Les progrès récents dans les technologies et les systèmes de communications sans fil soutenus par la miniaturisation de dispositifs ont donné naissance une nouvelle génération de réseaux personnels permettant des communications autour du corps humain: les réseaux corporels. Cette thèse étudie les différents types du canal de propagation des réseaux corporels en environnement intérieur dans le contexte de l’analyse du mouvement et de la navigation de groupe. Dans ce travail, une approche de simulation pour le cala de propagation est présenté. Le simulateur de canal de propagation est basé sur les techniques de tracé de rayons et l’approche de simulation est basée sur l’utilisation d’antennes perturbées et l’utilisation des données de capture de mouvement pour la modélisation de la mobilité humaine. Premièrement, nous étudions la question de l’antenne et l’influence de la proximité du corps humain sur diagramme de rayonnement de l’antenne. En outre, un modèle simple utilisé pour prédire le diagramme de rayonnement d’une antenne placée à proximité d’un corps humain. Deuxièmement, le simulateur physique est présenté et l’approche de simulation est introduite. Afin de vérifier l’approche proposée, des simulations préliminaires ont été effectuées et une première comparaison avec des donnes de mesures disponibles est faite. Enfin, une campagne de mesure spécifique joignant les données radio et les données de capture de mouvement a été exploitée pour valider et évaluer les résultats de la simulation. / Recent advances in wireless technologies and system, empowered by the miniaturization of devices, give rise to a new generation of Personal Area Networks allowing communications around the human body : Body Area networks. This thesis studies the Body Area Network channels in indoor environment in the context of motion analysis and group navigation. In this work a simulation approach for BAN channels is presented. The propagation channel simulator is based on ray tracing and the simulation approach is based on using perturbed antennas and the use of motion capture data for modelling the human mobility. Firstly, we investigate the antenna issue and the influence of the human body prox- imity on antenna radiation pattern. Besides, a simple model used to predict the antenna radiation pattern placed in proximity to a human body. Secondly, the physical sim- ulator is presented and the simulation approach is introduced. In order to check the proposed approach, preliminary simulations were carried out and a first comparison with available measurement data is made. Finally, a specific measurement campaign jointing radio data and motion capture data was exploited to validate and evaluate the simulation results.

One of the important factors to be considered in designing an engineering system is uncertainty, which emanates from natural randomness, limited data, or limited knowledge of systems. In this study, a robust design methodology is established in order to design multifunctional materials, employing multi-time and length scale analyses. The Robust Concept Exploration Method with Error Margin Index (RCEM-EMI) is proposed for design incorporating non-deterministic system behavior. The Inductive Design Exploration Method (IDEM) is proposed to facilitate distributed, robust decision-making under propagated uncertainty in a series of multiscale analyses or simulations. These methods are verified in the context of Design of Multifunctional Energetic Structural Materials (MESM). The MESM is being developed to replace the large amount of steel reinforcement in a missile penetrator for light weight, high energy release, and sound structural integrity. In this example, the methods facilitate following state-of-the-art design capabilities, robust MESM design under (a) random microstructure changes and (b) propagated uncertainty in a multiscale analysis chain. The methods are designed to facilitate effective and efficient materials design; however, they are generalized to be applicable to any complex engineering systems design that incorporates computationally intensive simulations or expensive experiments, non-deterministic models, accumulated uncertainty in multidisciplinary analyses, and distributed, collaborative decision-making.

This thesis studies the effect on labour demand of a transition from an unconstrained economy featuring a ’clean’ and a ’dirty’ production method towards a fully clean economy, using a simplified General Equilibrium model. This model is calibrated to the European Union Emission Trading System (EUETS) and features a linear decline in a cap placed on the dirty production method. After simulating the trajectory set by the EU ETS, this study finds that wages are expected to increase by 11.68% over the transition period in the baseline scenario. This result is found to hold qualitatively both for steeper transitions and wide ranges of the elasticity of substitution and consumer preference in consumption, as long as clean productivity growth is sufficientand the consumer is either indifferent or favours the clean consumption good. The minimum level of clean productivity growth needed to achieve a long-runincrease in wages is found to be slightly below 2% per year, even when the cap on dirty production faces a steeper decline.

