933 resultados para random oracle model


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Introduction : Le diabète de type 2 est une maladie évolutive débilitante et souvent mortelle qui atteint de plus en plus de personnes dans le monde. Le traitement antidiabétique non-insulinique (TADNI) notamment le traitement antidiabétique oral (TADO) est le plus fréquemment utilisé chez les adultes atteints de cette maladie. Toutefois, plusieurs de ces personnes ne prennent pas leur TADO tel que prescrit posant ainsi la problématique d’une adhésion sous-optimale. Ceci entraîne des conséquences néfastes aussi bien pour les patients que pour la société dans laquelle ils vivent. Il serait donc pertinent d’identifier des pistes de solution à cette problématique. Objectifs : Trois objectifs de recherche ont été étudiés : 1) Explorer la capacité de la théorie du comportement planifié (TCP) à prédire l’adhésion future au TADNI chez les adultes atteints de diabète de type 2, 2) Évaluer l’efficacité globale des interventions visant à améliorer l’adhésion au TADO chez les adultes atteints de diabète de type 2 et étudier l’influence des techniques de changement de comportement sur cette efficacité globale, et 3) Évaluer l’efficacité globale de l’entretien motivationnel sur l’adhésion au traitement médicamenteux chez les adultes atteints de maladie chronique et étudier l’influence des caractéristiques de cette intervention sur son efficacité globale. Méthodes : Pour l’objectif 1 : Il s’agissait d’une enquête web, suivie d’une évaluation de l’adhésion au TADNI sur une période de 30 jours, chez des adultes atteints de diabète de type 2, membres de Diabète Québec. L’enquête consistait à la complétion d’un questionnaire auto-administré incluant les variables de la TCP (intention, contrôle comportemental perçu et attitude) ainsi que d’autres variables dites «externes». Les informations relatives au calcul de l’adhésion provenaient des dossiers de pharmacie des participants transmis via la plateforme ReMed. Une régression linéaire multivariée a été utilisée pour estimer la mesure d’association entre l’intention et l’adhésion future au TADNI ainsi que l’interaction entre l’adhésion passée et l’intention. Pour répondre aux objectifs 2 et 3, deux revues systématiques et méta-analyses ont été effectuées et rapportées selon les lignes directrices de PRISMA. Un modèle à effets aléatoires a été utilisé pour estimer l’efficacité globale (g d’Hedges) des interventions et son intervalle de confiance à 95 % (IC95%) dans chacune des revues. Nous avons également quantifié l’hétérogénéité (I2 d’Higgins) entre les études, et avons fait des analyses de sous-groupe et des analyses de sensibilité. Résultats : Objectif 1 : Il y avait une interaction statistiquement significative entre l’adhésion passée et l’intention (valeur-p= 0,03). L’intention n’était pas statistiquement associée à l’adhésion future au TADNI, mais son effet était plus fort chez les non-adhérents que chez les adhérents avant l’enquête web. En revanche, l’intention était principalement prédite par le contrôle comportemental perçu à la fois chez les adhérents [β= 0,90, IC95%= (0,80; 1,00)] et chez les non-adhérents passés [β= 0,76, IC95%= (0,56; 0,97)]. Objectif 2 : L’efficacité globale des interventions sur l’adhésion au TADO était de 0,21 [IC95%= (-0,05; 0,47); I2= 82 %]. L’efficacité globale des interventions dans lesquelles les intervenants aidaient les patients et/ou les cliniciens à être proactifs dans la gestion des effets indésirables était de 0,64 [IC95%= (0,31; 0,96); I2= 56 %]. Objectif 3 : L’efficacité globale des interventions (basées sur l’entretien motivationnel) sur l’adhésion au traitement médicamenteux était de 0,12 [IC95%= (0,05; 0,20); I2= 1 %. Les interventions basées uniquement sur l’entretien motivationnel [β= 0,18, IC95%= (0,00; 0,36)] et celles dans lesquelles les intervenants ont été coachés [β= 0,47, IC95%= (0,03; 0,90)] étaient les plus efficaces. Aussi, les interventions administrées en face-à-face étaient plus efficaces que celles administrées par téléphone [β= 0,27, IC95%=(0,04; 0,50)]. Conclusion : Il existe un écart entre l’intention et l’adhésion future au TADNI, qui est partiellement expliqué par le niveau d’adhésion passée. Toutefois, il n’y avait pas assez de puissance statistique pour démontrer une association statistiquement significative entre l’intention et l’adhésion future chez les non-adhérents passés. D’un autre côté, quelques solutions au problème de l’adhésion sous-optimale au TADO ont été identifiées. En effet, le fait d’aider les patients et/ou les cliniciens à être proactifs dans la gestion des effets indésirables contribue efficacement à l’amélioration de l’adhésion au TADO chez les adultes atteints de diabète de type 2. Aussi, les interventions basées sur l’entretien motivationnel améliorent efficacement l’adhésion au traitement médicamenteux chez les adultes atteints de maladie chronique. L’entretien motivationnel pourrait donc être utilisé comme un outil clinique pour soutenir les patients dans l’autogestion de leur TADO.

