Mass gatherings stress local and global health care systems as they bring together individuals from all over the world that have very different health conditions. We firstly provide an overview of the concepts and results of mathematical epidemiology and public health. Secondly, we present an introduction to the mathematical modelling of measles using deterministic and stochastic approaches for both single and multiple populations. Lastly, we develop a model for mass gatherings and present an application to measles during the Hajj by studying an SIR deterministic metapopulation model with residency and its stochastic analogue. The models incorporate real world country data and time dependent movement and transmission rates, accounting for realistic volume of international travel and seasonality of measles activity. Numerical results for the deterministic system are presented. We conclude with a discussion on further work.

L’objectif de cette étude est de modéliser la digestion dans l’intestin grêle : le transport des aliments par les ondes péristaltiques, la dégradation par les enzymes endogènes et exogènes et l’absorption active et passive. Un modèle mécaniste basé sur les équations différentielles ordinaires a été utilisé pour représenter la digestion. Les équations décrivent l’évolution de la position et de la composition du bolus provenant de l’estomac. Nous montrons ensuite par les méthodes d’homogénéisation mathématiques que ce modèle peut être considéré comme une version macroscopique des modèles plus réalistes, qui contiennent des phénomènes biologiques à des échelles inférieures de l’intestin grêle. Enfin, nous étudions l’influence du changement de la structure de bolus sur la digestion en intégrant les fibres alimentaires dans sa composition. Les deux principales caractéristiques des fibres alimentaires qui interagissent avec la fonction de l’intestin grêle, à savoir, la viscosité et la capacité de rétention d’eau ont été modélisées. / The purpose of this study is to model the digestion in the small intestine : transport of the the bolus by the peristaltic waves, feedstuffs degradation according to the endogenous and exogenous enzymes and nutrients absorption. A mechanistic model based on ordinary differential equations is used to represent the digestion. The equations describe the evolution of the position and composition of the bolus of feedstuffs coming from the stomach. We prove by using the homogenization methods, that this model can be considered as a macroscopic version of more realistic models which contain the biological phenomena at lower scales of the small intestine. Finally, we investigate the digestion of a non-homogeneous feedstuffs matrix by integrating the dietary fibre in the bolus. The two main physiochemical characteristics of dietary fibre which interact with the function of the small intestine, i.e. viscosity and water holding capacity are modelled.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-05-30T23:14:12Z No. of bitstreams: 1 RomuloAlvesDeOliveira_TESE.pdf: 1869244 bytes, checksum: af0453c8083aee85607b79c3a17c1d4c (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-06-01T22:30:48Z (GMT) No. of bitstreams: 1 RomuloAlvesDeOliveira_TESE.pdf: 1869244 bytes, checksum: af0453c8083aee85607b79c3a17c1d4c (MD5) / Made available in DSpace on 2016-06-01T22:30:48Z (GMT). No. of bitstreams: 1 RomuloAlvesDeOliveira_TESE.pdf: 1869244 bytes, checksum: af0453c8083aee85607b79c3a17c1d4c (MD5) Previous issue date: 2015-01-27 / Atualmente os projetos de Ilumina??o P?blica (IP), ou seja, ruas, avenidas, pra?as, estacionamentos e similares s?o realizados com a utiliza??o de softwares comerciais ou livres, em geral, fornecidos por fabricantes ou grupos de fabricantes de produtos de ilumina??o, aplicando o M?todo Ponto a Ponto para o c?lculo dos n?veis de ilumina??o. Outros pontos em comum s?o: a falta de preocupa??o na redu??o dos custos dos projetos e a dificuldade em modificar as estruturas utilizadas, tais como: localiza??o e altura dos postes e lumin?rias, ?ngulo de inclina??o das lumin?rias, quantidade de lumin?rias por poste, entre outros. Qualquer altera??o nas estruturas ter? que ser feita manualmente, geralmente em um ambiente CAD, para depois obter os novos resultados e comparar com os anteriores. Para auxiliar nessa tarefa, ? proposta aqui a utiliza??o da Metaheur?stica Col?nia de Formigas, onde os par?metros e localiza??o das estruturas passam a ser definidos automaticamente, de forma a atender os n?veis de ilumina??o estabelecidos nas normas t?cnicas, al?m de otimizar o custo de material por unidade de ?rea. / The development of public lighting projects in Brazil must meet the standards established in Brazilian standards. Many of these projects is developed through the use of knowledge about "practical rules" practiced by the designers of this area. In some cases are also used computational tools offered, generally, by leading manufacturers of lamps/luminaires. These tools have served only as calculation tools, with some limitations, such as: are not able to verify compliance or not the parameters established by Brazilian standards, most of the luminaires offered in your database are not sold in Brazil, not have no concern about the analysis of the implementation costs of elaborate designs and, finally, present an enormous difficulty in performing tests on a large volume of possible projects. It is the goal of this thesis to develop a methodology and a computational tool for the development of public lighting projects based on genetic algorithm techniques that not only perform the calculations of these projects, but can also test several possible projects using in your database the luminaires marketed in Brazil, providing the user, as a solution, a set of projects that meet the Brazilian standards and classified according the implementation costs of each project. To adjust the proposed algorithm the following performance parameters were modified: number of individuals in the initial population; probability of achievement of the cross-over; probability of achievement of the mutation. A comparison of this method with the projects developed with the use of "practical rules" is performed for various types of existing roads. The results obtained using the proposed methodology and the developed computational tool show that the methodology, including the adjustments in performance parameters, is able to meet the objectives of the work.

