Object-oriented Environment For Intelligent Control Engineering

Most industrial processes have several particular characteristics, system and performance requirements, which may not be obtained by classical control methodologies. In this case advanced control strategies must be considered. In face of different options, simulation using software packages is a common practice. In this paper, a simulation environment aimed at consistently and uniformly integrating different software resources is proposed. It is based on object-oriented approach implemented into a knowledge-based shell. Full potential of knowledge based systems can be used for intelligent decision. An application example is described to illustrate the abilities of the proposed environment. This example is a prototype for simulation and control of a multivariable process: a heavy oil fractionary column.101596

Identificação e controle de processos via desenvolvimentos em séries ortonormais. Parte B: controle preditivo

This paper presents an overview about predictive control schemes based on orthonormal basis function models. Different predictive control schemes based on such models are discussed, namely, linear controllers with terminal (stabilizing) constraints, robust controllers, and non-linear controllers. The discussions comprise a broad bibliographical survey on the subject as well as two case studies involving a simulated dynamic system and a real process.O presente artigo aborda o problema da seleção da estrutura de modelos em algoritmos de controle preditivo para sistemas monovariáveis. Neste sentido, apresenta a utilização de modelos com estrutura dinâmica desenvolvida através de bases de funções ortonormais, como as funções de Laguerre, Kautz ou funções ortonormais generalizadas. Os principais aspectos relacionados com esta classe de modelos no contexto de controladores preditivos lineares com restrições terminais, não lineares e robusto são discutidos e uma revisão bibliográfica é apresentada. O desempenho de malha fechada das estratégias analisadas é ilustrado através de dois casos de estudo envolvendo uma incubadora para recém nascidos e um processo simulado de polimerização isotérmica.322336Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Identificação e controle de processos via desenvolvimentos em séries ortonormais. Parte A: identificação

In this paper, an overview about the identification of dynamic systems using orthonormal basis function models, such as those based on Laguerre and Kautz functions, is presented. The mathematical foundations of these models as well as their advantages and limitations are discussed within the contexts of linear, robust, and nonlinear identification. The discussions comprise a broad bibliographical survey on the subject and a comparative analysis involving some specific model realizations, namely, linear, Volterra, fuzzy, and neural models within the orthonormal basis function framework. Theoretical and practical issues regarding the identification of these models are also presented and illustrated by means of two case studies related to a polymerization process.O presente artigo apresenta uma visão geral do estado da arte na área de identificação de sistemas utilizando modelos dinâmicos com estrutura desenvolvida através de bases de funções ortonormais, como as funções de Laguerre, Kautz ou funções ortonormais generalizadas. Discute-se as vantagens e possíveis limitações desse tipo de estrutura bem como os fundamentos matemáticos dos modelos correspondentes nos contextos de identificação linear, linear com incertezas paramétricas (identificação robusta) e não linear, incluindo uma revisão bibliográfica abrangente sobre o tema. Diferentes realizações de modelos com funções de base ortonormal, a saber, modelos lineares, de Volterra, fuzzy e neurais, são detalhadas e discutidas comparativamente em termos de capacidade de representação, parcimônia, complexidade de projeto e interpretabilidade. Aspectos práticos da identificação desses modelos são também apresentados e ilustrados através de dois casos de estudo envolvendo um processo simulado de polimerização isotérmica.301321Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Size reduction of complex networks preserving modularity

The ubiquity of modular structure in real-world complex networks is being the focus of attention in many trials to understand the interplay between network topology and functionality. The best approaches to the identification of modular structure are based on the optimization of a quality function known as modularity. However this optimization is a hard task provided that the computational complexity of the problem is in the NP-hard class. Here we propose an exact method for reducing the size of weighted (directed and undirected) complex networks while maintaining invariant its modularity. This size reduction allows the heuristic algorithms that optimize modularity for a better exploration of the modularity landscape. We compare the modularity obtained in several real complex-networks by using the Extremal Optimization algorithm, before and after the size reduction, showing the improvement obtained. We speculate that the proposed analytical size reduction could be extended to an exact coarse graining of the network in the scope of real-space renormalization.Comment: 14 pages, 2 figure

Robust Expansion of Uncertain Volterra Kernels into Orthonormal Series

Abstract-This paper is concerned with the computation of uncertainty bounds for the expansion of uncertain Volterra models into an orthonormal basis of functions, such as the Laguerre or Kautz bases. This problem has already been addressed in the context of linear systems by means of an approach in which the uncertainty bounds of the expansion coefficients have been estimated from a structured set of impulse responses describing a linear uncertain process. This approach is extended here towards nonlinear Volterra models through the computation of the uncertainty bounds of the expansion coefficients from a structured set of uncertain Volterra kernels. The proposed formulation assures that the resulting model is able to represent all the original uncertainties with minimum intervals for the expansion coefficients. An example is presented to illustrate the effectiveness of the proposed formulation

Finding and evaluating community structure in networks

We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.Comment: 16 pages, 13 figure