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No. of bitstreams: 1 alessandracristinagomesmagno.pdf: 3807060 bytes, checksum: a3775aee860af7ea06cde6b9d587ab80 (MD5) Previous issue date: 2016-03-22 / O objetivo principal deste trabalho é adicionar e analisar uma equação que repre senta o volume no modelo dinâmico do ciclo celular de mamíferos proposto por Gérard e Goldbeter (2011). A divisão celular ocorre quando o complexo ciclinaB/Cdk1(quínase dependente de ciclina) é totalmente degradado atingindo um valor mínimo. Neste ponto, a célula é divida em duas novas células filhas e cada uma irá conter a metade do conteúdo citoplasmático da célula mãe. As equações do modelo de base são válidas apenas se o volume celular, onde as reações ocorrem, é constante. Quando o volume celular não é constante, isto é, a taxa de variação do volume em relação ao tempo é explicitamente levada em consideração no modelo matemático, então as equações do modelo original não são mais válidas. Portanto, todas as equações foram modificadas a partir do princípio de conservação das massas para considerar um volume que varia ao longo do tempo. Por meio desta abordagem, o volume celular afeta todas as variáveis do modelo. Dois méto dos diferentes de simulação foram efetuados: determinista e estocástico. Na simulação estocástica, o volume afeta todos os parâmetros do modelo que possuem de alguma forma unidade molar, enquanto que no determinista, ele é incorporado nas equações diferen ciais. Na simulação determinista, as espécies bioquímicas podem estar em unidades de concentração, enquanto na simulação estocástica tais espécies devem ser convertidas para número de moléculas que são diretamente proporcional ao volume celular. Em um esforço para entender a influência da nova equação sobre o modelo uma análise de estabilidade foi feita. Isso esclarece como o novo parâmetro µ, fator de crescimento do volume celular, impacta na estabilidade do ciclo limite do modelo. Para encontrar a solução aproximada do modelo determinista, o método Runge Kutta de quarta ordem foi implementado. Já para o modelo estocástico, o método direto de Gillespie foi usado. Para concluir, um modelo mais preciso, em comparação ao modelo de base, foi desenvolvido ao levar em consideração a influência da taxa de variação do volume celular sobre o ciclo celular. / The main goal of this work is to add and analyse an equation that represents the volume in a dynamical model of the mammalian cell cycle proposed by Gérard and Gold beter (2011). The cell division occurs when the cyclinB/Cdk1 (cyclin-dependent kinase) complex is totally degraded and it reaches a minimum value. At this point, the cell is divided into two newborn daughter cells and each one will contain the half of the cyto plasmic content of the mother cell. The equations of our base model are valid only if the cell volume, where the reactions occur, is constant. Whether the cell volume is not constant, that is, the rate of change of its volume with respect to time is explicitly taken into account in the mathematical model, then the equations of the original model are no longer valid. Therefore, every equations were modified from the mass conservation prin ciple for considering a volume that changes with time. Through this approach, the cell volume affects all model variables. Two different dynamic simulation methods were ac complished: deterministic and stochastic. In the stochastic simulation, the volume affects every model’s parameters which have molar unit, whereas in the deterministic one, it is incorporated into the differential equations. In deterministic simulation, the biochemical species may be in concentration units, while in stochastic simulation such species must be converted to number of molecules which are directly proportional to the cell volume. In an effort to understand the influence of the new equation over the model an stability analysis was performed. This elucidates how the new parameter µ, cell volume growth factor, impacts the stability of the model’s limit cycle. In order to find the approximated solution of the deterministic model, the fourth order Runge Kutta method was implemen ted. As for the stochastic model, the Gillespie’s Direct Method was used. In conclusion, a more precise model, in comparison to the base model, was created for the cell cycle as it now takes into consideration the rate of change of the cell volume.