The utilization of mathematical tools within biology and medicine has traditionally been less widespread compared to other hard sciences, such as physics and chemistry. However, an increased need for tools such as data processing, bioinformatics, statistics, and mathematical modeling, have emerged due to advancements during the last decades. These advancements are partly due to the development of high-throughput experimental procedures and techniques, which produce ever increasing amounts of data. For all aspects of biology and medicine, these data reveal a high level of inter-connectivity between components, which operate on many levels of control, and with multiple feedbacks both between and within each level of control. However, the availability of these large-scale data is not synonymous to a detailed mechanistic understanding of the underlying system. Rather, a mechanistic understanding is gained first when we construct a hypothesis, and test its predictions experimentally. Identifying interesting predictions that are quantitative in nature, generally requires mathematical modeling. This, in turn, requires that the studied system can be formulated into a mathematical model, such as a series of ordinary differential equations, where different hypotheses can be expressed as precise mathematical expressions that influence the output of the model. Within specific sub-domains of biology, the utilization of mathematical models have had a long tradition, such as the modeling done on electrophysiology by Hodgkin and Huxley in the 1950s. However, it is only in recent years, with the arrival of the field known as systems biology that mathematical modeling has become more commonplace. The somewhat slow adaptation of mathematical modeling in biology is partly due to historical differences in training and terminology, as well as in a lack of awareness of showcases illustrating how modeling can make a difference, or even be required, for a correct analysis of the experimental data. In this work, I provide such showcases by demonstrating the universality and applicability of mathematical modeling and hypothesis testing in three disparate biological systems. In Paper II, we demonstrate how mathematical modeling is necessary for the correct interpretation and analysis of dominant negative inhibition data in insulin signaling in primary human adipocytes. In Paper III, we use modeling to determine transport rates across the nuclear membrane in yeast cells, and we show how this technique is superior to traditional curve-fitting methods. We also demonstrate the issue of population heterogeneity and the need to account for individual differences between cells and the population at large. In Paper IV, we use mathematical modeling to reject three hypotheses concerning the phenomenon of facilitation in pyramidal nerve cells in rats and mice. We also show how one surviving hypothesis can explain all data and adequately describe independent validation data. Finally, in Paper I, we develop a method for model selection and discrimination using parametric bootstrapping and the combination of several different empirical distributions of traditional statistical tests. We show how the empirical log-likelihood ratio test is the best combination of two tests and how this can be used, not only for model selection, but also for model discrimination. In conclusion, mathematical modeling is a valuable tool for analyzing data and testing biological hypotheses, regardless of the underlying biological system. Further development of modeling methods and applications are therefore important since these will in all likelihood play a crucial role in all future aspects of biology and medicine, especially in dealing with the burden of increasing amounts of data that is