Spacing of Hydraulically Fractured Horizontal Laterals in Low Permeability Formations

The discovery of unconventional reservoirs such as tight gas sands and shale gas, the resolve for the United States to become independent of foreign hydrocarbons, toppled with depleting conventional fields around the world, has pushed for new technologies and effective and efficient approaches in unconventional reservoirs long-term production to supply their ever-growing demand.;Horizontal drilling along with hydraulic fracturing are the two most popular methods used to render low-permeability formations such as the Marcellus Shale economically productive. Nowadays, coupled with horizontal drilling and hydraulic fracturing, new strategies and tools such as coil tubing and multi-lateral horizontal wells are being strategized and used for unconventional reservoirs as well as conventional reservoirs to maximize recovery. However, the understanding of the production performance of recently drilled hydraulically fractured horizontal wells in low-permeability formations represents a challenge because of the lack extensive production history on these new producing wells. The same is true about the multi-lateral horizontal wells, though they have great potential for improving the recovery. The objective of this study was to conduct a modeling study, to investigate the impact of wells spacing on the production performance of hydraulically fractured multi-lateral horizontal wells in low permeability formations

Hybrid static/dynamic scheduling for already optimized dense matrix factorization

We present the use of a hybrid static/dynamic scheduling strategy of the task dependency graph for direct methods used in dense numerical linear algebra. This strategy provides a balance of data locality, load balance, and low dequeue overhead. We show that the usage of this scheduling in communication avoiding dense factorization leads to significant performance gains. On a 48 core AMD Opteron NUMA machine, our experiments show that we can achieve up to 64% improvement over a version of CALU that uses fully dynamic scheduling, and up to 30% improvement over the version of CALU that uses fully static scheduling. On a 16-core Intel Xeon machine, our hybrid static/dynamic scheduling approach is up to 8% faster than the version of CALU that uses a fully static scheduling or fully dynamic scheduling. Our algorithm leads to speedups over the corresponding routines for computing LU factorization in well known libraries. On the 48 core AMD NUMA machine, our best implementation is up to 110% faster than MKL, while on the 16 core Intel Xeon machine, it is up to 82% faster than MKL. Our approach also shows significant speedups compared with PLASMA on both of these systems

Essays on time series forecasting with neural-network or long-dependence autoregressive models and macroeconomic news effects on bond yields