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This work aims to investigate the relationship between the entrepreneurship and the incidence of bureaucratic corruption in the states of Brazil and Federal District. The main hypothesis of this study is that the opening of a business in Brazilian states is negatively affected by the incidence of corruption. The theoretical reference is divided into Entrepreneurship and bureaucratic corruption, with an emphasis on materialistic perspective (objectivist) of entrepreneurship and the effects of bureaucratic corruption on entrepreneurial activity. By the regression method with panel data, we estimated the models with pooled data and fixed and random effects. To measure corruption, I used the General Index of Corruption for the Brazilian states (BOLL, 2010), and to represent entrepreneurship, firm entry per capita by state. Tests (Chow, Hausman and Breusch-Pagan) indicate that the random effects model is more appropriate, and the preliminary results indicate a positive impact of bureaucratic corruption on entrepreneurial activity, contradicting the hypothesis expected and found in previous articles to Brazil, and corroborating the proposition of Dreher and Gassebner (2011) that, in countries with high regulation, bureaucratic corruption can be grease in the wheels of entrepreneurship

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The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.

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This work aims to investigate the relationship between the entrepreneurship and the incidence of bureaucratic corruption in the states of Brazil and Federal District. The main hypothesis of this study is that the opening of a business in Brazilian states is negatively affected by the incidence of corruption. The theoretical reference is divided into Entrepreneurship and bureaucratic corruption, with an emphasis on materialistic perspective (objectivist) of entrepreneurship and the effects of bureaucratic corruption on entrepreneurial activity. By the regression method with panel data, we estimated the models with pooled data and fixed and random effects. To measure corruption, I used the General Index of Corruption for the Brazilian states (BOLL, 2010), and to represent entrepreneurship, firm entry per capita by state. Tests (Chow, Hausman and Breusch-Pagan) indicate that the random effects model is more appropriate, and the preliminary results indicate a positive impact of bureaucratic corruption on entrepreneurial activity, contradicting the hypothesis expected and found in previous articles to Brazil, and corroborating the proposition of Dreher and Gassebner (2011) that, in countries with high regulation, bureaucratic corruption can be grease in the wheels of entrepreneurship

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Dissertação (mestrado)—Universidade de Brasília, Departamento de Administração, Programa de Pós-graduação em Administração, 2016.

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Background: Preterm labor, which defines as live-birth delivery before 37 weeks of gestation is a main determinant of neonatal morbidity and mortality around the world. Objective: The aim of this study was to determine the prevalence of preterm labor in Iran by a meta-analysis study, to be as a final measure for policy makers in this field. Materials and Methods: In this meta-analysis, the databases of Thomson database (Web of Knowledge), PubMed/Medline, Science Direct, Scopus, Google Scholar, Iranmedex, Scientific Information Database (SID), Magiran, and Medlib were searched for articles in English and Persian language published between 1995 and 2014. Among the studies with regard to the inclusion and exclusion criteria, 14 studies (out of 1370 publications) were selected. Data were analyzed by using Stata software version 11. The heterogeneity of reported prevalence among studies was evaluated by the Chi-square based Q test and I2 statistics. Results: The results of Chi-square based on Q test and I2 statistics revealed severe heterogeneity (Q=2505.12, p-value < 0.001 and I2= 99.5%) and consequently, the random effect model was used for the meta-analysis. Based on the random effect model, the overall estimated prevalence of preterm in Iran was 9.2% (95% CI: 7.6 – 10.7). Conclusion: Present study summarized the results of previous studies and provided a comprehensive view about the preterm delivery in Iran. In order to achieve a more desirable level and its reduction in the coming years, identifying affecting factor and interventional and preventive actions seem necessary.