made available with new experimental techniques. / Användandet av matematiska verktyg har inom biologi och medicin traditionellt sett varit mindre utbredd jämfört med andra ämnen inom naturvetenskapen, såsom fysik och kemi. Ett ökat behov av verktyg som databehandling, bioinformatik, statistik och matematisk modellering har trätt fram tack vare framsteg under de senaste decennierna. Dessa framsteg är delvis ett resultat av utvecklingen av storskaliga datainsamlingstekniker. Inom alla områden av biologi och medicin så har dessa data avslöjat en hög nivå av interkonnektivitet mellan komponenter, verksamma på många kontrollnivåer och med flera återkopplingar både mellan och inom varje nivå av kontroll. Tillgång till storskaliga data är emellertid inte synonymt med en detaljerad mekanistisk förståelse för det underliggande systemet. Snarare uppnås en mekanisk förståelse först när vi bygger en hypotes vars prediktioner vi kan testa experimentellt. Att identifiera intressanta prediktioner som är av kvantitativ natur, kräver generellt sett matematisk modellering. Detta kräver i sin tur att det studerade systemet kan formuleras till en matematisk modell, såsom en serie ordinära differentialekvationer, där olika hypoteser kan uttryckas som precisa matematiska uttryck som påverkar modellens output. Inom vissa delområden av biologin har utnyttjandet av matematiska modeller haft en lång tradition, såsom den modellering gjord inom elektrofysiologi av Hodgkin och Huxley på 1950‑talet. Det är emellertid just på senare år, med ankomsten av fältet systembiologi, som matematisk modellering har blivit ett vanligt inslag. Den något långsamma adapteringen av matematisk modellering inom biologi är bl.a. grundad i historiska skillnader i träning och terminologi, samt brist på medvetenhet om exempel som illustrerar hur modellering kan göra skillnad och faktiskt ofta är ett krav för en korrekt analys av experimentella data. I detta arbete tillhandahåller jag sådana exempel och demonstrerar den matematiska modelleringens och hypotestestningens allmängiltighet och tillämpbarhet i tre olika biologiska system. I Arbete II visar vi hur matematisk modellering är nödvändig för en korrekt tolkning och analys av dominant-negativ-inhiberingsdata vid insulinsignalering i primära humana adipocyter. I Arbete III använder vi modellering för att bestämma transporthastigheter över cellkärnmembranet i jästceller, och vi visar hur denna teknik är överlägsen traditionella kurvpassningsmetoder. Vi demonstrerar också frågan om populationsheterogenitet och behovet av att ta hänsyn till individuella skillnader mellan celler och befolkningen som helhet. I Arbete IV använder vi matematisk modellering för att förkasta tre hypoteser om hur fenomenet facilitering uppstår i pyramidala nervceller hos råttor och möss. Vi visar också hur en överlevande hypotes kan beskriva all data, inklusive oberoende valideringsdata. Slutligen utvecklar vi i Arbete I en metod för modellselektion och modelldiskriminering med hjälp av parametrisk ”bootstrapping” samt kombinationen av olika empiriska fördelningar av traditionella statistiska tester. Vi visar hur det empiriska ”log-likelihood-ratio-testet” är den bästa kombinationen av två tester och hur testet är applicerbart, inte bara för modellselektion, utan också för modelldiskriminering. Sammanfattningsvis är matematisk modellering ett värdefullt verktyg för att analysera data och testa biologiska hypoteser, oavsett underliggande biologiskt system. Vidare utveckling av modelleringsmetoder och tillämpningar är därför viktigt eftersom dessa sannolikt kommer att spela en avgörande roll i framtiden för biologi och medicin, särskilt när det gäller att hantera belastningen från ökande datamängder som blir tillgänglig med nya experimentella tekniker.

