Multi-Objective Pipe Smoothing Genetic Algorithm For Water Distribution Network Design

This paper describes the formulation of a Multi-objective Pipe Smoothing Genetic Algorithm (MOPSGA) and its application to the least cost water distribution network design problem. Evolutionary Algorithms have been widely utilised for the optimisation of both theoretical and real-world non-linear optimisation problems, including water system design and maintenance problems. In this work we present a pipe smoothing based approach to the creation and mutation of chromosomes which utilises engineering expertise with the view to increasing the performance of the algorithm whilst promoting engineering feasibility within the population of solutions. MOPSGA is based upon the standard Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and incorporates a modified population initialiser and mutation operator which directly targets elements of a network with the aim to increase network smoothness (in terms of progression from one diameter to the next) using network element awareness and an elementary heuristic. The pipe smoothing heuristic used in this algorithm is based upon a fundamental principle employed by water system engineers when designing water distribution pipe networks where the diameter of any pipe is never greater than the sum of the diameters of the pipes directly upstream resulting in the transition from large to small diameters from source to the extremities of the network. MOPSGA is assessed on a number of water distribution network benchmarks from the literature including some real-world based, large scale systems. The performance of MOPSGA is directly compared to that of NSGA-II with regard to solution quality, engineering feasibility (network smoothness) and computational efficiency. MOPSGA is shown to promote both engineering and hydraulic feasibility whilst attaining good infrastructure costs compared to NSGA-II.

Cooperação e multi-level governance: o caso do Grande Recife Consórcio de Transporte Metropolitano

As primeiras regiões metropolitanas brasileiras foram instituídas de maneira vertical e autoritária como parte da estratégia de desenvolvimento nacional promovida pelo governo militar. Percebidas como instituições não-democráticas e rejeitadas como possível quarto ente federativo, as regiões metropolitanas, desde a Constituição de 1988, foram gradualmente esvaziadas dos seus propósitos originais. Em sua orfandade, os problemas socioeconômicos proliferaram e foram acentuados, e passaram a predominar relações intergovernamentais competitivas em vez de cooperativas. Um dos principais desafios enfrentados pelo modelo federalista brasileiro, em especial quando se trata destas regiões, está relacionado à necessidade de estabelecer maior cooperação e coordenação, tidas como imprescindíveis para garantir um relacionamento mais equilibrado entre os entes federativos, assim como para a efetiva implementação de políticas de enfrentamento das desigualdades e exclusão social nas aglomerações urbanas. Este trabalho analisa o Grande Recife Consórcio Metropolitano de Transportes (CMT), empresa pública multifederativa estabelecida em 2008 entre os governos municipais e estadual da Região Metropolitana de Recife (RMR). Responsável pelo planejamento, gestão e implementação compartilhada da política de transporte público coletivo na RMR, o Grande Recife se tornou realidade com a aprovação e regulamentação da Lei Federal nº 11.107 de 2005, conhecida como a Lei de Consórcios Públicos. O Grande Recife é uma experiência pioneira e inovadora, demonstrando que é possível encontrar uma maneira de superar conflitos e desafios comuns e, ao mesmo tempo, garantir a preservação da autonomia de cada ente, bem como os direitos cidadãos. Neste trabalho consideramos essa experiência de cooperação intergovernamental como um exemplo de multi-level governance (MLG), uma vez que é ilustrativa de um novo arranjo institucional democrático entre distintas esferas governamentais para a gestão compartilhada de um serviço público.

