GA topology optimization using random keys for tree encoding of structures

Topology optimization consists in finding the spatial distribution of a given total volume of material for the resulting structure to have some optimal property, for instance, maximization of structural stiffness or maximization of the fundamental eigenfrequency. In this paper a Genetic Algorithm (GA) employing a representation method based on trees is developed to generate initial feasible individuals that remain feasible upon crossover and mutation and as such do not require any repairing operator to ensure feasibility. Several application examples are studied involving the topology optimization of structures where the objective functions is the maximization of the stiffness and the maximization of the first and the second eigenfrequencies of a plate, all cases having a prescribed material volume constraint.

Conectividade estrutural do cérebro

Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Área de especialização: Ressonância Magnética

Análise de risco em organizações: caso de estudo

Dissertação Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica no perfil de Manutenção e Produção

Conectividade estrutural do cérebro: diferenças entre um cérebro normal e um cérebro com patologia

Perceber a rede estrutural formada pelos neurónios no cérebro a nível da macro escala é um desafio atual na área das neurociências. Neste estudo analisou-se a conectividade estrutural do cérebro em 22 indivíduos saudáveis e em dois doentes com epilepsia pós-traumática. Avaliaram-se as diferenças entre estes dois grupos. Também se pesquisaram diferenças a nível do género e idade no grupo de indivíduos saudáveis e os que têm valores médios mais elevados nas métricas de caracterização da rede. Para tal, desenvolveu-se um protocolo de análise recorrendo a diversos softwares especializados e usaram-se métricas da Teoria dos Grafos para a caracterização da conectividade estrutural entre 118 regiões encefálicas distintas. Dentro do grupo dos indivíduos saudáveis concluiu-se que os homens, no geral, são os que têm média mais alta para as métricas de caracterização da rede estrutural. Contudo, não se observaram diferenças significativas em relação ao género nas métricas de caracterização global do cérebro. Relativamente à idade, esta correlaciona-se negativamente, no geral, com as métricas de caracterização da rede estrutural. As regiões onde se observaram as diferenças mais importantes entre indivíduos saudáveis e doentes são: o sulco rolândico, o hipocampo, o pré-cuneus, o tálamo e o cerebelo bilateralmente. Estas diferenças são consistentes com as imagens radiológicas dos doentes e com a literatura estudada sobre a epilepsia pós-traumática. Preveem-se desenvolvimentos para o estudo da conectividade estrutural do cérebro humano, uma vez que a sua potencialidade pode ser combinada com outros métodos de modo a caracterizar as alterações dos circuitos cerebrais.

Group decision making for selection of an information system in a business context

The main objective of this work is to report on the development of a multi-criteria methodology to support the assessment and selection of an Information System (IS) framework in a business context. The objective is to select a technological partner that provides the engine to be the basis for the development of a customized application for shrinkage reduction on the supply chains management. Furthermore, the proposed methodology di ers from most of the ones previously proposed in the sense that 1) it provides the decision makers with a set of pre-defined criteria along with their description and suggestions on how to measure them and 2)it uses a continuous scale with two reference levels and thus no normalization of the valuations is required. The methodology here proposed is has been designed to be easy to understand and use, without a specific support of a decision making analyst.

Models and tools for value systems analysis in collaborative environments

Dissertation to obtain the degree of Doctor in Electrical and Computer Engineering, specialization of Collaborative Networks

Estimation of infection rate in epidemic models with multiple populations

Dissertação para obtenção do Grau de Mestre em Matemática e Aplicações Especialização em Actuariado, Estatística e Investigação Operacional

An agent-based evolutionary approach for manufacturing system layout design

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Multipath inter-domain policy routing

Dissertação submetida para a obtenção do grau de Doutor em Engenharia Electrotécnica e de Computadores

Multipath policy routing in packet switched networks

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Planning and verification of multipath routing protocols

Conventionally the problem of the best path in a network refers to the shortest path problem. However, for the vast majority of networks present nowadays this solution has some limitations which directly affect their proper functioning, as well as an inefficient use of their potentialities. Problems at the level of large networks where graphs of high complexity are commonly present as well as the appearing of new services and their respective requirements, are intrinsically related to the inability of this solution. In order to overcome the needs present in these networks, a new approach to the problem of the best path must be explored. One solution that has aroused more interest in the scientific community considers the use of multiple paths between two network nodes, where they can all now be considered as the best path between those nodes. Therefore, the routing will be discontinued only by minimizing one metric, where only one path between nodes is chosen, and shall be made by the selection of one of many paths, thereby allowing the use of a greater diversity of the present paths (obviously, if the network consents). The establishment of multi-path routing in a given network has several advantages for its operation. Its use may well improve the distribution of network traffic, improve recovery time to failure, or it can still offer a greater control of the network by its administrator. These factors still have greater relevance when networks have large dimensions, as well as when their constitution is of high complexity, such as the Internet, where multiple networks managed by different entities are interconnected. A large part of the growing need to use multipath protocols is associated to the routing made based on policies. Therefore, paths with different characteristics can be considered with equal level of preference, and thus be part of the solution for the best way problem. To perform multi-path routing using protocols based only on the destination address has some limitations but it is possible. Concepts of graph theory of algebraic structures can be used to describe how the routes are calculated and classified, enabling to model the routing problem. This thesis studies and analyzes multi-path routing protocols from the known literature and derives a new algebraic condition which allows the correct operation of these protocols without any network restriction. It also develops a range of software tools that allows the planning and the respective verification/validation of new protocols models according to the study made.

Convex and toric geometry to analyze complex dynamics in chemical reaction systems

Convex cone, toric variety, graph theory, electrochemical catalysis, oxidation of formic acid, feedback-loopsbifurcations, enzymatic catalysis, Peroxidase reaction, Shil'nikov chaos

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Structural prediction of peptides bound to MHC class I.

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.

Structural Analysis of Networks

Network analysis naturally relies on graph theory and, more particularly, on the use of node and edge metrics to identify the salient properties in graphs. When building visual maps of networks, these metrics are turned into useful visual cues or are used interactively to filter out parts of a graph while querying it, for instance. Over the years, analysts from different application domains have designed metrics to serve specific needs. Network science is an inherently cross-disciplinary field, which leads to the publication of metrics with similar goals; different names and descriptions of their analytics often mask the similarity between two metrics that originated in different fields. Here, we study a set of graph metrics and compare their relative values and behaviors in an effort to survey their potential contributions to the spatial analysis of networks.