Orientador: João Manuel Marques Cordeiro / Banca: Rosangela da Silva de Laurentiz / Banca: Ricardo Gargano / Resumo: Utilizando a simulação molecular com método Monte Carlo estudou-se neste trabalho as características estruturais e termodinâmicas dos líquidos puros N-Metilformamida (NMF) e Tetrahidrofurano (THF), bem como de suas misturas em diferentes concentrações. Realizou- se também um estudo das características estruturais e termodinâmicas de misturas N- Metilformamida-Água, em diferentes concentrações, para fins de comparação. Os resultados estruturais obtidos para os líquidos puros foram comparados com resultados experimentais de difratometria de nêutrons com posterior refinamento estrutural por potencial empírico (EPSR). Na simulação computacional foi utilizado o programa DIADORIM. As simulações foram realizadas no ensemble estatístico NpT, ou seja, com número de moléculas, pressão e temperatura constantes. O número total de moléculas na caixa de simulação foi de 400, à pressão de 1 atm e à temperatura de 298,15K. A concordância entre os resultados experimentais e teóricos dos líquidos puros foi boa, o que permitiu prosseguir sabendo que os resultados obtidos para as misturas seriam confiáveis. Em relação aos líquidos puros foi mostrada a presença de ligações de hidrogênio na estrutura da NMF e da água, sendo que estes líquidos apresentam-se bem estruturados. Conclui-se que o THF apresenta estrutura ―desorganizada‖.Os resultados obtidos para a mistura NMF-THF sugerem a existência de ligações de hidrogênio entre estas moléculas de NMF e THF no líquido, e os valores de suas energias de ligação de hidrogênio, segundo os resultados obtidos, apresentam valores semelhantes à interação NMF-NMF, o que juntamente com a observação das propriedades termodinâmicas estudadas neste trabalho indicam um comportamento ideal da solução. Também pode ser observado... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The structural and thermodynamic properties of pure liquids N-methylformamide (NMF) and Tetrahydrofuran (THF), as well as their mixture in different concentrations were studied through Monte Carlo simulation. The structural and thermodynamic properties of N- Methylformamide-water in different concentrations was also investigated, aiming comparisons. The structural results obtained to pure liquids were compared to experimental results of neutron diffratometry with empirical potential structure refinement (EPSR). For the computational simulation, was used the DIADORIM program. The simulations were carried out on the NpT statistical ensemble. The total number of molecules in the simulation box was 400, over a 1 atm pressure and 298,15K temperature. The accordance between the experimental and theoretical structure of pure liquids was good, what permits to infer on the reliability of the results obtained for the mixtures. Regarding to pure liquids it was shown the presence of hydrogen bonds in NMF and water structures, being that those liquids are well structured. It was not observed the presence of hyrogen bonds among the molecules of THF presenting this liquid a disorganized structure. The obtained results of the NMF-THF mixture, suggest the presence of hydrogen bonds between those molecules in the liquid. It was also observed by the obtained results that this liquid is well structured. The mixture NMF-water, as well as the mixture NMF-THF, are well structured, but either NMF and water are proton acceptors and donors and leads to the presence of two hydrogen connections between NMF and water molecules. The study of structural and thermodynamic properties of the mixture NMF-water, show a proper behavior for the solution, as in the case THF-NMF, but when comparing NMF-NMF in both cases, the water appeared to interfere more on the interaction between NMF molecules than THF ones / Mestre

Estudamos o comportamento dinâmico do algoritmo de Wolff no modelo de Ising diluído com correlação de sítios e ligações (modelo SBC). Nosso objetivo principal foi estudar a performance deste algoritmo em um sistema onde além da desordem à presença de impurezas não magnéticas, houvesse mais um parâmetro presente, a correlação espacial. Além disto foi possível obter o diagrama de fases para o modelo, o qual possibilita entendermos um pouco o efeito da desordem e da correlação no sistema.Verificamos que o diagrama de fases por nós obtidos está em boa concordância com os dados experimentais obtidos com o composto magnético Knip Mg1-pF3, o qual foi a motivação para o modelo SBC. O estudo do comportamento dinâmico nos possibilitou entender um pouco mais como o algoritmo de Wolff se comporta quando submetido a sistemas mais complexos, como é o caso do modelo em estudo, verificamos uma melhor performance deste algoritmo à medida que tanto a diluição quanto a correlação é aumentada. Esse comportamento é oposto àquele verificado nos algoritmos locais. Essa melhor performance do algoritmo de Wolff quando submetido a tais sistemas é bastante positivo, pois isto possibilita obtermos medidas de quantidades físicas de interesse de forma mais precisa, pois há uma redução drástica da correlação estatística entre configurações produzidas por esta dinâmica. / We extend the Wolff Algorithm to include correlated spin interactions in diluted magnetic systems. This algorithm is applied to study the site-bond-correlated Ising model on a two-dimensional square lattice. We use a finite-size scaling procedure to obtain the phase diagram in the temperature-concentration space. Our results are in excellent agreement with the experimental data for the Knip Mg1-pF3, compound. We also present the critical dynamical behavior of the Wolff algorithm for this system. We have verified that the autocorrelation time diminishes in the presence of dilution and correlation, showing that the Wolff algorithm performs even better in such situations. This behavior is completely different from those exhibited by the single spin-flips algorithms.

