Many complex systems arising from nature and human society can be described as complex networks. In this dissertation, on the basis of complex network theory, we pay attention to the topological structure of complex network and the dynamics on it. We established models to investigate the influences of the structure on the dynamics of networks and to shed light on some peculiar properties of complex systems. This dissertation includes four parts. In the first part, the empirical properties (degree distribution, clustering coefficient, diameter, and characteristic path length) of urban road network of Le Mans city in France are studied. The degree distribution shows a double power-law which we studied in detail. In the second part, we propose two models to investigate the possible mechanisms leading to the deviation from simple power law. In the first model, probabilistic addition of nodes and links, and rewiring of links are considered; in the second one, only random and preferential link growth is included. The simulation results of the modelling are compared with the real data. In the third part,the probabilistic uncertainty behavior of double power law distribution is investigated. The network optimization and optimal design of scale free network to random failures are discussed from the viewpoint of entropy maximization. We defined equilibrium network ensemble as stationary ensembles of graphs by using some thermodynamics like notions such as "energy", "temperature", "free energy" for network. In the forth part, an union-division model is established to investigate the time evolution of certain networks like cultural or economical networks. In this model, the nodes represent, for example, the cultures. Several quantities such as richness, age, identity, ingredient etc. are used to parameterize the probabilistic evolution of the network. The model offers a long term view on the apparently periodic dynamics of an ensemble of cultural or economic entities in interaction.

O presente trabalho consiste em uma extensão do sistema FreeFlow-2D para simular escoamentos de fluidos não newtonianos bidimensionais com superfí cies livres, onde o fluido é descrito pelos modelos de Cross ou o modelo ``power-law\'\'. O método numérico empregado é o método GENSMAC. As equações governantes são aproximadas pelo método de diferenças finitas em uma malha deslocada e partículas marcadoras são utilizadas para a visualização do escoamento e localização da superfície livre. Resultados numéricos são apresentados. Em particular, a presente implementação é validada comparando-se a solução numérica com uma solução analítica / This work presents an extention of the Freeflow-2D system to non-Newtonian free surface flows. The governing equations are solved by the finite difference method on a staggered grid. Marker particles are used to describe the fluid providing the location and the visualization of the free surface. The methodology employed is based on the GENSMAC method. The fluid is modelled by the Cross and power-law models. Numerical examples are presented. The code is validated by making a comparison between analytical and numerical solutions

Um sistema reparável opera sob uma estratégia de manutenção que exige ações de recuperação preventiva em tempos pré-definidos e ações de reparo quando ocorre a perda de função do sistema. A manutenção preventiva (MP) é programada periodicamente e muitas vezes possui um intervalo de tempo fixo para ações. No entanto, as atividades de MP podem não restaurar o sistema para uma condição similar ao início de vida deste, mas para uma situação intermediária. Nesse caso, a MP é denominada de imperfeita. Além disso, ao longo da vida do sistema, são executados diferentes planos de manutenção com condições e atividades distintas que podem afetar a intensidade de falha de diferentes maneiras. Para modelar essas características da MP em um sistema reparável, propõe-se uma nova classe de modelo de fator de melhoria, denominado fator de melhoria variável que possibilita a modelagem da situação de manutenção perfeita. A formulação da função de verossimilhança foi desenvolvida para estimação dos parâmetros bem como desenvolvidos testes de verificação da qualidade de ajuste, intervalos de confiança para os parâmetros e otimização da periodicidade de realização da MP com base no enfoque dos novos modelos propostos. Os resultados foram aplicados em dados reais e verificou-se uma parametrização mais flexível a MP imperfeita e maior versatilidade nas análises de confiabilidade do sistema quando utilizado os novos modelos. / A repairable system operates under a maintenance strategy that calls for preventive repair actions at prescheduled times and the repair actions that restore system when failure occurs. The preventive maintenance (PM) is scheduled periodically and it often holds a fixed time interval for PM actions. However, PM activities are generally imperfect and cannot restore the system to as good as new condition but to an intermediate situation, which is called imperfect PM. In addition, throughout system life are implemented diverse maintenance policies with different activities and conditions that may affect the failure intensity in different ways. To model these PM characteristics, proposes a new model class of improvement factor called variable improvement factor that also enables modeling perfect maintenance situation. The likelihood function is developed for parameter estimation as well as goodness-of-fit tests and confidence intervals for the parameters are developed, and optimization of the PM intervals based on the proposed models is presented. The proposed model was applied to a data set and a more flexible parameterization for imperfect PM and greater versatility in the system reliability analysis were verified with the use of the new model.