Cette thèse, organisée en trois chapitres, porte sur la modélisation et la prévision des séries chronologiques économiques et financières. Les deux premiers chapitres proposent de nouveaux modèles économétriques pour l'analyse des données économiques et financières en relaxant certaines hypothèses irréalistes habituellement faites dans la littérature. Le chapitre 1 développe un nouveau modèle de volatilité appelé TVP[indice ANN]-GARCH. Ce modèle offre une dynamique riche pour modéliser les données financières en considérant une structure GARCH (Generalized autoregressive conditional heteroscedasticity) dans laquelle les paramètres varient dans le temps selon un réseau de neurones artificiels (ANN). L'utilisation des ANNs permet de résoudre le problème de l'évaluation de la vraisemblance (présent dans les modèles à paramètres variables dans le temps (TVP)) et permet également l'utilisation de variables explicatives supplémentaires. Le chapitre développe également un algorithme Monte Carlo séquentiel (SMC) original et efficace pour estimer le modèle. Une application empirique montre que le modèle se compare favorablement aux processus de volatilité populaires en termes de prévisions de court et de long terme. L'approche peut facilement être étendue à tout modèle à paramètres fixes. Le chapitre 2 développe trois polynômes de retard autorégressifs (AR) parcimonieux qui génèrent des fonctions d'autocorrélation à décroissance lente, comme on l'observe généralement dans les séries chronologiques financières et économiques. La dynamique des polynômes de retard est similaire à celle de deux processus très performants, à savoir le modèle MSM (Multifractal Markov-Switching) et le modèle FHMV (Factorial Hidden Markov Volatility). Ils sont très flexibles car ils peuvent être appliqués à de nombreux modèles populaires tels que les processus ARMA, GARCH et de volatilité stochastique. Une analyse empirique met en évidence l'utilité des polynômes de retard pour la prévision de la moyenne conditionnelle et de la volatilité. Ils devraient être considérés comme des modèles de prévision alternatifs pour les séries chronologiques économiques et financières. Le dernier chapitre s'appuie sur une approche de régression prédictive en deux étapes pour identifier l'impact des nouvelles macroéconomiques américaines sur les rendements obligataires de trois petites économies ouvertes (Canada, Royaume-Uni et Suède). Nos résultats suggèrent que les nouvelles macroéconomiques américaines sont significativement plus importantes pour expliquer la dynamique de la courbe des taux dans les petites économies ouvertes (PEO) que les nouvelles nationales elles-mêmes. Les nouvelles relatives à la politique monétaire américaine ne sont pas les seuls facteurs importants des variations des rendements obligataires des PEO, mais les nouvelles relatives au cycle économique jouent également un rôle significatif.This thesis, organized in three chapters, focuses on modelling and forecasting economic and financial time series. The first two chapters propose new econometric models for analysing economic and financial data by relaxing unrealistic assumptions usually made in the literature. Chapter 1 develops a new volatility model named TVP[subscript ANN]-GARCH. The model offers rich dynamics to model financial data by allowing for a generalized autoregressive conditional heteroscedasticity (GARCH) structure in which parameters vary over time according to an artificial neural network (ANN). The use of ANNs for parameters dynamics is a valuable contribution as it helps to deal with the problem of likelihood evaluation (exhibited in time-varying parameters (TVP) models). It also allows for the use of additional explanatory variables. The chapter develops an original and efficient Sequential Monte Carlo sampler (SMC) to estimate the model. An empirical application shows that the model favourably compares to popular volatility processes in terms of out-of sample fit. The approach can easily be extended to any fixed-parameters model. Chapter 2 develops three parsimonious autoregressive (AR) lag polynomials that generate slowly decaying autocorrelation functions as generally observed financial and economic time series. The dynamics of the lag polynomials are similar to that of two well performing processes, namely the Markov-Switching Multifractal (MSM) and the Factorial Hidden Markov Volatility (FHMV) models. They are very flexible as they can be applied in many popular models such as ARMA, GARCH, and stochastic volatility processes. An empirical analysis highlights the usefulness of the lag polynomials for conditional mean and volatility forecasting. They could be considered as forecasting alternatives for economic and financial time series. The last chapter relies on a two steps predictive regression approach to identify the impact of US macroeconomic releases on three small open economies (Canada, United Kingdom, and Sweden) bond yields at high and low frequencies. Our findings suggest that US macro news are significantly more important in explaining yield curve dynamics in small open economies (SOEs) than domestic news itself. Not only US monetary policy news are important drivers of SOEs bond yield changes, but business cycle news also play a significant role

Comparison of electrocoagulation and chemical coagulation processes in the treatment of an effluent of a textile factory

In this work, electrocoagulation and chemical coagulation were applied to the exit effluent of a textile factory located at Douala (Cameroon).The investigations were focused on the operational (pH, conductivity) and pollution parameters (COD, total phosphorus, turbidity). The electrolytic treatment was carried out with 0.4 A current intensity, and chemical coagulation was conducted in adding initially to the effluent the same quantities of aluminium than that electrogenerated.the elimination of pollution contents depended on the quantity of Al3+ ions produced by the electrodissolution of the aluminium anode and that of the aluminium salt dissolved in solution. In fact, 58.86, 94.44 and 97.81% of COD, total phosphorous and turbidity were respectively removed by electrocoagulation, while hemical coagulation, the turbidity was also reduced roughly at the same level as by electrolytic treatment. Also, 56.08 and 63.64% COD and total phosphorus were respectively removed by chemical route. During electrocoagulation, highest removals were reached after 2.49x10-3mmol of aluminum was released in solution (after 30 minutes of treatment). Thus, the final pH obtained by this process was around 9 and the conductivity varying slightly, compared to the initial value. By contrast, chemical coagulation rendered the effluent more acidic and more conducting (γ> 4 mS.cm-1). Electrocoagulation is the best process, by the fact that the textile effluent treated by this technique can be re-used or rejected without risk in the environment. Chemical coagulation: indeed, the corrosive nature of effluent treated by this method and the significant content of the residual phosphorus can seriously inhibit the perspective of recycling.Keywords: Electrocoagulation, Chemical coagulation, Textile effluents, Removal efficienc

Caractérisation de contacts électrodes-tissus pour les stimulateurs neuro-musculaires implantables

