and the tibias and femurs were collected to perform the following analyses: fatfree dry matter, ash percentage, bone resistance and Seedor index. Therefore, it was possible to establish correlations between bone quality and eggshell quality. Characteristics of bone quality were highly correlated to each other; on the other hand, there were no correlations between bone quality and external egg quality. In conclusion, there was no effect of egg production on egg quality, possibly because there was no reabsorption of bone minerals.

The malarial impact on the nutritional status of Amazonian adult subjects

A avaliação antropométrica (pêso, altura, circunferência branquial, prega cutânea tricipital, prega cutânea subescapular, índice de Quetelet e circunferência muscular do braço) e bioquímica (proteínas e lipides) foi realizado em 120 indivíduos (93 masculinos e 27 do sexo feminino), de 17 a 72 anos de idade, moradores de área endêmica de malária (Humaitá -AM). de acordo com a história da doença (malária) eles foram divididos em 4 grupos: G1 - controle (n = 30), sem história de malária; G2 - controle (n = 40), com história de malária, mas sem manifestação de doença atual; G3 - doentes com Plasmodium vivax (n = 19) e G4 - doentes com Plasmodium faleiparum (n = 31). O diagnóstico de malária foi estabelecido por manifestações clínicas e confirmado laboratorialmente (gota espessa e esfregaço). No global as medidas antropométricas e bioquímicas discriminaram os grupos diferentemente. As medidas antropométricas do pêso, altura, reservas calóricas e estoque proteicos somáticos, apresentaram pouca sensibilidade, discriminado apenas os grupos extremos (Gl > G4). As medidas bioquímicas, no geral diferenciaram dois grandes grupos, os sadios e os doentes (G1+G2) e (G3+G4). Os doentes com Plasmodium falciparum (G4) foram os que se apresentaram em pior estado nutricional para a maioria das variáveis, sem entretanto, nenhuma variável individual que os discriminasse significativamente do G3. Estes dados permitem concluir que a malária resulta em desnutrição do hospedeiro, cuja gravidade está relacionada ao tipo e estágio da doença.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Swarm control designs applied to a micro-electro-mechanical gyroscope system (MEMS)

This paper analyzes the non-linear dynamics of a MEMS Gyroscope system, modeled with a proof mass constrained to move in a plane with two resonant modes, which are nominally orthogonal. The two modes are ideally coupled only by the rotation of the gyro about the plane's normal vector. We demonstrated that this model has an unstable behavior. Control problems consist of attempts to stabilize a system to an equilibrium point, a periodic orbit, or more general, about a given reference trajectory. We also developed a particle swarm optimization technique for reducing the oscillatory movement of the nonlinear system to a periodic orbit. © 2010 Springer-Verlag.