New algorithms for the matrix exponential and its Fréchet derivative are presented. First, we derive a new scaling and squaring algorithm (denoted expm[new]) for computing eA, where A is any square matrix, that mitigates the overscaling problem. The algorithm is built on the algorithm of Higham [SIAM J.Matrix Anal. Appl., 26(4): 1179-1193, 2005] but improves on it by two key features. The first, specific to triangular matrices, is to compute the diagonal elements in the squaring phase as exponentials instead of powering them. The second is to base the backward error analysis that underlies the algorithm on members of the sequence {||Ak||1/k} instead of ||A||. The terms ||Ak||1/k are estimated without computing powers of A by using a matrix 1-norm estimator. Second, a new algorithm is developed for computing the action of the matrix exponential on a matrix, etAB, where A is an n x n matrix and B is n x n₀ with n₀ << n. The algorithm works for any A, its computational cost is dominated by the formation of products of A with n x n₀ matrices, and the only input parameter is a backward error tolerance. The algorithm can return a single matrix etAB or a sequence etkAB on an equally spaced grid of points tk. It uses the scaling part of the scaling and squaring method together with a truncated Taylor series approximation to the exponential. It determines the amount of scaling and the Taylor degree using the strategy of expm[new].Preprocessing steps are used to reduce the cost of the algorithm. An important application of the algorithm is to exponential integrators for ordinary differential equations. It is shown that the sums of the form $\sum_{k=0}^p\varphi_k(A)u_k$ that arise in exponential integrators, where the $\varphi_k$ are related to the exponential function, can be expressed in terms of a single exponential of a matrix of dimension $n+p$ built by augmenting $A$ with additional rows and columns. Third, a general framework for simultaneously computing a matrix function, $f(A)$, and its Fréchet derivative in the direction $E$, $L_f(A,E)$, is established for a wide range of matrix functions. In particular, we extend the algorithm of Higham and $\mathrm{expm_{new}}$ to two algorithms that intertwine the evaluation of both $e^A$ and $L(A,E)$ at a cost about three times that for computing $e^A$ alone. These two extended algorithms are then adapted to algorithms that simultaneously calculate $e^A$ together with an estimate of its condition number. Finally, we show that $L_f(A,E)$, where $f$ is a real-valued matrix function and $A$ and $E$ are real matrices, can be approximated by $\Im f(A+ihE)/h$ for some suitably small $h$. This approximation generalizes the complex step approximation known in the scalar case, and is proved to be of second order in $h$ for analytic functions $f$ and also for the matrix sign function. It is shown that it does not suffer the inherent cancellation that limits the accuracy of finite difference approximations in floating point arithmetic. However, cancellation does nevertheless vitiate the approximation when the underlying method for evaluating $f$ employs complex arithmetic. The complex step approximation is attractive when specialized methods for evaluating the Fréchet derivative are not available.

Le fonctionnement d'un neurone, unité fondamentale du système nerveux, intéresse de nombreuses disciplines scientifiques. Il existe ainsi des modèles mathématiques qui décrivent leur comportement par des systèmes d'EDO ou d'EDP. Plusieurs de ces modèles peuvent ensuite être couplés afin de pouvoir étudier le comportement de réseaux, systèmes complexes au sein desquels émergent des propriétés. Ce travail présente, dans un premier temps, les principaux mécanismes régissant ce fonctionnement pour en comprendre la modélisation. Plusieurs modèles sont alors présentés, jusqu'à celui de FitzHugh-Nagumo (FHN), qui présente une dynamique très intéressante.C'est sur l'étude théorique mais également numérique de la dynamique asymptotique et transitoire du modèle de FHN en EDO, que se concentre la seconde partie de cette thèse. A partir de cette étude, des réseaux d'interactions d'EDO sont construits en couplant les systèmes dynamiques précédemment étudiés. L'étude du phénomène de synchronisation identique au sein de ces réseaux montre l'existence de propriétés émergentes pouvant être caractérisées par exemple par des lois de puissance. Dans une troisième partie, on se concentre sur l'étude du système de FHN dans sa version EDP. Comme la partie précédente, des réseaux d'interactions d'EDP sont étudiés. On entreprend dans cette partie une étude théorique et numérique. Dans la partie théorique, on montre l'existence de l'attracteur global dans l'espace L2(Ω)nd et on donne des conditions suffisantes de synchronisation. Dans la partie numérique, on illustre le phénomène de synchronisation ainsi que l'émergence de lois générales telles que les lois puissances ou encore la formation de patterns, et on étudie l'effet de l'ajout de la dimension spatiale sur la synchronisation. / The neuron, a fundamental unit in the nervous system, is a point of interest in many scientific disciplines. Thus, there are some mathematical models that describe their behavior by ODE or PDE systems. Many of these models can then be coupled in order to study the behavior of networks, complex systems in which the properties emerge. Firstly, this work presents the main mechanisms governing the neuron behaviour in order to understand the different models. Several models are then presented, including the FitzHugh-Nagumo one, which has a interesting dynamic. The theoretical and numerical study of the asymptotic and transitory dynamics of the aforementioned model is then proposed in the second part of this thesis. From this study, the interaction networks of ODE are built by coupling previously dynamic systems. The study of identical synchronization phenomenon in these networks shows the existence of emergent properties that can be characterized by power laws. In the third part, we focus on the study of the PDE system of FHN. As the previous part, the interaction networks of PDE are studied. We have in this section a theoretical and numerical study. In the theoretical part, we show the existence of the global attractor on the space L2(Ω)nd and give the sufficient conditions for identical synchronization. In the numerical part, we illustrate the synchronization phenomenon, also the general laws of emergence such as the power laws or the patterns formation. The diffusion effect on the synchronization is studied.

