Optimal uncertainty quantification of a risk measurement from a computer code

La quantification des incertitudes lors d'une étude de sûreté peut être réalisée en modélisant les paramètres d'entrée du système physique par des variables aléatoires. Afin de propager les incertitudes affectant les entrées, un modèle de simulation numérique reproduisant la physique du système est exécuté avec différentes combinaisons des paramètres d'entrée, générées suivant leur loi de probabilité jointe. Il est alors possible d'étudier la variabilité de la sortie du code, ou d'estimer certaines quantités d'intérêt spécifiques. Le code étant considéré comme une boîte noire déterministe, la quantité d'intérêt dépend uniquement du choix de la loi de probabilité des entrées. Toutefois, cette distribution de probabilité est elle-même incertaine. En général, elle est choisie grâce aux avis d'experts, qui sont subjectifs et parfois contradictoires, mais aussi grâce à des données expérimentales souvent en nombre insuffisant et entachées d'erreurs. Cette variabilité dans le choix de la distribution se propage jusqu'à la quantité d'intérêt. Cette thèse traite de la prise en compte de cette incertitude dite de deuxième niveau. L'approche proposée, connue sous le nom d'Optimal Uncertainty Quantification (OUQ) consiste à évaluer des bornes sur la quantité d'intérêt. De ce fait on ne considère plus une distribution fixée, mais un ensemble de mesures de probabilité sous contraintes de moments sur lequel la quantité d'intérêt est optimisée. Après avoir exposé des résultats théoriques visant à réduire l'optimisation de la quantité d'intérêt aux point extrémaux de l'espace de mesures de probabilité, nous présentons différentes quantités d'intérêt vérifiant les hypothèses du problème. Cette thèse illustre l'ensemble de la méthodologie sur plusieurs cas d'applications, l'un d'eux étant un cas réel étudiant l'évolution de la température de gaine du combustible nucléaire en cas de perte du réfrigérant.Uncertainty quantification in a safety analysis study can be conducted by considering the uncertain inputs of a physical system as a vector of random variables. The most widespread approach consists in running a computer model reproducing the physical phenomenon with different combinations of inputs in accordance with their probability distribution. Then, one can study the related uncertainty on the output or estimate a specific quantity of interest (QoI). Because the computer model is assumed to be a deterministic black-box function, the QoI only depends on the choice of the input probability measure. It is formally represented as a scalar function defined on a measure space. We propose to gain robustness on the quantification of this QoI. Indeed, the probability distributions characterizing the uncertain input may themselves be uncertain. For instance, contradictory expert opinion may make it difficult to select a single probability distribution, and the lack of information in the input variables affects inevitably the choice of the distribution. As the uncertainty on the input distributions propagates to the QoI, an important consequence is that different choices of input distributions will lead to different values of the QoI. The purpose of this thesis is to account for this second level uncertainty. We propose to evaluate the maximum of the QoI over a space of probability measures, in an approach known as optimal uncertainty quantification (OUQ). Therefore, we do not specify a single precise input distribution, but rather a set of admissible probability measures defined through moment constraints. The QoI is then optimized over this measure space. After exposing theoretical results showing that the optimization domain of the QoI can be reduced to the extreme points of the measure space, we present several interesting quantities of interest satisfying the assumption of the problem. This thesis illustrates the methodology in several application cases, one of them being a real nuclear engineering case that study the evolution of the peak cladding temperature of fuel rods in case of an intermediate break loss of coolant accident

Identifying topological edge states in 2D optical lattices using light scattering

We recently proposed in a Letter [Physical Review Letters 108 255303] a novel scheme to detect topological edge states in an optical lattice, based on a generalization of Bragg spectroscopy. The scope of the present article is to provide a more detailed and pedagogical description of the system - the Hofstadter optical lattice - and probing method. We first show the existence of topological edge states, in an ultra-cold gas trapped in a 2D optical lattice and subjected to a synthetic magnetic field. The remarkable robustness of the edge states is verified for a variety of external confining potentials. Then, we describe a specific laser probe, made from two lasers in Laguerre-Gaussian modes, which captures unambiguous signatures of these edge states. In particular, the resulting Bragg spectra provide the dispersion relation of the edge states, establishing their chiral nature. In order to make the Bragg signal experimentally detectable, we introduce a "shelving method", which simultaneously transfers angular momentum and changes the internal atomic state. This scheme allows to directly visualize the selected edge states on a dark background, offering an instructive view on topological insulating phases, not accessible in solid-state experiments.Comment: 17 pages, 10 figures. Revised and extended version, to appear in EJP Special Topic for the special issue on "Novel Quantum Phases and Mesoscopic Physics in Quantum Gases". Extended version of arXiv:1203.124

Molecular Blocking of CD23 Supports Its Role in the Pathogenesis of Arthritis

BACKGROUND: CD23 is a differentiation/activation antigen expressed by a variety of hematopoietic and epithelial cells. It can also be detected in soluble forms in biological fluids. Initially known as the low-affinity receptor for immunoglobulin E (Fc epsilonRII), CD23 displays various other physiologic ligands such as CD21, CD11b/c, CD47-vitronectin, and mannose-containing proteins. CD23 mediates numerous immune responses by enhancing IgE-specific antigen presentation, regulating IgE synthesis, influencing cell differentiation and growth of both B- and T-cells. CD23-crosslinking promotes the secretion of pro-inflammatory mediators from human monocytes/macrophages, eosinophils and epithelial cells. Increased CD23 expression is found in patients during allergic reactions and rheumatoid arthritis while its physiopathologic role in these diseases remains to be clarified. METHODOLOGY/PRINCIPAL FINDINGS: We previously generated heptapeptidic countrestructures of human CD23. Based on in vitro studies on healthy and arthritic patients' cells, we showed that CD23-specific peptide addition to human macrophages greatly diminished the transcription of genes encoding inflammatory cytokines. This was also confirmed by significant reduction of mediator levels in cell supernatants. We also show that CD23 peptide decreased IgE-mediated activation of both human and rat CD23(+) macrophages. In vivo studies in rat model of arthritis showed that CD23-blocking peptide ameliorates clinical scores and prevent bone destruction in a dose dependent manner. Ex-vivo analysis of rat macrophages further confirmed the inhibitory effect of peptides on their activation. Taken together our results support the role of CD23 activation and subsequent inflammatory response in arthritis. CONCLUSION: CD23-blocking peptide (p30A) prevents the activation of monocytes/macrophages without cell toxicity. Thus, targeting CD23 by antagonistic peptide decreases inflammatory markers and may have clinical value in the treatment of human arthritis and allergic reactions involving CD23

Conducting Polymer Nanomaterials and Their Applications

A paradigm shift takes place in the fabrication of conducting polymers from bulky features with microsize to ultrafine features with nanometer range. Novel conducting polymer nanomaterials require the potential to control synthetic approaches of conducting polymer on molecular and atomic levels. In this article, the synthetic methodology of conducting polymer has been briefly considered with chemical oxidation polymerization and electrochemical polymerization. The recent achievements in the fabrication of conducting polymer nanomaterials have been extensively reviewed with respect to soft template method, hard template method and template-free method. It also details the morphological spectrum of conducting polymer nanomaterials such as nanoparticle, core-shell nanomaterial, hollow nanosphere, nanofiber/nanorod, nanotube, thin film and nanopattern and nanocomposite. In addition, their applications are discussed under nanometer-sized dimension.This work has been financially supported by the Brain Korea 21 program of the Korean Ministry of Education and the Hyperstructured Organic Materials Research Center supported by Korea Science and Engineering Foundation