UNBOUND

Featured here, are the extraordinary works of our graduating Fashion Design class. This accomplishment is truly a celebration of the tree years of passion, hard work, and dedication of our students. It\u27s our hope that the fashion industry will partake in the creative endeavors of the emerging designers from the Fashion Design program at Fanshawe College in London, Ontario.https://first.fanshawec.ca/famd_design_fashiondesign_unbound/1002/thumbnail.jp

Heel raises versus prefabricated orthoses in the treatment of posterior heel pain associated with calcaneal apophysitis (Sever's Disease): study protocol for a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>Posterior Heel pain can present in children of 8 to 14 years, associated with or clinically diagnosed as Sever's disease, or calcaneal apophysitis. Presently, there are no comparative randomised studies evaluating treatment options for posterior heel pain in children with the clinical diagnosis of calcaneal apophysitis or Sever's disease. This study seeks to compare the clinical efficacy of some currently employed treatment options for the relief of disability and pain associated with posterior heel pain in children.</p> <p>Method</p> <p>Design: Factorial 2 × 2 randomised controlled trial with monthly follow-up for 3 months.</p> <p>Participants: Children with clinically diagnosed posterior heel pain possibly associated with calcaneal apophysitis/Sever's disease (n = 124).</p> <p>Interventions: Treatment factor 1 will be two types of shoe orthoses: a heel raise or prefabricated orthoses. Both of these interventions are widely available, mutually exclusive treatment approaches that are relatively low in cost. Treatment factor 2 will be a footwear prescription/replacement intervention involving a shoe with a firm heel counter, dual density EVA midsole and rear foot control. The alternate condition in this factor is no footwear prescription/replacement, with the participant wearing their current footwear.</p> <p>Outcomes: Oxford Foot and Ankle Questionnaire and the Faces pain scale.</p> <p>Discussion</p> <p>This will be a randomised trial to compare the efficacy of various treatment options for posterior heel pain in children that may be associated with calcaneal apophysitis also known as Sever's disease.</p> <p>Trial Registration</p> <p>Trial Number: ACTRN12609000696291</p> <p>Ethics Approval Southern Health: HREC Ref: 09271B</p

Amélioration du modèle reconstruction des sections efficaces dans un code de calcul de neutronique à l’échelle cœur

Modern nuclear reactors utilize core calculations that implement a thermo-hydraulic feedback requiring accurate homogenized few-group cross sections.They describe the interactions of neutrons with matter, and are endowed with the properties of smoothness and regularity, steaming from their underling physical phenomena. This thesis is devoted to the modeling of these functions by industry state-of-theart and innovative machine learning techniques. Mathematically, the subject can be defined as the analysis of convenient mapping techniques from one multi-dimensional space to another, conceptualize as the aggregated sum of these functions, whose quantity and domain depends on the simulations objectives. Convenient is intended in terms of computational performance, such as the model’s size, evaluation speed, accuracy, robustness to numerical noise, complexity,etc; always with respect to the engineering modeling objectives that specify the multidimensional spaces of interest. In this thesis, a standard UO₂ PWR fuel assembly is analyzed for three state-variables, burnup,fuel temperature, and boron concentration.Library storage requirements are optimized meeting the evaluation speed and accuracy targets in view of microscopic, macroscopic cross sections and the infinite multiplication factor. Three approximation techniques are studied: The state-of-the-art spline interpolation using computationally convenient B-spline basis, that generate high order local approximations. A full grid is used as usually donein the industry. Kernel methods, that are a very general machine learning framework able to pose in a normed vector space, a large variety of regression or classification problems. Kernel functions can reproduce different function spaces using an unstructured support,which is optimized with pool active learning techniques. The approximations are found through a convex optimization process simplified by the kernel trick. The intrinsic modular character of the method facilitates segregating the modeling phases: function space selection, application of numerical routines and support optimization through active learning. Artificial neural networks which are“model free” universal approximators able Artificial neural networks which are“model free” universal approximators able to approach continuous functions to an arbitrary degree without formulating explicit relations among the variables. With adequate training settings, intrinsically parallelizable multi-output networks minimize storage requirements offering the highest evaluation speed. These strategies are compared to each other and to multi-linear interpolation in a Cartesian grid, the industry standard in core calculations. The data set, the developed tools, and scripts are freely available under aMIT license.Pour estimer la répartition de la puissance au sein d’un réacteur nucléaire, il est nécessaire de coupler des modélisations neutroniques et thermohydrauliques. De telles simulations doivent disposer des valeurs sections efficaces homogénéisées à peu de groupes d’énergies qui décrivent les interactions entre les neutrons et la matière. Cette thèse est consacrée à la modélisation des sections efficaces par des techniques académiques innovantes basées sur l’apprentissage machine. Les premières méthodes utilisent les modèles à noyaux du type RKHS (Reproducing Kernel Hilbert Space) et les secondes par réseaux de neurones. La performance d’un modèle est principalement définie par le nombre de coefficients qui le caractérisent (c’est-à-dire l’espace mémoire nécessaire pour le stocker), la vitesse d’évaluation, la précision, la robustesse au bruit numérique, la complexité, etc. Dans cette thèse, un assemblage standard de combustible UOX REP est analysé avec trois variables d’état : le burnup, la température du combustible et la concentration en bore. La taille de stockage des bibliothèques est optimisée en cherchant à maximiser la vitesse et la précision de l’évaluation, tout en cherchant à réduire l’erreur de reconstruction des sections efficaces microscopiques, macroscopiques et du facteur de multiplication infini. Trois techniques d’approximation sont étudiées. Les méthodes de noyaux, qui utilisent le cadre général d’apprentissage machine, sont capables de proposer, dans un espace vectoriel normalisé, une grande variété de modèles de régression ou de classification. Les méthodes à noyaux peuvent reproduire différents espaces de fonctions en utilisant un support non structuré, qui est optimisé avec des techniques d’apprentissage actif. Les approximations sont trouvées grâce à un processus d’optimisation convexe facilité par "l’astuce du noyau”. Le caractère modulaire intrinsèque de la méthode facilite la séparation des phases de modélisation : sélection de l’espace de fonctions, application de routines numériques, et optimisation du support par apprentissage actif. Les réseaux de neurones sont des méthodes d’approximation universelles capables d’approcher de façon arbitraire des fonctions continues sans formuler de relations explicites entre les variables. Une fois formés avec des paramètres d’apprentissage adéquats, les réseaux à sorties multiples (intrinsèquement parallélisables) réduisent au minimum les besoins de stockage tout en offrant une vitesse d’évaluation élevée. Les stratégies que nous proposons sont comparées entre elles et à l’interpolation multilinéaire sur une grille cartésienne qui est la méthode utilisée usuellement dans l’industrie. L’ensemble des données, des outils, et des scripts développés sont disponibles librement sous licence MIT