Three- and four-derivative k-step Hermite-Birkhoff-Obrechkoff (HBO) methods are constructed for solving stiff systems of first-order differential equations of the form y'= f(t,y), y(t0) = y0. These methods use higher derivatives of the solution y as in Obrechkoff methods. We compute their regions of absolute stability and show the three- and four-derivative HBO are A( 𝜶)-stable with 𝜶 > 71 ° and 𝜶 > 78 ° respectively. We conduct numerical tests and show that our new methods are more efficient than several existing well-known methods.

No description available.

The goal of this thesis was to propose an image segmentation method, which is capable of effective segmentation process with small datasets. Recently published ODE neural network was used for this method, because its features should provide better generalization in case of tasks with only small datasets available. The proposed ODE-UNet network was created by combining UNet architecture with ODE neural network, while using benefits of both networks. ODE-UNet reached following results on ISBI dataset: Rand: 0,950272 and Info: 0,978061. These results are better than the ones received from UNet model, which was also tested in this thesis, but it has been proven that state of the art can not be outperformed using ODE neural networks. However, the advantages of ODE neural network over tested UNet architecture and other methods were confirmed, and there is still a room for improvement by extending this method.

L'un des enjeux majeurs actuellement en neurosciences est l'élaboration de modèles computationnels capables de reproduire les données obtenues expérimentalement par des méthodes d'imagerie et permettant l'étude de la relation structure-fonction dans le cerveau. Les travaux de modélisation dans cette thèse se situent à deux échelles et l'analyse des modèles a nécessité le développement d'outils théoriques et numériques dédiés. À l'échelle locale, nous avons proposé un nouveau modèle d'équations différentielles ordinaires générant des activités neuronales, caractérisé et classifié l'ensemble des comportements générés, comparé les sorties du modèle avec des données expérimentales et identifié les structures dynamiques sous-tendant la génération de comportements pathologiques. Ce modèle a ensuite été couplé bilatéralement à un nouveau compartiment modélisant les dynamiques de neuromédiateurs et leurs rétroactions sur l'activité neuronale. La caractérisation théorique de l'impact de ces rétroactions sur l'excitabilité a été obtenue en formalisant l'étude des variations d'une valeur de bifurcation en un problème d'optimisation sous contrainte. Nous avons enfin proposé un modèle de réseau, pour lequel la dynamique des noeuds est fondée sur le modèle local, incorporant deux couplages: neuronal et astrocytaire. Nous avons observé la propagation d'informations différentiellement selon ces deux couplages et leurs influences cumulées, révélé les différences qualitatives des profils d'activité neuronale et gliale de chaque noeud, et interprété les transitions entre comportements au cours du temps grâce aux structures dynamiques identifiées dans les modèles locaux. / A current issue in neuroscience is to elaborate computational models that are able to reproduce experimental data recorded with various imaging methods, and allowing us to study the relationship between structure and function in the human brain. The modeling objectives of this work are two scales and the model analysis need the development of specific theoretical and numerical tools. At the local scale, we propose a new ordinary differential equations model generating neuronal activities. We characterize and classify the behaviors the model can generate, we compare the model outputs to experimental data and we identify the dynamical structures of the neural compartment underlying the generation of pathological patterns. We then extend this approach to a new neuro-glial mass model: a bilateral coupling between the neural compartment and a new one modeling the impact of astrocytes on neurotransmitter concentrations and the feedback of these concentrations on neural activity is developed. We obtain a theoretical characterization of these feedbacks impact on neuronal excitability by formalizing the variation of a bifurcation value as a problem of optimization under constraint. Finally, we propose a network model, which node dynamics are based on the local neuro-glial mass model, embedding a neuronal coupling and a glial one. We numerically observe the differential propagations of information according to each of these coupling types and their cumulated impact, we highlight qualitatively distinct patterns of neural and glial activities of each node, and link the transitions between behaviors with the dynamical structures identified in the local models.