Multiphase transport phenomena primarily encompass the fundamental principles and applications concerning the systems where overall dynamics are precept by phase change evolution. On the other hand, multiscale transport phenomena essentially corroborate to a domain where the transport characteristics often contain components at disparate scales. Relevant examples as appropriate to multiphase and multiscale thermofluidic transport phenomena comprise solid-liquid phase change during conventional solidification process and hydrodynamics through narrow confinements. The additional effect of superimposed magnetic field over such multiphase and multiscale systems may give rise to intriguing transport characteristics, significantly unique in nature as compared to flows without it. The present investigation focuses on multiphase, multi-scale transport phenomena in physical systems subjected to the superimposed magnetic field, considering four important and inter-linked aspects. To begin with, for a multiphase system concerning binary alloy solidification, a normal mode linear stability analysis has been carried out to investigate stationary and oscillatory convective stability in the mushy layer in the presence of external magnetic field. The stability results indicate that the critical Rayleigh number for stationary convection shows a linear relationship with increasing Ham (mush Hartmann number). Magnetohydrodynamic effect imparts a stabilizing influence during stationary convection. In comparison to that of stationary convective mode, the oscillatory mode appears to be critically susceptible at higher values of  (a function of the Stefan number and concentration ratio), and vice versa for lower  values. Analogous to the behaviour for stationary convection, the magnetic field also offers a stabilizing effect in oscillatory convection and thus influences global stability of the mushy layer. Increasing magnetic strength shows reduction in the wavenumber and in the number of rolls formed in the mushy layer. In multiscale paradigm, the combined electroosmotic and pressure-driven transport through narrow confinements have been firstly analyzed with an effect of spatially varying non–uniform magnetic field. It has been found that a confluence of the steric interactions with the degree of wall charging (zeta potential) may result in heat transfer enhancement, and overall reduction in entropy generation of the system under appropriate conditions. In particular, it is revealed that a judicious selection of spatially varying magnetic field strength may lead to an augmentation in the heat transfer rate. It is also inferred that incorporating non–uniformity in distribution of the applied magnetic field translates the system to be dominated by the heat transfer irreversibility. Proceeding further, a semi-analytical investigation has been carried out considering implications of magnetohydrodynamic forces and interfacial slip on the heat transfer characteristics of streaming potential mediated flow in narrow fluidic confinements. An augmentation in the streaming potential field as attributable to the wall slip activated enhanced electromagnetohydrodynamic transport of the ionic species within the EDL has been found. Furthermore, the implications of Stern layer conductivity and magnetohydrodynamic influence on system irreversibility have been shown through analysis of entropy generation due to fluid friction and heat transfer. The results being obtained in this analysis have significant scientific and technological consequences in the context of novel design of future generation energy efficient devices, and can be useful in the further advancement of theory, simulation, and experimental work. Finally, the combined consequences of interfacial electrokinetics, rheology, and superimposed magnetic field subjected to a non-Newtonian (power-law obeying) fluid in a narrow confinement are studied in this work. The theoretical results demonstrate that the applied magnetic field imparts a retarding influence in the induced streaming potential development, whereas, triggers the heat transfer magnitude. Moreover, additional influences of power law index show reduction in heat transfer as well as the streaming potential magnitude. It is unveiled that the optimal combinations of power law index and the magnetic field lead to the minimization of the global total entropy generation in the system.

We study minimization problems with respect to a one-parameter family of generalized relative entropies. These relative entropies, which we call relative -entropies (denoted I (P; Q)), arise as redundancies under mismatched compression when cumulants of compression lengths are considered instead of expected compression lengths. These parametric relative entropies are a generalization of the usual relative entropy (Kullback-Leibler divergence). Just like relative entropy, these relative -entropies behave like squared Euclidean distance and satisfy the Pythagorean property. We explore the geometry underlying various statistical models and its relevance to information theory and to robust statistics. The thesis consists of three parts. In the first part, we study minimization of I (P; Q) as the first argument varies over a convex set E of probability distributions. We show the existence of a unique minimizer when the set E is closed in an appropriate topology. We then study minimization of I on a particular convex set, a linear family, which is one that arises from linear statistical constraints. This minimization problem generalizes the maximum Renyi or Tsallis entropy principle of statistical physics. The structure of the minimizing probability distribution naturally suggests a statistical model of power-law probability distributions, which we call an -power-law family. Such a family is analogous to the exponential family that arises when relative entropy is minimized subject to the same linear statistical constraints. In the second part, we study minimization of I (P; Q) over the second argument. This minimization is generally on parametric families such as the exponential family or the - power-law family, and is of interest in robust statistics ( > 1) and in constrained compression settings ( < 1). In the third part, we show an orthogonality relationship between the -power-law family and an associated linear family. As a consequence of this, the minimization of I (P; ), when the second argument comes from an -power-law family, can be shown to be equivalent to a minimization of I ( ; R), for a suitable R, where the first argument comes from a linear family. The latter turns out to be a simpler problem of minimization of a quasi convex objective function subject to linear constraints. Standard techniques are available to solve such problems, for example, via a sequence of convex feasibility problems, or via a sequence of such problems but on simpler single-constraint linear families.