Fondements de la stimulation nerveuse -- Organisation structurelle du système nerveux -- Activité électrique des neurones -- Le processus sensoriel -- La fonction motrice du système nerveux -- Réhabilitation par stimulation électrique -- Les électrodes et leur contact avec les tissus biologiques -- Différentes classes d'électrodes -- Le contact électrode-tissus -- Critères d'évaluation d'une électrode -- Techniques de caractérisation in vivo du contact électrode-tissus -- Mesure d'impédance -- Contrôle de la tension d'électrode -- Méthodes impulsionnelles -- Estimation de la densité de charge par phase et du désiquilibre de charge -- Autres techniques de caractérisation -- Conception d'une interface dédiée -- "A versatile electrodes-tissues contact characterization method for reliable implantable stimulation" -- Compléments sur l'amplificateur opérationnel -- Compléments sur la cellule GM -- Bilan des travaux effectués -- Prototype réalisé avec des composants discrets -- Interface dédiée à un stimulateur urinaire : puce ICDPMTEL -- Interface dédiée à un stimulateur intracortical : puce ICDPMIMC -- Techniques d'interconnexion d'un implant à multi-électrodes

Caracterisitiques des patients tuberculeux à l’ouest cameroun: 2000-2009

Introduction: La tuberculose (TB) reste de nos jours un problème majeur de santé publique dans les pays en voie de développement. Elle devient de plus en plus importante à cause de l'infection au VIH. Cette étude avait pour but de caractériser les patients admis dans le plus grand Centre de Diagnostic et de Traitement de la Tuberculose (CDT) de l'Ouest Cameroun entre 2000 et 2009. Méthodes: Les patients de 15 ans et plus admis au CDT de Baleng durant la période allant du 1er janvier 2000 au 31  décembre 2009 ont été inclus. Les données ont étés collectées grâce à une grille pré conçue. Le calcul des fréquences, moyennes et les  comparaisons de groupes ont été faites pour ressortir les caractéristiquesdes participants. Résultats: 2556 patients ont été inclus dans l'étude.  64,8% étaient de sexe masculin et l'âge médian étaient de 33ans. 2141(83,7%) de patients présentaient une TPM+, 319 (12,5%) une TPM- et 96 (3,8%) une TEP. 64,7% des patients résidaient hors du district de santéd'implantation du CDT. 79,16% de patients tuberculeux ont fait le test de dépistage du VIH et la séroprévalence chez ceux testés était de 26,06%.Les différentes évolutions en fin de période de suivi de chaque patient ont été les suivantes: évolution favorable (guéri et traitement terminé) 1954(76,6%) ; perdus de vue 231(9,0%) ; décès 230(9,0%) ; transféré 92(3,6%) ; échec 49(1,9%). Conclusion: Une proportion considérable depatients résident loin du CDT ce qui augmenterait le perdus de vue et les transferts pendant le traitement. En plus vulgariser les autres CDT de larégion, il est nécessaire de renforcer le système de transfert pour éviter les perdus de vue entre deux CDT

COMPARISON OF ELECTROCOAGULATION AND CHEMICAL COAGULATION IN THE TREATMENT OF ARTISANAL TANNERY EFFLUENTS

In this study, the treatment of the effluents of an artisanal tannery by electrocoagulation with iron electrodes was carried out. During electrolytic treatment, a current intensity of 0.4 A was used, and the electrolysis time was varied from 0 to 40 minutes. The performance of electrocoagulation was compared with that of chemical coagulation with ferric sulfate. The results obtained showed that highest removals after electrocoagulation were 71.15, 98.26, and 86.59% for the COD, colour and turbidity respectively. Chemical coagulation reduced the COD up to 83.17%, while colour and turbidity removals were 99.81 and 98.62% respectively. Although chemical coagulation leads to highest percentage removals after treatment, electrocoagulation results in near neutral pH values and also decrease conductivity weakly. By contrast, chemical coagulation increases the acidity of the effluent and its conductivity. Hence with close to neutral pH and weak conductivity values obtained for effluents treated by electrocoagulation, it is inferred that such treated effluents can be recycled. http://dx.doi.org/10.4314/njt.v35i1.2

Self-Learning Algorithms for Intrusion Detection and Prevention Systems (IDPS)

Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network\u27s traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian linear functions as hidden layers display autonomous learning capabilities and are a highly accurate anomaly detection method that can be implemented in cyberattack detection and intrusion prevention with low incidence of false positives