O processo de radioterapia consiste em várias etapas, iniciando na indicação pelo médico. O plano de tratamento passa então por um processo denominado simulação, onde é adquirida uma série de imagens por tomografia computadorizada que são transferidas para o sistema de planejamento, onde a delineação dos volumes alvos e tecidos normais adjacentes serão realizadas. Após a delineação desses volumes, no sistema de planejamento são colocados os campos de irradiação e a dose desejada conforme prescrição médica. O sistema de planejamento calcula então a dose que o volume alvo e os tecidos adjacentes poderão receber. Se estas doses estão dentro dos padrões aceitáveis, o planejamento então é aprovado e enviado ao acelerador linear para a execução do tratamento. Antes da execução do tratamento, é realizada uma imagem, seja através de filme radiográfico ou digitalmente, para avaliar a posição no paciente na mesa de tratamento. Se a localização do paciente está correta, a dose é então liberada. Esse protocolo de aquisição de imagem é denominado como Radioterapia Guiada por Imagem (IGRT). A quantidade de radiografias de posicionamento segue um protocolo definido conforme a região a ser irradiada. Como resultado deste procedimento, sabe-se que uma determinada dose adicional é recebida pelos pacientes, tornando-se um fator importante a ser determinado. Esta avaliação foi realizada através da simulação de Monte Carlo, utilizando o código MCNP. Para isso foi realizada primeiramente toda a caracterização da fonte de raios X com uso de câmaras de ionização e dosimetros TL juntamente com as simulações no MCNP. Após essa caracterização, as imagens e as estruturas do planejamento radioterápico foram convertidas para serem utilizadas no código MCNP. Para que as doses fossem calculadas nos principais órgãos de risco no tratamento de próstata: bexiga, reto e cabeças de fêmur direita e esquerda. / The process of radiotherapy treatment consists of several stages, starting from the statement given by the physician. The treatment planning undergoes a process called simulation, where a series of computed tomography images is acquired and transferred to the treatment planning system, where the delineation of target volumes and adjacent normal tissues will be performed. After the delineation of these volumes, then irradiation fields and dose precribed by the physician are placed in the treatment planning system. It calculates the dose that target volume and surrounding tissues are receiving. If the doses are within acceptable standard values, then the design is approved and submitted to the linear accelerator for the treatment course. Before treatment course, an image is performed, either by radiographic or digital film, in order to evaluate (check) the patient position on the treatment table. If the patient position is correct, the treatment is realized. This image acquisition protocol is called Image-Guided Radiotherapy (IGRT). The amount of radiographic positioning follow a protocol defined for the region to be treated. As a result of this procedure, it is known that a specific additional dose is received by the patient, becoming an important factor to be determined. In this work, this additional dose evaluation was performed by the Monte Carlo simulation using the MCNP algorithm. The characterization of the entire X-ray source was primarily realized with ionization chamber thermoluminescent dosimeters and simulations with the MCNP code. After the X-ray tube characterization, images and the structures for the radiotherapy planning were converted to be used in the MCNP code for dose calculation at the main organs at risk during a prostate treatment: bladder, rectum and femoral heads right and left.