Lethality and centrality in protein networks

In this paper we present the first mathematical analysis of the protein interaction network found in the yeast, S. cerevisiae. We show that, (a) the identified protein network display a characteristic scale-free topology that demonstrate striking similarity to the inherent organization of metabolic networks in particular, and to that of robust and error-tolerant networks in general. (b) the likelihood that deletion of an individual gene product will prove lethal for the yeast cell clearly correlates with the number of interactions the protein has, meaning that highly-connected proteins are more likely to prove essential than proteins with low number of links to other proteins. These results suggest that a scale-free architecture is a generic property of cellular networks attributable to universal self-organizing principles of robust and error-tolerant networks and that will likely to represent a generic topology for protein-protein interactions.Comment: See also http:/www.nd.edu/~networks and http:/www.nd.edu/~networks/cel

The Deleterious Effects of Shiga Toxin Type 2 Are Neutralized In Vitro by FabF8:Stx2 Recombinant Monoclonal Antibody

Hemolytic Uremic Syndrome (HUS) associated with Shiga-toxigenic Escherichia coli (STEC) infections is the principal cause of acute renal injury in pediatric age groups. Shiga toxin type 2 (Stx2) has in vitro cytotoxic effects on kidney cells, including human glomerular endothelial (HGEC) and Vero cells. Neither a licensed vaccine nor effective therapy for HUS is available for humans. Recombinant antibodies against Stx2, produced in bacteria, appeared as the utmost tool to prevent HUS. Therefore, in this work, a recombinant FabF8:Stx2 was selected from a human Fab antibody library by phage display, characterized, and analyzed for its ability to neutralize the Stx activity from different STEC-Stx2 and Stx1/Stx2 producing strains in a gold standard Vero cell assay, and the Stx2 cytotoxic effects on primary cultures of HGEC. This recombinant Fab showed a dissociation constant of 13.8 nM and a half maximum effective concentration (EC50 ) of 160 ng/mL to Stx2. Additionally, FabF8:Stx2 neutralized, in different percentages, the cytotoxic effects of Stx2 and Stx1/2 from different STEC strains on Vero cells. Moreover, it significantly prevented the deleterious effects of Stx2 in a dose-dependent manner (up to 83%) in HGEC and protected this cell up to 90% from apoptosis and necrosis. Therefore, this novel and simple anti-Stx2 biomolecule will allow further investigation as a new therapeutic option that could improve STEC and HUS patient outcomes.Fil: Luz, Daniela. Governo do Estado de Sao Paulo. Secretaria da Saude. Instituto Butantan; BrasilFil: Gomez, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Ferreira, Raissa L.. Governo do Estado de Sao Paulo. Secretaria da Saude. Instituto Butantan; BrasilFil: Melo, Bruna S.. Governo do Estado de Sao Paulo. Secretaria da Saude. Instituto Butantan; BrasilFil: Guth, Beatriz E. C.. Universidade de Sao Paulo; BrasilFil: Quintilio, Wagner. Governo do Estado de Sao Paulo. Secretaria da Saude. Instituto Butantan; BrasilFil: Moro, Ana Maria. Governo do Estado de Sao Paulo. Secretaria da Saude. Instituto Butantan; BrasilFil: Presta, Agostina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Sacerdoti, Flavia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Ibarra, Cristina Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Chen, Gang. University of Toronto; CanadáFil: Sidhu, Sachdev S.. University of Toronto; CanadáFil: Amaral, María Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: Fontes Piazza, Roxane María. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; Argentin

Conservation of geosites as a tool to protect geoheritage: the inventory of Ceará Central Domain, Borborema Province - NE/Brazil

The Ceará Central Domain, in the northern Borborema Province/NE Brazil, encompasses important geological records (geosites) which allow understanding a relevant period of the Earth’s evolution, mainly associated to Neoproterozoic Brazilian/Pan-African Cycle and West Gondwana amalgamation, besides Neoarchean to Ordovician records. The presented geoheritage inventory aims to characterise the geosites with scienti c relevance of Ceará Central Domain. By applying a method for large areas, the nal selection resulted in eight geological frameworks represented by 52 geosites documented in a single database. This is the rst step for a geoconservation strategy based on systematic inventories, statutory protection, geoethical behaviour and awareness about scienti c, educational and/or cultural relevance of geosites.We specially thank all experts that helped us with this inventory: Afonso Almeida, Carlos E.G. de Araújo, César Veríssimo, Christiano Magini, Clóvis Vaz Parente, Felipe G. Costa, Irani C. Mattos, Neivaldo de Castro, Otaciel de Melo, Sebástian G. Chiozza, Ticiano Santos and Stefano Zincone. We are also thankful to Kátia Mansur, Ricardo Fraga Pereira and anonymous reviewers for their valuable contributions. PM is grateful to Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for PhD mobility scholarship PDSE Program/Process n 88881.132168/2016-01info:eu-repo/semantics/publishedVersio