A review of history parameters in PWR core analysis

International audienceWe present a review of the methodologies for history effect modelling with few-group cross sections for PWR fuel assemblies. Different depletion conditions with varying moderator density, fuel temperature and protracted control rod insertion are considered where the exposure history can show significant changes in the local isotopic inventory and in the neutron spectra.Cross sections are homogenized over a classical PWR-type $\text{UO}_2$ fuel assembly provided by the Burn-up Credit Criticality Benchmark NEA-6227 and condensed in two energy groups by the lattice code APOLLO2.8. The methodologies are analyzed in view of their capability to improve the error in microscopic and macroscopic cross sections, and the associated multiplication factor and the spectral index in the infinite homogeneous medium

A review of history parameters in PWR core analysis

International audienceWe present a review of the methodologies for history effect modelling with few-group cross sections for PWR fuel assemblies. Different depletion conditions with varying moderator density, fuel temperature and protracted control rod insertion are considered where the exposure history can show significant changes in the local isotopic inventory and in the neutron spectra.Cross sections are homogenized over a classical PWR-type $\text{UO}_2$ fuel assembly provided by the Burn-up Credit Criticality Benchmark NEA-6227 and condensed in two energy groups by the lattice code APOLLO2.8. The methodologies are analyzed in view of their capability to improve the error in microscopic and macroscopic cross sections, and the associated multiplication factor and the spectral index in the infinite homogeneous medium

A Review of History Parameters in LWR core nalysis

International audienceLiterature review of history effects modelling for few-group cross section for PWR. Parametrization on spectran index, spectral history, concentration and historical variables are presented

FEW-GROUP CROSS SECTIONS LIBRARY BY ACTIVE LEARNING WITH SPLINE KERNELS

This work deals with the representation of homogenized few-groups cross sections libraries by machine learning. A Reproducing Kernel Hilbert Space (RKHS) is used for different Pool Active Learning strategies to obtain an optimal support. Specifically a spline kernel is used and results are compared to multi-linear interpolation as used in industry, discussing the reduction of the library size and of the overall performance. A standard PWR fuel assembly provides the use case (OECD-NEA Burn-up Credit Criticality Benchmark [1])

FEW-GROUP CROSS SECTIONS MODELING BY ARTIFICIAL NEURAL NETWORKS

This work deals with the modeling of homogenized few-group cross sections by Artificial Neural Networks (ANN). A comprehensive sensitivity study on data normalization, network architectures and training hyper-parameters specifically for Deep and Shallow Feed Forward ANN is presented. The optimal models in terms of reduction in the library size and training time are compared to multi-linear interpolation on a Cartesian grid. The use case is provided by the OECD-NEA Burn-up Credit Criticality Benchmark [1]. The Pytorch [2] machine learning framework is used

FEW-GROUP CROSS SECTIONS MODELING BY ARTIFICIAL NEURAL NETWORKS

This work deals with the modeling of homogenized few-group cross sections by Artificial Neural Networks (ANN). A comprehensive sensitivity study on data normalization, network architectures and training hyper-parameters specifically for Deep and Shallow Feed Forward ANN is presented. The optimal models in terms of reduction in the library size and training time are compared to multi-linear interpolation on a Cartesian grid. The use case is provided by the OECD-NEA Burn-up Credit Criticality Benchmark [1]. The Pytorch [2] machine learning framework is used

Reconstruction of few-group homogenized cross section by kernel method and active learning

International audienceThis work deals with the approximation of homogenized few-groups cross sections by kernel methods. Different kernels types are used in conjunction with pool active learning to optimize the cross section's support. They are compared to multi-variate splines and multi-linear interpolation, similar to industry. A standard PWR fuel assembly is provided by the OECD-NEA Burn-up Credit Criticality Benchmark, Phase-IID, to evaluate their performances