Granger (1980) found that the aggregation of m short-memory AR(1) processes yields a long-memory process. Thereby he assumed m -> ∞, Gaussian shape and betadistributed AR(1) parameters over (0; 1). To test hypotheses that long memory in climate time series comes from aggregation, the finding of Granger (1980) cannot be directly applied. First, the number of \"microclimatic\" processes to be aggregated is finite. Second, climatic processes often produce right-skewed data. Third, the AR(1) parameters of the microclimatic processes could be restricted to a narrower interval than (0; 1). We therefore perform Monte Carlo simulations to study aggregation in climate time series under realistic conditions. The long-memory parameter, H, is estimated by fitting an ARFIMA model to various types of aggregations. Our results are as follows. First, for m above a few hundred, H approaches saturation. Second, the distributional shape has little influence, as noted by Granger (1980). Third, the upper limit of the interval for the AR(1) parameter has a strong influence on the saturation value of H, as noted by Granger (1980). / Granger (1980) fand heraus, dass die Summe von m schwach seriell abhängigen AR(1)-Prozessen einen stark seriell abhängigen Prozess ergibt. Er nahm dabei an, dass m -> ∞ geht, die Verteilungen Gaußsch sind und die AR(1)-Parameter beta-verteilt über (0; 1) sind. Um die Hypothese zu testen, daß starke serielle Abhängigkeit in Klimazeitreihen von dieser \"Aggregation\" rührt, kann das Ergebnis von Granger (1980) jedoch nicht direkt angewendet werden. Erstens: die Anzahl \"mikroklimatischer\", zu summierender Prozesse is endlich. Zweitens: Klimaprozesse erzeugen oft rechtsschief verteilte Daten. Drittens: die AR(1)-Parameter der mikroklimatischen Prozesse mögen auf ein engeres Intervall begrenzt sein als (0; 1). Wir f¨uhren deshalb Monte-Carlo-Simulationen durch, um die Aggregation in Klimazeitreihen für realistische Bedingungen zu studieren. Der Parameter H, der die starke serielle Abhängigkeit beschreibt, wird geschätzt durch die Anpassung eines ARFIMA-Modelles an unterschiedliche Aggregations-Typen. Unsere Ergebnisse sind wie folgt. Erstens: für m oberhalb einiger hundert erreicht H S¨attigung. Zweitens: die Verteilungsform hat geringen Einfluß, wie von Granger (1980) bemerkt. Drittens: die obere Grenze des Intervalles für den AR(1)-Parameter hat einen starken Einfluß auf den Sättigungwert von H, wie von Granger (1980) bemerkt.

Depuis le début du XX siècle, la thématique de transport a concentré l’attentionde nombreux chercheurs. L’objectif étant alors d’identifier et de comprendre lesdifférentes sources de diffusions prenant part à la résistivité de la matière. Les deuxpremières sources diffusives mises en évidence ont été les phonons dépendant de latempérature, et les défauts du réseau cristallin. Dans les années 1950, l’étude des semiconducteursa fait émerger une troisième source de diffusion, la diffusion magnétique.Dès la mise en évidence du rôle joué par le magnétisme sur la résistivité de certainsmatériaux, il a rapidement été établi que la résistivité magnétique R est tributaire dela stabilité de l’ordre magnétique du réseau. A basse température T, la diffusion desélectrons s’ opère par l e biais des ondes de spins. A haute température, R est proportionnelleaux corrélations spin-spin. Cependant, les mécanismes de diffusion ayant lieuau voisinage de la température de transition ordre/désordre magnétique restent encoremal comprise. L’objectif de cette thèse a consisté à étudier ce problème à l’aide d’uneapproche nouvelle basée sur la simulation Monte Carlo. En effet, les théories existantessont toutes construites avec des hypothèses sur les mécanismes à l’origine du comportementde résistance tels que : fonction corrélation spin-spin, longueur de localisation.Elles utilisent beaucoup d’approximations au cours du calcul telles que théorie du champmoyen, approximation du temps de relaxation, la portée des fonctions de corrélation. Lesprincipaux handicaps de ces théories sont de n’être valables que pour certaines gammesde températures, et d’être tributaires du type de magnétisme porté par les réseaux cristallins.Notre approche offre quant à elle une procédure unifiée concernant l’étude desrésistivités magnétiques fonction de la température. Cette méthode peut s’appliquer `atout type de matériaux, tout ordre magnétique (ferromagnétique, antiferromagnétique,ferrimagnétique, verre de spin, ...), tout type de modèle de spins (Ising, Heisenberg, XY,...), enfin tout type de réseau cristallin. Seule la