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Les biotechnologies, le réchauffement climatique, les ressources naturelles et la gestion des écosystèmes sont tous représentatifs de la “nouvelle politique de la nature” (Hajer 2003), un terme englobant les enjeux marqués par une grande incertitude scientifique et un encadrement réglementaire inadapté aux nouvelles réalités, suscitant de fait un conflit politique hors du commun. Dans l'espoir de diminuer ces tensions et de générer un savoir consensuel, de nombreux gouvernements se tournent vers des institutions scientifiques ad hoc pour documenter l'élaboration des politiques et répondre aux préoccupations des partie-prenantes. Mais ces évaluations scientifiques permettent-elles réellement de créer une compréhension commune partagée par ces acteurs politiques polarisés? Alors que l'on pourrait croire que celles-ci génèrent un climat d'apprentissage collectif rassembleur, un environnement politique conflictuel rend l'apprentissage entre opposant extrêmement improbable. Ainsi, cette recherche documente le potentiel conciliateur des évaluation scientifique en utilisant le cas des gaz de schiste québécois (2010-2014). Ce faisant, elle mobilise la littérature sur les dimensions politiques du savoir et de la science afin de conceptualiser le rôle des évaluations scientifiques au sein d'une théorie de la médiation scientifique (scientific brokerage). Une analyse de réseau (SNA) des 5751 références contenues dans les documents déposés par 268 organisations participant aux consultations publiques de 2010 et 2014 constitue le corps de la démonstration empirique. Précisément, il y est démontré comment un médiateur scientifique peut rediriger le flux d'information afin de contrer l'incompatibilité entre apprentissage collectif et conflit politique. L'argument mobilise les mécanismes cognitifs traditionnellement présents dans la théorie des médiateurs de politique (policy broker), mais introduit aussi les jeux de pouvoir fondamentaux à la circulation de la connaissance entre acteurs politiques.

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Les biotechnologies, le réchauffement climatique, les ressources naturelles et la gestion des écosystèmes sont tous représentatifs de la “nouvelle politique de la nature” (Hajer 2003), un terme englobant les enjeux marqués par une grande incertitude scientifique et un encadrement réglementaire inadapté aux nouvelles réalités, suscitant de fait un conflit politique hors du commun. Dans l'espoir de diminuer ces tensions et de générer un savoir consensuel, de nombreux gouvernements se tournent vers des institutions scientifiques ad hoc pour documenter l'élaboration des politiques et répondre aux préoccupations des partie-prenantes. Mais ces évaluations scientifiques permettent-elles réellement de créer une compréhension commune partagée par ces acteurs politiques polarisés? Alors que l'on pourrait croire que celles-ci génèrent un climat d'apprentissage collectif rassembleur, un environnement politique conflictuel rend l'apprentissage entre opposant extrêmement improbable. Ainsi, cette recherche documente le potentiel conciliateur des évaluation scientifique en utilisant le cas des gaz de schiste québécois (2010-2014). Ce faisant, elle mobilise la littérature sur les dimensions politiques du savoir et de la science afin de conceptualiser le rôle des évaluations scientifiques au sein d'une théorie de la médiation scientifique (scientific brokerage). Une analyse de réseau (SNA) des 5751 références contenues dans les documents déposés par 268 organisations participant aux consultations publiques de 2010 et 2014 constitue le corps de la démonstration empirique. Précisément, il y est démontré comment un médiateur scientifique peut rediriger le flux d'information afin de contrer l'incompatibilité entre apprentissage collectif et conflit politique. L'argument mobilise les mécanismes cognitifs traditionnellement présents dans la théorie des médiateurs de politique (policy broker), mais introduit aussi les jeux de pouvoir fondamentaux à la circulation de la connaissance entre acteurs politiques.

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The papers included in this thesis deal with a few aspects of insurance economics that have seldom been dealt with in the applied literature. In the first paper I apply for the first time the tools of the economics of crime to study the determinants of frauds, using data on Italian provinces. The contributions to the literature are manifold: -The price of insuring has a positive correlation with the propensity to defraud -Social norms constraint fraudulent behavior, but their strength is curtailed in economic downturns -I apply a simple extension of the Random Coefficient model, which allows for the presence of time invariant covariates and asymmetries in the impact of the regressors. The second paper assesses how the evolution of macro prudential regulation of insurance companies has been reflected in their equity price. I employ a standard event study methodology, deriving the definition of the “control” and “treatment” groups from what is implied by the regulatory framework. The main results are: -Markets care about the evolution of the legislation. Their perception has shifted from a first positive assessment of a possible implicit “too big to fail” subsidy to a more negative one related to its cost in terms of stricter capital requirement -The size of this phenomenon is positively related to leverage, size and on the geographical location of the insurance companies The third paper introduces a novel methodology to forecast non-life insurance premiums and profitability as function of macroeconomic variables, using the simultaneous equation framework traditionally employed macroeconometric models and a simple theoretical model of insurance pricing to derive a long term relationship between premiums, claims expenses and short term rates. The model is shown to provide a better forecast of premiums and profitability compared with the single equation specifications commonly used in applied analysis.

