The effectiveness of the Natural Resource Conservation Service (NRCS) and Huff rainfall distribution methods for use in detention basin design

This thesis focuses on the effectiveness of the NRCS and Huff rainfall distribution methods for use in detention basin design. This study required the use of HEC-HMS, hydrologic modeling software, in order to analyze the distribution methods. Three separate detention basins and their watersheds were modeled for this study. The watersheds were analyzed for both undeveloped and developed conditions. The parameters analyzed include detention basin inflow, detention basin outflow, watershed peak discharge, and detention basin storage capacity. The determination of detention basin effectiveness was based upon these parameters. The NRCS distribution method is widely used; however, many who use it have little understanding of its effectiveness. The Huff distribution method differs in several ways from the NRCS distribution method including providing the user with an option to use different storm durations --Abstract, page iii

De la continuité entre non-sens et sens : réflexions d'après Deleuze, Ricœur et Wittgenstein

A beginner reader might think that my title is senseless: aren't sense and nonsense supposed to be opposed? Why seeking continuity there? The Stoics and after them this line that goes from Leibniz to Deleuze via Nietzsche, have revealed an intrinsic relationship of continuity between these two entities that common sense sees as contradictory. Although this continuity exists at least from a Stoic-Deleuzian viewpoint, its functioning and nature remain problematic. My goal here is to study this continuity to understand and actualize it, focusing, of course, on Deleuze’s writings, but also on Ricœur’s and Wittgenstein’s which will help me get a better sense of the generation of meaning and the relationship between nonsense and sense

Synthèse et caractérisation de nouveaux complexes aryloxy à base de tungstène

International audienc

Forecasting financial time series

The world went through weeks of financial turbulence in stock markets and investors were overcome by fears fuelled by more bad news, while countries continued their attempts to calm the markets with more injection of funds. By these very disturbed times, even if traders hope extreme risk aversion has passed, an investor would like predict the future of the market in order to protect his portfolio and a speculator would like to optimize his tradings. This thesis describes the design of numerical models and algorithms for the forecasting of financial time series, for speculation on a short time interval. To this aim, we will use two models: - " Price Forecasting Model " forecasts the behavior of an asset for an interval of three hours. This model is based on Functional Clustering and smoothing by cubic-splines in the training phase to build local Neural models, and Functional Classification for generalization, - " Model of Trading " forecasts the First Stopping time, when an asset crosses for the first time a threshold defined by the trader. This model combines a Price Forecasting Model for the prediction of market trend, and a Trading Recommendation for prediction of the first stopping time. We use an auto-adaptive Dynamic State Space Model, with Particle Filters and Kalman-Bucy Filters for parameter estimation.(FSA 3) -- UCL, 200

Electron Processing at 50 eV of Terphenylthiol Self-Assembled Monolayers: Contributions of Primary and Secondary Electrons

International audienceAromatic self-assembled monolayers (SAMs) can serve as platforms for development of supramolecular assemblies driven by surface templates. For many applications, electron processing is used to locally reinforce the layer. To achieve better control of the irradiation step, chemical transformations induced by electron impact at 50 eV of terphenylthiol SAMs are studied, with these SAMs serving as model aromatic SAMs. High-resolution electron energy loss spectroscopy (HREELS) and electron-stimulated desorption (ESD) of neutral fragment measurements are combined to investigate electron-induced chemical transformation of the layer. The decrease of the CH stretching HREELS signature is mainly attributed to dehydrogenation, without a noticeable hybridization change of the hydrogenated carbon centers. Its evolution as a function of the irradiation dose gives an estimate of the effective hydrogen content loss cross-section, σ = 2.7−4.7 × 10 −17 cm 2. Electron impact ionization is the major primary mechanism involved, with the impact electronic excitation contributing only marginally. Therefore, special attention is given to the contribution of the low-energy secondary electrons to the induced chemistry. The effective cross-section related to dissociative secondary electron attachment at 6 eV is estimated to be 1 order of magnitude smaller. The 1 eV electrons do not induce significant chemical modification for a 2.5 mC cm −2 dose, excluding their contribution

Classification et prédiction fonctionnelles d'actifs boursiers en intraday

Nous présentons une méthode d’analyse fonctionnelle pour la prédiction de séries temporelles. A partir de la décomposition des dynamiques en clusters, nous construisons des modèles locaux pour la prédiction de l’évolution des séries à partir des données du passé. Un modèle probabiliste est utilisé pour la combinaison des prédictions locales. Cette méthode peut être appliquée à tout problème de prédiction de séries temporelles mais elle est particulièrement adaptée aux données avec des dépendances non linéaires et des clusters, tels que les séries financières. La méthode a été appliquée à la prédiction des séries boursières de données en "tick par tick"

Infections communautaires en médecine aiguë gériatrique (profil gériatrique et devenir des patients)