connaissance du Hamiltonien permet defaire la simulation, et de reproduire des mesures expérimentales avec la possibilité d’unecomparaison quantitative.Dans un premier temps, nous traitons de structures ferromagnétiques et interprétons les différents mécanismes de diffusion en fonction de la température. Nousétendons ensuite l´étude aux systèmes antiferromagnétiques, frustrés et non-frustrés. Cessystèmes n’ont fait l’objet que de peu d’études. Dans le cas des systèmes antiferromagnétiques non-frustrés, nous sommes en mesure de contredire une prédiction théoriquefaite par Haas en 1968, concernant la forme de la résistance magnétique à la transition dephase . Dès lors, nous nous consacrerons à l’étude des mécanismes de transport dansdes systèmes antiferromagnétiques frustrés. Ces travaux ont permis de mettre en évidencedes comportements nouveaux des transitions de phases des résistances magnétiques : nousmontrons que ces résistances subissent une transition du premier ordre , mais qu’ilest également possible par le contrôle d’un paramètre du modèle, de choisir le sens de latransition : des hautes résistances vers les basses résistances ou inversement .Pour finir, nous confrontons nos résultats de simulations avec des mesures expérimentalesen réalisant une étude de transport sur un matériau semiconducteur antiferromagnétique :MnTe. Il résulte de cette étude un bon accord entre nos résultats de simulations et lesmesures expérimentales . / ....

Les protocoles de traitement du cancer par Radiothérapie Conformationnelle par Modulation d'Intensité (RCMI) ciblent avec une précision de plus en plus grande la tumeur. Pour cela, ils nécessitent des informations anatomiques précises du patient juste avant le traitement, qui peuvent d'être obtenues à l'aide de systèmes d'imagerie embarqués sur l'accélérateur linéaire médical délivrant le faisceau de traitement. Ces systèmes, composés d'un tube à rayons X et d'un détecteur 2D planaire, sont appelés kV-Cone Beam CT (kV-CBCT). Aujourd'hui, leur usage est très fortement répandu dans le cadre des traitements par RCMI. Cependant, ces examens kV-CBCT sont responsables d'une dose de rayonnements ionisants additionnelle qui est loin d'être négligeable et pouvant d'être à l'origine de l'apparition d'effets secondaires, tels que des cancers radio-induits chez les patients traités. Au cours de cette thèse, un simulateur basé sur la méthode de Monte-Carlo a été développé permettant ainsi d'estimer avec précision les doses délivrées aux organes lors des examens d'imagerie kV-CBCT. Cet outil a ensuite été utilisé afin d'étudier différentes stratégies de prise en compte clinique de ces doses additionnelles. L'étude présentée dans ce manuscrit propose notamment une méthode rapide d'estimation des niveaux de doses délivrés aux organes prenant en compte la morphologie de chaque patient. Cette stratégie a été développée à partir d'une cohorte de 50 patients incluant 40 enfants et 10 adultes. Ces travaux ont été réalisés en collaboration avec l'unité de physique médicale du Centre Eugène Marquis à Rennes qui a fourni les données cliniques nécessaires à l'étude. / Protocols for cancer treatment using intensity-modulated radiation therapy (IMRT) allow to target the tumor with an increased precision. They require accurate anatomical information of the patient just before the treatment, which can be obtained using on-board imaging systems mounted on the medical linear accelerator delivering the treatment beam. These systems, composed of an X-ray tube and a 2D planar detector, are called kV-Cone Beam CT (kV-CBCT). Nowadays, they are widely used in the context of IMRT treatments. However, these kV-CBCT examinations are also responsible for an additional dose of ionizing radiations which is far to be negligible and could be the cause for secondary effects, such as radiation-induced second cancers for treated patients. During this PhD work, a simulator based on the Monte Carlo method was developed in order to calculate accurately the doses delivered to organs during kV-CBCT examinations. Then, this tool was used to study several strategies to take in account for the imaging additional doses in clinical environment. The study reported here includes, in particular, a fast and personalized method to estimate the doses delivered to organs. This strategy was developed using a cohort of 50 patients including 40 children and 10 adults. This work has been done in collaboration with the medical physics unit of the Eugène Marquis medical center in Rennes, which has collected the clinical data used for this study.