L'une des tâches les plus difficiles de l'enseignement en chirurgie, consiste à expliquer aux étudiants quelles sont les amplitudes des forces et des couples à appliquer pour guider les instruments au cours d'une opération. Ce problème devient plus important dans le domaine de la chirurgie mini-invasive (MIS) où la perception de profondeur est perdue et le champ visuel est réduit. Pour cette raison, l'évaluation de l'habileté chirurgicale associée est devenue un point capital dans le processus d'apprentissage en médecine. Des problèmes évidents de subjectivité apparaissent dans la formation des médecins, selon l'instructeur. De nombreuses études et rapports de recherches concernent le développement de techniques automatisées d'évaluation du geste. La première partie du travail présenté dans cette thèse introduit une nouvelle méthode de classification de gestes médicaux 3D reposant sur des modèles cinématiques et biomécaniques. Celle-ci analyse de manière qualitative mais aussi quantitative les mouvements associés aux tâches effectuées. La classification du geste est réalisée en utilisant un paramétrage reposant sur la longueur d'arc pour calculer la courbure pour chaque trajectoire. Les avantages de cette approche sont l'indépendance du temps, un système de repérage absolu et la réduction du nombre de données. L'étude inclue l'analyse expérimentale de plusieurs gestes, obtenus avec plusieurs types de capteurs et réalisés par différents sujets. La deuxième partie de ce travail se concentre sur la classification reposant sur les données cinématiques et dynamiques. En premier lieu, une expression empirique, entre la géométrie du mouvement et les données cinématiques, sert à calculer une nouvelle variable appelée vitesse affine. Les expériences conduites dans ce travail de thèse montrent la nature constante de cette grandeur lorsque les gestes médicaux sont simples et identiques. Une dernière technique de classification a été implémentée en utilisant un calcul de l'énergie utilisée au cours de chaque segment du geste. Cette méthode a été validée expérimentalement en utilisant six caméras et un laparoscope instrumenté. La position 3-D de l'extrémité de l'effecteur a été enregistrée, pour plusieurs participants, en utilisant le logiciel OptiTrack Motive et des marqueurs réfléchissants montés sur le laparoscope. Les mesures de force et de couple, d'autre part, ont été acquises à l'aide des capteurs fixés sur l'outil et situés entre la pointe et la poignée de l'outil afin de capturer l'interaction entre le participant et le matériau manipulé. Les résultats expérimentaux présentent une bonne corrélation entre les valeurs de l'énergie et les compétences chirurgicales des participants impliqués dans ces expériences. / One of the most difficult tasks in surgical education is to teach students what is the optimal magnitude of forces and torques to guide the instrument during operation. This problem becomes even more relevant in the field of Mini Invasive Surgery (MIS), where the depth perception is lost and visual field is reduced. In this way, the evaluation of surgical skills involved in this field becomes in a critical point in the learning process. Nowadays, this assessment is performed by expert surgeons observation in different operating rooms, making evident subjectivity issues in the results depending on the trainer in charge of the task. Research works around the world have focused on the development of the automated evaluation techniques, that provide an objective feedback during the learning process. Therefore, first part of this thesis describe a new method of classification of 3D medical gestures based on biomechanical models (kinematics). This new approach analyses medical gestures based on the smoothness and quality of movements related to the tasks performed during the medical training. Thus, gesture classification is accomplished using an arc length parametrization to compute the curvature for each trajectory. The advantages of this approach are mainly oriented towards time and location independence and problem simplification. The study included several gestures that were performed repeatedly by different subjects; these data sets were acquired, also, with three different devices. Second part of this work is focused in a classification technique based on kinematic and dynamic data. In first place, an empirical expression between movement geometry and kinematic data is used to compute a different variable called the affine velocity. Experiments carried out in this work show the constant nature of this feature in basic medical gestures. In the same way, results proved an adequate classification based on this computation. Parameters found in previous experiments were taken into account to study movements more complex. Likewise, affine velocity was used to perform a segmentation of pick and release tasks, and the classification stage was completed using an energy computation, based on dynamic data, for each segment. Final experiments were performed using six video cameras and an instrumented laparoscope. The 3-D position of the end effector was recorded, for each participant, using the OptiTrack Motive Software and reflective markers mounted on the laparoscope. Force and torque measurements, on the other hand, were acquired using force and torque sensors attached to the instrument and located between the tool tip and the handle of the tool in order to capture the interaction between participant and the manipulated material. Results associated to these experiments present a correlation between the energy values and the surgical skills of the participants involved in these experiments.