Understanding e-government technology transfer: evidence from the transfer of a patent management system from EPO to INPI

Drawing upon Brazilian experience, this research explores some of the key issues to be addressed in using e-government technical cooperation designed to enhance service provision of Patent Offices in developing countries. While the development of software applications is often seen merely as a technical engineering exercise, localization and adaptation are context bounded matters that are characterized by many entanglements of human and non-humans. In this work, technical, legal and policy implications of technical cooperation are also discussed in a complex and dynamic implementation environment characterized by the influence of powerful hidden agendas associated with the arena of intellectual property (IP), which are shaped by recent technological, economic and social developments in our current knowledge-based economy. This research employs two different theoretical lenses to examine the same case, which consists of transfer of a Patent Management System (PMS) from the European Patent Office (EPO) to the Brazilian Patent Office that is locally named ‘Instituto Nacional da Propriedade Industrial’ (INPI). Fundamentally, we have opted for a multi-paper thesis comprising an introduction, three scientific articles and a concluding chapter that discusses and compares the insights obtained from each article. The first article is dedicated to present an extensive literature review on e-government and technology transfer. This review allowed the proposition on an integrative meta-model of e-government technology transfer, which is named E-government Transfer Model (ETM). Subsequently, in the second article, we present Actor-Network Theory (ANT) as a framework for understanding the processes of transferring e-government technologies from Patent Offices in developed countries to Patent Offices in developing countries. Overall, ANT is seen as having a potentially wide area of application and being a promising theoretical vehicle in IS research to carry out a social analysis of messy and heterogeneous processes that drive technical change. Drawing particularly on the works of Bruno Latour, Michel Callon and John Law, this work applies this theory to a longitudinal study of the management information systems supporting the Brazilian Patent Office restructuration plan that involved the implementation of a European Patent Management System in Brazil. Based upon the ANT elements, we follow the actors to identify and understand patterns of group formation associated with the technical cooperation between the Brazilian Patent Office (INPI) and the European Patent Office (EPO). Therefore, this research explores the intricate relationships and interactions between human and non-human actors in their attempts to construct various network alliances, thereby demonstrating that technologies embodies compromise. Finally, the third article applies ETM model as a heuristic frame to examine the same case previously studied from an ANT perspective. We have found evidence that ETM has strong heuristic qualities that can guide practitioners who are engaged in the transfer of e-government systems from developed to developing countries. The successful implementation of e-government projects in developing countries is important to stimulate economic growth and, as a result, we need to understand the processes through which such projects are being implemented and succeed. Here, we attempt to improve understanding on the development and stabilization of a complex social-technical system in the arena of intellectual property. Our preliminary findings suggest that e-government technology transfer is an inherently political process and that successful outcomes require continuous incremental actions and improvisations to address the ongoing issues as they emerge.

A Informatização da saúde no Brasil: uma análise multi-paper inspirada na teoria ator-rede

As tecnologias da informação e comunicação (TIC) estão presentes nas mais diversas áreas e atividades cotidianas, mas, em que pesem as ações de governos e instituições privadas, a informatização da saúde ainda é um desafio em aberto no Brasil. A situação atual leva a um questionamento sobre as dificuldades associadas à informatização das práticas em saúde, assim como, quais efeitos tais dificuldades têm causado à sociedade Brasileira. Com objetivo de discutir as questões acima citadas, esta tese apresenta quatro artigos sobre processo de informação da saúde no Brasil. O primeiro artigo revisa a literatura sobre TIC em saúde e baseado em duas perspectivas teóricas – estudos Europeus acerca dos Sistemas de Informação em Saúde (SIS) nos Países em Desenvolvimento e estudos sobre Informação e Informática em Saúde, no âmbito do Movimento da Reforma Sanitária –, formula um modelo integrado que combina dimensões de análise e fatores contextuais para a compreensão dos SIS no Brasil. Já o segundo artigo apresenta os conceitos e teóricos e metodológicos da Teoria Ator-Rede (ANT), uma abordagem para o estudo de controvérsias associadas às descobertas científicas e inovações tecnológicas, por meio das redes de atores envolvidos em tais ações. Tal abordagem tem embasado estudos de SI desde 1990 e inspirou as análises dois artigos empíricos desta tese. Os dois últimos artigos foram redigidos a partir da análise da implantação de um SIS em um hospital público no Brasil ocorrida entre os anos de 2010 e 2012. Para a análise do caso, seguiram-se os atores envolvidos nas controvérsias que surgiram durante a implantação do SIS. O terceiro artigo se debruçou sobre as atividades dos analistas de sistema e usuários envolvidos na implantação do SIS. As mudanças observadas durante a implantação do sistema revelam que o sucesso do SIS não foi alcançado pela estrita e técnica execução das atividades incialmente planejadas. Pelo contrário, o sucesso foi construído coletivamente, por meio da negociação entre os atores e de dispositivos de interessamento introduzidos durante o projeto. O quarto artigo, baseado no conceito das Infraestruturas de Informação, discutiu como o sistema CATMAT foi incorporado ao E-Hosp. A análise revelou como a base instalada do CATMAT foi uma condição relevante para a sua escolha durante a implantação do E-Hosp. Além disso, descrevem-se negociações e operações heterogêneas que aconteceram durante a incorporação do CATMAT no sistema E-Hosp. Assim, esta tese argumenta que a implantação de um SIS é um empreendimento de construção coletiva, envolvendo analistas de sistema, profissionais de saúde, políticos e artefatos técnicos. Ademais, evidenciou-se como os SIS inscrevem definições e acordos, influenciando as preferências dos atores na área de saúde.