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Bayesian analysis is definitely different from the classic statistical methods. Although, both of them use subjective ideas, it is used in the selection of models in the classic statistical methods, rather than as an explicit part in Bayesian models, which allows the combination of subjective ideas with the data collected, update the prior information and improve inferences. Drastic growth of Bayesian applications indicates it becomes more and more popular, because the advent of computational methods (e.g., MCMC) renders sophisticated analysis. In Bayesian framework, the flexibility and generality allows it to cope with very complex problems. One big obstacle in earlier Bayesian analysis is how to sample from the usually complex posterior distribution. With modern techniques and fast-developed computation capacity, we now have tools to solve this problem. We discuss Acceptance-Rejection sampling, importance sampling and then the MCMC methods. Metropolis-Hasting algorithm, as a very versatile, efficient and powerful simulation technique to construct a Markov Chain, borrows the idea from the well-known acceptance-rejection sampling to generate candidates that are either accepted or rejected, but then retains the current values when rejection takes place (1). A special case of Metropolis-Hasting algorithm is Gibbs Sampler. When dealing with high dimensional problems, Gibbs Sampler doesn’t require a decent proposal distribution. It generates the Markov Chain through univariate conditional probability distribution, which greatly simplifies problems. We illustrate the use of those approaches with examples (with R codes) to provide a thorough review. Those basic methods have variants to deal with different situations. And they are building blocks for more advanced problems. This report is not a tutorial for statistics or the software R. The author assumes that readers are familiar with basic statistical concepts and common R statements. If needed, a detailed instruction of R programming can be found in the Comprehensive R Archive Network (CRAN): http://cran.R-project.org / text

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In structural equation models, researchers often examine two types of causal effects: direct and indirect effects. Direct effects involve variables that "directly" influence other variables, whereas indirect effects are transmitted via intervening variables. While researchers have paid considerable attention to the distribution of sample direct effects, the distribution of sample indirect effects has only recently been considered. Using the (delta) method (Rao, 1973), Sobel (1982) derived the asymptotic distribution for estimators of indirect effects in recursive systems. Sobel (1986) then derived the asymptotic distribution for estimators of total indirect effects in the Joreskog covariance structure model (Joreskog, 1977). This study examined the applicability of the large sample theory described by Sobel (1986) in small samples. Monte Carlo methods were used to evaluate the behavior of estimated total indirect effects in sample sizes of 50, 100, 200, 400, and 800. Two models were used in the analysis. Model 1 was a nonrecursive model with latent variables, feedback, and functional constraints among the effects (Duncan, Haller, & Portes, 1968; Sobel, 1986). Model 2 was a recursive model with observable variables (Duncan, Featherman, & Duncan, 1972). In addition, variations in these models were studied by randomly increasing and decreasing model parameters. The principal findings of the study suggest certain guidelines for researchers who use Sobel's procedures to evaluate total indirect effects in structural equation models. In order for the behavior of the estimates to approximate the asymptotic properties, sample sizes of 400 or more are indicated for nonrecursive systems similar to Model 1, and for recursive systems such as Model 2, sample sizes of 200 or more are suggested. At these sample sizes, researchers can expect sample indirect effects to be accurate point estimators, and confidence intervals for the effects to behave as theory predicts. A caveat to the above guidelines is that, when the total indirect effects are "small" in magnitude, relative to the scale of the model, convergence to the asymptotic properties appears to be very slow. Under these conditions, sampling distributions for the "smaller" valued estimates were positively skewed. This caused estimates to be significantly different from true values, and confidence intervals to behave contrary to theoretical expectations.