Le but est d’explorer l’interaction entre les particules quantiques et la gravitation. On utilisera la quantification de Landau, l’effet Hall quantique et on examinera la relation entre la gravitation et l’effet Josephson. On propose une version de l’expérience "COW" (Colella-Overhauser-Werner) pour examiner les déviations de la loi du carré inverse de type Yukawa et de puissance inverse. Il est question de montages permettant d’investiguer la possibilité de mesurer le gravitomagnétisme et la constante de la gravitation G. On a examiné les transitions quantiques pour des neutrons ultra-froids (Ultra-Cold Neutron : UCN). Les résultats étaient satisfaisants pour 105 UCN. On a imaginé un effet laser avec ces neutrons émetteurs de gravitons : le phénomène est cependant très faible. Pour les corrections des niveaux de Landau : on a utilisé trois types d’espace-temps. Pour Schwarzschild, en utilisant une masse perturbatrice, les corrections d’ordres 1 et 2 dépendent du niveau n et du nombre quantique `. Cela enlève la dégénérescence des niveaux conventionnels. On obtient des résultats similaires pour les espaces-temps de Kerr et de Levi-Civita. On a proposé une expérience analogue à l’expérience COW. On a des déphasages malgré de faibles valeurs anticipées : de 10^−18 rad à 10^−4 rad pour le type Yukawa et de 10^−3 rad à 10^−9 rad pour puissance inverse. On a proposé des mesures possibles pour le gravitomagnétisme. On a aussi repris l’étude de l’influence de la gravitation sur l’effet Hall quantique. On obtient de faibles corrections pour un champ gravitationnel. On ne peut toutefois pas conclure à des quantités mesurables pour les déviations de type Yukawa et de puissance inverse. Par contre, on peut utiliser l’effet pour mesurer G avec grande précision. On a examiné l’effet Josephson sous l’effet de la gravitation en imaginant un montage simple. On a d’excellents résultats : des corrections de 10^−7 à 10^−9 Hz pour des déviations de type Yukawa et 10^−6 Hz pour des déviations en puissance inverse. Surtout, le lien entre la gravitation et la fréquence du courant de Josephson est clairement établit et mesurable. / The goal is to explore the interaction between quantum particles and gravitation. We will use Landau quantization, the quantum Hall effect and we will examine the relationship between gravity and Josephson’s effect. We propose a version of "COW" experience (Colella-Overhauser-Werner) to examine the Yukawa and inverse power deviations. We propose setups to investigate the possibility to measure gravitomagnetism and the gravitational constant G. Quantum transitions for ultra-cold neutrons (UCN) have been examined. The results were satisfactory for 105 UCN. We imagined a laser effect with these graviton emitting neutrons: the phenomenon is however very weak. For Landau level corrections: we proceeded with three types of space-times. For Schwarzschild, using a disturbing mass, the corrections of orders 1 and 2 depend on the level n and the quantum number "`". This removes the degeneracy of conventional levels. Similar results are obtained for the Kerr and the Levi-Civita spacetimes. We took over an analog of the COW experiment. We have phase shifts despite low expected values: from 10^−18 rad to 10^−4 rad for Yukawa and from 10^−3 rad to 10^−9 rad for inverse power laws. The same setup has been proposed for testing gravitomagnetism. We have also resumed the study of the influence of gravity on the quantum Hall effect. Small corrections are obtained for a gravitational field. We cannot however conclude with measurable quantities for Yukawa and inverse power laws. On the other hand, one can use the effect to measure G with great precision. We examined the Josephson effect under the effect of gravity by imagining a simple setup. We have excellent results: corrections from 10^−7 to 10^−9 Hz for Yukawa and 10^−6 Hz for inverse power law. Above all, the link between gravity and the frequency of Josephson’s current is clearly established and measurable.