Severity of banana leaf spot in an intercropping system in two cycles of banana Prata Ana

Prata Ana is the most planted banana cultivar in northern Minas Gerais, Brazil. It is however susceptible to several pathogens. This study was carried out to evaluate the disease severity of banana leaf spot in the Prata Ana cv. in the first and second cycle under six different planting systems. The randomized block experimental design was used with six treatments and four replications. lit an evaluation of the severity of banana leaf spot, no disease symptoms were found on Thap Maeo and Caipira. The evolution curve of the disease indicated seasonal effects in the first and second cycles. The severity, of banana leaf spot was highest soon after the regional rainy period from November to March. A comparison of the means of the evaluations indicated a reduction in disease severity from the first to the second cycle.

Aprendizado por Reforço com Valores deInfluência em Sistemas Multi-Agente

We propose a new paradigm for collective learning in multi-agent systems (MAS) as a solution to the problem in which several agents acting over the same environment must learn how to perform tasks, simultaneously, based on feedbacks given by each one of the other agents. We introduce the proposed paradigm in the form of a reinforcement learning algorithm, nominating it as reinforcement learning with influence values. While learning by rewards, each agent evaluates the relation between the current state and/or action executed at this state (actual believe) together with the reward obtained after all agents that are interacting perform their actions. The reward is a result of the interference of others. The agent considers the opinions of all its colleagues in order to attempt to change the values of its states and/or actions. The idea is that the system, as a whole, must reach an equilibrium, where all agents get satisfied with the obtained results. This means that the values of the state/actions pairs match the reward obtained by each agent. This dynamical way of setting the values for states and/or actions makes this new reinforcement learning paradigm the first to include, naturally, the fact that the presence of other agents in the environment turns it a dynamical model. As a direct result, we implicitly include the internal state, the actions and the rewards obtained by all the other agents in the internal state of each agent. This makes our proposal the first complete solution to the conceptual problem that rises when applying reinforcement learning in multi-agent systems, which is caused by the difference existent between the environment and agent models. With basis on the proposed model, we create the IVQ-learning algorithm that is exhaustive tested in repetitive games with two, three and four agents and in stochastic games that need cooperation and in games that need collaboration. This algorithm shows to be a good option for obtaining solutions that guarantee convergence to the Nash optimum equilibrium in cooperative problems. Experiments performed clear shows that the proposed paradigm is theoretical and experimentally superior to the traditional approaches. Yet, with the creation of this new paradigm the set of reinforcement learning applications in MAS grows up. That is, besides the possibility of applying the algorithm in traditional learning problems in MAS, as for example coordination of tasks in multi-robot systems, it is possible to apply reinforcement learning in problems that are essentially collaborative