Contexte : Les maladies infectieuses sont une des principales causes d hospitalisation des sujets âgés et la troisième cause de mortalité chez cette population. L objectif de l étude est de décrire les infections communautaires en Médecine Aigüe Gériatrique (MAG) en s intéressant au profil et au devenir des patients infectés. Méthode : Etude prospective monocentrique portant sur tous les patients hospitalisés consécutivement en MAG au centre hospitalier régional universitaire de Lille entre janvier et juillet 2013. Chaque patient bénéficie d une évaluation gériatrique à son admission. En fin d hospitalisation, le devenir du patient est relevé. L évolution clinique du patient est consignée quotidiennement. Le diagnostic d infection repose sur les critères de Mac Geer, révisés en 2012. Le caractère communautaire ou nosocomial est défini par le délai d apparition des symptômes. L antibiothérapie initiale reçue par les patients vivant en institution présentant une infection communautaire est relevée rétrospectivement, à partir du dossier médical. Résultats : Six cent cinquante deux patients ont été inclus dans l étude (âge moyen : 84.1 +- 6.4 ans, 65.3% de femmes). Au total, 1/3 des patients présentent une infection bactérienne en MAG, selon les critères de Mac Geer. Cent dix neuf infections communautaires (18.3%) sont recensées : 61.3% sont des infections pulmonaires, 18.5% sont des infections urinaires et 10.1% sont des bactériémies. Dix patients (17.9%) présentent une infection nosocomiale au décours d une infection communautaire. Les patients infectés ont un profil gériatrique similaire aux autres patients, hormis un taux d albumine (p<0.001) et un score ADL à l entrée (p<0.05) significativement plus bas. La durée moyenne d hospitalisation (p=0.65), l orientation en service de SSR (RR= 0.91 [0.58 - 1.43]) ou en soins palliatifs (RR= 1.28 [0.70 2.33]) et la mortalité intra-hospitalière (RR= 1.58 [0.76 - 3.30]) ne sont pas différentes entre les patients présentant une infection communautaire et les autres patients. Une antibiothérapie initiale à visée communautaire est prédominante et satisfaisante dans les infections acquises en institution. Conclusion : Près d un patient sur cinq hospitalisé en MAG présente une infection communautaire, selon les critères de Mac Geer. Le profil gériatrique de ces patients est comparable à celui des patients hospitalisés en MAG pour un autre motif. Le diagnostic d infection communautaire n est pas associé à une augmentation du risque d infection nosocomiale, ni d une augmentation de la durée moyenne d hospitalisation, de la mortalité intra hospitalière et d une orientation plus fréquente en SSR. En cas d infection acquise en institution, l étude suggère une efficacité des antibiothérapies à visée communautaire.LILLE2-BU Santé-Recherche (593502101) / SudocSudocFranceF

Forecasting high and low of financial time series by particle filters and Kalman filters

The analysis of financial time series is very useful in the economic world. This paper deals with a data-driven empirical analysis of financial time series. In this paper we present a forecasting method of the first stopping times, when the prices cross for the first time a "high" or "low" threshold defined by the trader, based on an empirical functional analysis of the past "tick data" of the series, without resampling. An originality of this method is that it does not use a theoretical financial model but a non-parametric space state representation with non-linear RBF neural networks. Modelling and forecasting are made by Particles systems and Kalman filters. This method can be applied to any forecasting problem of stopping time, but is particularly suited for data showing nonlinear dependencies and observed at irregularly and randomly spaced times like financial time series of «tick data» do. The method is applied to the forecasting of stopping times of "high" and "low" of financial time series in order to be useful for speculator

Modelling and Forecasting financial time series of «tick data» by functional analysis and neural networks

The analysis of financial time series is of primary importance in the economic world. This paper deals with a data-driven empirical analysis of financial time series. The goal is to obtain insights into the dynamics of series and out-of-sample forecasting. In this paper we present a forecasting method based on an empirical functional analysis of the past of series. An originality of this method is that it does not make the assumption that a single model is able to capture the dynamics of the whole series. On the contrary, it splits the past of the series into clusters, and generates a specific local neural model for each of them. The local models are then combined in a probabilistic way, according to the distribution of the series in the past. This forecasting method can be applied to any time series forecasting problem, but is particularly suited for data showing nonlinear dependencies, cluster effects and observed at irregularly and randomly spaced times like high-frequency financial time series do. One way to overcome the irregular and random sampling of "tick-data" is to resample them at low-frequency, as it is done with "Intraday". However, even with optimal resampling using say five minute returns when transactions are recorded every second, a vast amount of data is discarded, in contradiction to basic statistical principles. Thus modelling the noise and using all the data is a better solution, even if one misspecifies the noise distri- bution. The method is applied to the forecasting of financial time series of «tick data» of assets on a short horizon in order to be useful for speculator