The wind power prediction plays a vital role in a wind power project both during the planning and operational phase of a project. A time series based wind power prediction model is introduced and the simulations are run for different case studies. The prediction model works based on the input from 1) nearby representative wind measuring station 2) Global average wind speed value from Meteorological Institute Uppsala University mesoscale model (MIUU) 3) Power curve of the wind turbine. The measured wind data is normalized to minimize the variation in the wind speed and multiplied with the MIUU to get a distributed wind speed. The distributed wind speed is then used to interpolate the wind power with the help of the power curve of the wind turbine. The interpolated wind power is then compared with the Actual Production Data (APD) to validate the prediction model. The simulation results show that the model works fairly predicting the Annual Energy Production (AEP) on monthly averages for all sites but the model could not follow the APD trend on all cases. The sensitivity analysis shows that the variation in production does not depend on ’the variation in roughness class’ nor ’the difference in distance between the measuring station and the wind farm’. The thesis has been concluded from the results that the model works fairly predicting the AEP for all cases within the variation bounds. The accuracy of the model has been validated only for monthly averages since the APD was available only on monthly averages. But the accuracy could be increased based on future work, to assess the Power law exponent (a) parameter for different terrain and validate the model for different time scales provided if the APD is available on different time scales.

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<p dir="ltr">Since graphs are natural representations for encoding relational data, the problem of graph matching is an emerging task and has attracted increasing attention, which could potentially impact various domains such as social network de-anonymization and computer vision. Our main interest is designing polynomial-time algorithms for seeded graph matching problems where a subset of pre-matched vertex-pairs (seeds) is revealed. </p><p dir="ltr">However, the existing work does not fully investigate the pivotal role of seeds and falls short of making the most use of the seeds. Notably, the majority of existing hand-crafted algorithms only focus on using ``witnesses'' in the 1-hop neighborhood. Although some advanced algorithms are proposed to use multi-hop witnesses, their theoretical analysis applies only to \ER random graphs and requires seeds to be all correct, which often do not hold in real applications. Furthermore, a parallel line of research, Graph Neural Network (GNN) approaches, typically employs a semi-supervised approach, which requires a large number of seeds and lacks the capacity to distill knowledge transferable to unseen graphs.</p><p dir="ltr">In my dissertation, I have taken two approaches to address these limitations. In the first approach, we study to design hand-crafted algorithms that can properly use multi-hop witnesses to match graphs. We first study graph matching using multi-hop neighborhoods when partially-correct seeds are provided. Specifically, consider two correlated graphs whose edges are sampled independently from a parent \ER graph $\mathcal{G}(n,p)$. A mapping between the vertices of the two graphs is provided as seeds, of which an unknown fraction is correct. We first analyze a simple algorithm that matches vertices based on the number of common seeds in the $1$-hop neighborhoods, and then further propose a new algorithm that uses seeds in the $D$-hop neighborhoods. We establish non-asymptotic performance guarantees of perfect matching for both $1$-hop and $2$-hop algorithms, showing that our new $2$-hop algorithm requires substantially fewer correct seeds than the $1$-hop algorithm when graphs are sparse. Moreover, by combining our new performance guarantees for the $1$-hop and $2$-hop algorithms, we attain the best-known results (in terms of the required fraction of correct seeds) across the entire range of graph sparsity and significantly improve the previous results. We then study the role of multi-hop neighborhoods in matching power-law graphs. Assume that two edge-correlated graphs are independently edge-sampled from a common parent graph with a power-law degree distribution. A set of correctly matched vertex-pairs is chosen at random and revealed as initial seeds. Our goal is to use the seeds to recover the remaining latent vertex correspondence between the two graphs. Departing from the existing approaches that focus on the use of high-degree seeds in $1$-hop neighborhoods, we develop an efficient algorithm that exploits the low-degree seeds in suitably-defined $D$-hop neighborhoods. Our result achieves an exponential reduction in the seed size requirement compared to the best previously known results.</p><p dir="ltr">In the second approach, we study GNNs for seeded graph matching. We propose a new supervised approach that can learn from a training set how to match unseen graphs with only a few seeds. Our SeedGNN architecture incorporates several novel designs, inspired by our theoretical studies of seeded graph matching: 1) it can learn to compute and use witness-like information from different hops, in a way that can be generalized to graphs of different sizes; 2) it can use easily-matched node-pairs as new seeds to improve the matching in subsequent layers. We evaluate SeedGNN on synthetic and real-world graphs and demonstrate significant performance improvements over both non-learning and learning algorithms in the existing literature. Furthermore, our experiments confirm that the knowledge learned by SeedGNN from training graphs can be generalized to test graphs of different sizes and categories.</p>