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Earthquake prediction is a complex task for scientists due to the rare occurrence of high-intensity earthquakes and their inaccessible depths. Despite this challenge, it is a priority to protect infrastructure, and populations living in areas of high seismic risk. Reliable forecasting requires comprehensive knowledge of seismic phenomena. In this thesis, the development, application, and comparison of both deterministic and probabilistic forecasting methods is shown. Regarding the deterministic approach, the implementation of an alarm-based method using the occurrence of strong (fore)shocks, widely felt by the population, as a precursor signal is described. This model is then applied for retrospective prediction of Italian earthquakes of magnitude M≥5.0,5.5,6.0, occurred in Italy from 1960 to 2020. Retrospective performance testing is carried out using tests and statistics specific to deterministic alarm-based models. Regarding probabilistic models, this thesis focuses mainly on the EEPAS and ETAS models. Although the EEPAS model has been previously applied and tested in some regions of the world, it has never been used for forecasting Italian earthquakes. In the thesis, the EEPAS model is used to retrospectively forecast Italian shallow earthquakes with a magnitude of M≥5.0 using new MATLAB software. The forecasting performance of the probabilistic models was compared to other models using CSEP binary tests. The EEPAS and ETAS models showed different characteristics for forecasting Italian earthquakes, with EEPAS performing better in the long-term and ETAS performing better in the short-term. The FORE model based on strong precursor quakes is compared to EEPAS and ETAS using an alarm-based deterministic approach. All models perform better than a random forecasting model, with ETAS and FORE models showing better performance. However, to fully evaluate forecasting performance, prospective tests should be conducted. The lack of objective tests for evaluating deterministic models and comparing them with probabilistic ones was a challenge faced during the study.

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We consider the problem of interaction neighborhood estimation from the partial observation of a finite number of realizations of a random field. We introduce a model selection rule to choose estimators of conditional probabilities among natural candidates. Our main result is an oracle inequality satisfied by the resulting estimator. We use then this selection rule in a two-step procedure to evaluate the interacting neighborhoods. The selection rule selects a small prior set of possible interacting points and a cutting step remove from this prior set the irrelevant points. We also prove that the Ising models satisfy the assumptions of the main theorems, without restrictions on the temperature, on the structure of the interacting graph or on the range of the interactions. It provides therefore a large class of applications for our results. We give a computationally efficient procedure in these models. We finally show the practical efficiency of our approach in a simulation study.

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We present a method to simulate the Magnetic Barkhausen Noise using the Random Field Ising Model with magnetic long-range interaction. The method allows calculating the magnetic flux density behavior in particular sections of the lattice reticule. The results show an internal demagnetizing effect that proceeds from the magnetic long-range interactions. This demagnetizing effect induces the appearing of a magnetic pattern in the region of magnetic avalanches. When compared with the traditional method, the proposed numerical procedure neatly reduces computational costs of simulation. (c) 2008 Published by Elsevier B.V.

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The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

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A new conceptual model for soil pore-solid structure is formalized. Soil pore-solid structure is proposed to comprise spatially abutting elements each with a value which is its membership to the fuzzy set ''pore,'' termed porosity. These values have a range between zero (all solid) and unity (all pore). Images are used to represent structures in which the elements are pixels and the value of each is a porosity. Two-dimensional random fields are generated by allocating each pixel a porosity by independently sampling a statistical distribution. These random fields are reorganized into other pore-solid structural types by selecting parent points which have a specified local region of influence. Pixels of larger or smaller porosity are aggregated about the parent points and within the region of interest by controlled swapping of pixels in the image. This creates local regions of homogeneity within the random field. This is similar to the process known as simulated annealing. The resulting structures are characterized using one-and two-dimensional variograms and functions describing their connectivity. A variety of examples of structures created by the model is presented and compared. Extension to three dimensions presents no theoretical difficulties and is currently under development.

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Most cellular solids are random materials, while practically all theoretical structure-property results are for periodic models. To be able to generate theoretical results for random models, the finite element method (FEM) was used to study the elastic properties of solids with a closed-cell cellular structure. We have computed the density (rho) and microstructure dependence of the Young's modulus (E) and Poisson's ratio (PR) for several different isotropic random models based on Voronoi tessellations and level-cut Gaussian random fields. The effect of partially open cells is also considered. The results, which are best described by a power law E infinity rho (n) (1<n<2), show the influence of randomness and isotropy on the properties of closed-cell cellular materials, and are found to be in good agreement with experimental data. (C) 2001 Acta Materialia Inc. Published by Elsevier Science Ltd. All rights reserved.