Controlador preditivo generalizado multi-modelo aplicado ao controle de pressão arterial

Postsurgical complication of hypertension may occur in cardiac patients. To decrease the chances of complication it is necessary to reduce elevated blood pressure as soon as possible. Continuous infusion of vasodilator drugs, such as sodium nitroprusside (Nipride), would quickly lower the blood pressure in most patients. However, each patient has a different sensitivity to infusion of Nipride. The parameters and the time delays of the system are initially unknown. Moreover, the parameters of the transfer function associated with a particular patient are time varying. the objective of the study is to develop a procedure for blood pressure control i the presence of uncertainty of parameters and considerable time delays. So, a methodology was developed multi-model, and for each such model a Preditive Controller can be a priori designed. An adaptive mechanism is then needed for deciding which controller should be dominant for a given plant

AC Biosusceptometry as a Tool for Monitoring the Gastrointestinal Transit of Multiparticulate Drug Delivery System

Drug delivery systems based on natural polysaccharides, such as chitosan (CS) and pectin (PC), rather than on synthetic polymers, have been widely studied. Some reasons for that are low toxicity and costs and high biodegradability of the formers. A multiparticulate system based on CS and PC was developed in our laboratories, including the addition of an enteric polymer, cellulose acetate phtalate (CAP). Such improvement promoted stronger gastric and enteric resistances, as assessed in vitro, making the systems more selective to enzymatic degradation in the colon. Although in vitro dissolution tests can simulate some properties concerning the gastrointestinal transit (GT), collaborating to characterize the systems behavior in the biological fluids, frequently they do not result in satisfactory in vitro/in vivo correlations. The objective of this work was to follow in vivo the GT of the particles developed by means of AC biosusceptometry (ACB), a non-invasive and of low cost methodology. The particles containing ferrite in powder form were prepared by complex coacervation using an ideal 3:1:1 mass ratio for PC:CS:CAP. The magnetic particles were administered to healthy volunteers by oral route. The GT was monitored by using multi-sensor ACB system and the signal acquisition was performed every IS min until the colonic region was reached. By means of ACB technique, it was possible to acquiring images generated by the magnetic particles within the whole gastrointestinal tract including the colonic region. Variable particles transit times were observed among the volunteers, but without interference on the mapping of the particles until the colonic region. The particles were able to produce magnetic field strong enough to generate signals adequate for mapping the particles. The results suggest that integral particles reached the colon, after they resisted against gastric and enteric media. Studies associating transit time and in vivo drug release are in development in order to confirm the efficiency of the systems.

An Intelligent System for Petroleum Well Drilling Cutting Analysis

Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.

Quantitative characterization of multi-phase materials by digital image processing

An automatic image processing and analysis technique has been developed for quantitative characterization of multi-phase materials. For the development of this technique is used the Khoros system that offers the basic morphological tools and a flexible, visual programming language. These techniques are implemented in a highly user oriented image processing environment that allows the user to adapt each step of the processing to his special requirements.To illustrate the implementation and performance of this technique, images of two different materials are processed for microstructure characterization. The result is presented through the determination of volume fraction of the different phases or precipitates.

Wear Particle Classifier System Based on an Artificial Neural Network

This paper describes a method of identifying morphological attributes that classify wear particles in relation to the wear process from which they originate and permit the automatic identification without human expertise. The method is based on the use of Multi Layer Perceptron (MLP) for analysis of specific types of microscopic wear particles. The classification of the wear particles was performed according to their morphological attributes of size and aspect ratio, among others. (C) 2010 Journal of Mechanical Engineering. All rights reserved.

Neural network based on adaptive resonance theory with continuous training for multi-configuration transient stability analysis of electric power systems

This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines. (C) 2010 Elsevier B.V. All rights reserved.

Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)