Magnetization reversal in amorphous Fe/Dy multilayers: a Monte Carlo study

The Monte Carlo method in the canonical ensemble is used to investigate magnetization reversal in amorphous transition metal - rare earth multilayers. Our study is based on a model containing diluted clusters which exhibit an effective uniaxial anisotropy in competition with random magnetic anisotropy in the matrix. We simulate hysteresis loops for an abrupt profile and a diffuse one obtained from atom probe tomography analyses. Our results evidence that the atom probe tomography profile favors perpendicular magnetic anisotropy in agreement with magnetic measurements. Moreover, the hysteresis loops calculated at several temperatures qualitatively agree with the experimental ones

Silicon on Nothing Mems Electromechanical Resonator

The very significant growth of the wireless communication industry has spawned tremendous interest in the development of high performances radio frequencies (RF) components. Micro Electro Mechanical Systems (MEMS) are good candidates to allow reconfigurable RF functions such as filters, oscillators or antennas. This paper will focus on the MEMS electromechanical resonators which show interesting performances to replace SAW filters or quartz reference oscillators, allowing smaller integrated functions with lower power consumption. The resonant frequency depends on the material properties, such as Young's modulus and density, and on the movable mechanical structure dimensions (beam length defined by photolithography). Thus, it is possible to obtain multi frequencies resonators on a wafer. The resonator performance (frequency, quality factor) strongly depends on the environment, like moisture or pressure, which imply the need for a vacuum package. This paper will present first resonator mechanisms and mechanical behaviors followed by state of the art descriptions with applications and specifications overview. Then MEMS resonator developments at STMicroelectronics including FEM analysis, technological developments and characterization are detailed.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/EDA-Publishing

A descriptive review of variable selection methods in four epidemiologic journals : there is still room for improvement

Background : A review of epidemiological papers conducted in 2009 concluded that several studies employed variable selection methods susceptible to introduce bias and yield inadequate inferences. Many new confounder selection methods have been developed since then. Methods: The goal of the study was to provide an updated descriptive portrait of which variable selection methods are used by epidemiologists for analyzing observational data. Studies published in four major epidemiological journals in 2015 were reviewed. Only articles concerned with a predictive or explicative objective and reporting on the analysis of individual data were included. Method(s) employed for selecting variables were extracted from retained articles. Results : A total of 975 articles were retrieved and 299 met eligibility criteria, 292 of which pursued an explicative objective. Among those, 146 studies (50%) reported using prior knowledge or causal graphs for selecting variables, 34 (12%) used change in effect estimate methods, 26 (9%) used stepwise approaches, 16 (5%) employed univariate analyses, 5 (2%) used various other methods and 107 (37%) did not provide sufficient details to allow classification (more than one method could be employed in a single article). Conclusions : Despite being less frequent than in the previous review, stepwise and univariable analyses, which are susceptible to introduce bias and produce inadequate inferences, were still prevalent. Moreover, 37% studies did not provide sufficient details to assess how variables were selected. We thus believe there is still room for improvement in variable selection methods used by epidemiologists and in their reporting

Carotid artery wall mechanics in young males with high cardiorespiratory fitness

The influence of cardiorespiratory fitness (CRF) on arterial stiffness in young adults remains equivocal. Beyond conventional measures of arterial stiffness, 2D strain imaging of the common carotid artery (CCA) provides novel information related to the intrinsic properties of the arterial wall. Therefore, this study aimed to assess the effect of CRF on both conventional indices of CCA stiffness and 2D strain parameters, at rest and following a bout of aerobic exercise in young healthy males. Short‐axis ultrasound images of the CCA were recorded in 34 healthy men [22 years (95%CI, 19–22)] before, and immediately after 5‐minutes of aerobic exercise (40% VO2max). Images were analysed for arterial diameter, peak circumferential strain (PCS), and peak systolic and diastolic strain rates (S‐SR, D‐SR). Heart rate (HR), systolic and diastolic blood pressure (SBP, DBP) were simultaneously assessed and Petersons' elastic modulus (Ep) and Beta stiffness (β1) were calculated. Participants were separated post hoc into moderate and high fitness groups [VO2max: 48.9 ml.kg‐1 min‐1 (95%CI, 44.7–53.2) vs. 65.6 ml.kg‐1 min‐1 (95%CI, 63.1–68.1); P 0.13) but were elevated in the moderate‐fitness group post‐exercise (P 0.05). High‐fit individuals exhibit elevated CCA PCS and S‐SR, which may reflect training‐induced adaptations that help to buffer the rise in pulse‐pressure and stroke volume during exercise

Estimation de la variance et construction d'intervalles de confiance pour le ratio standardisé de mortalité avec application à l'évaluation d'un programme de dépistage du cancer

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2010-2011L'effet d'un programme de dépistage de cancer peut être évalué par le biais d'un ratio standardisé de mortalité (SMR). Dans ce mémoire, nous proposons un estimateur de la variance du dénominateur du SMR, c'est-à-dire du nombre de décès attendus, dans le cas où ce dernier est calculé selon la méthode de Sasieni. Nous donnons d'abord une expression générale pour la variance, puis développons des cas particuliers où des estimateurs spécifiques de l'incidence de la maladie et de son temps de survie sont utilisés. Nous montrons comment ce nouvel estimateur de la variance peut être utilisé dans la construction d'intervalles de confiance pour le SMR. Nous étudions la couverture de différents types d'intervalles de confiance par le biais de simulations et montrons que les intervalles utilisant l'estimateur de variance proposé disposent des meilleures propriétés. Nous appliquons la méthode suggérée sur les données du Programme québécois de dépistage du cancer du sein

A test for the correct specification of marginal structural models

Marginal structural models (MSMs) allow estimating the causal effect of a time‐varying exposure on an outcome in the presence of time‐dependent confounding. The parameters of MSMs can be estimated utilizing an inverse probability of treatment weight estimator under certain assumptions. One of these assumptions is that the proposed causal model relating the outcome to exposure history is correctly specified. However, in practice, the true model is unknown. We propose a test that employs the observed data to attempt validating the assumption that the model is correctly specified. The performance of the proposed test is investigated with a simulation study. We illustrate our approach by estimating the effect of repeated exposure to psychosocial stressors at work on ambulatory blood pressure in a large cohort of white‐collar workers in Québec City, Canada. Code examples in SAS and R are provided to facilitate the implementation of the test

Evaluation and comparison of covariate balance metrics in studies with time-dependent confounding

Marginal structural models have been increasingly used by analysts in recent years to account for confounding bias in studies with time-varying treatments. The parameters of these models are often estimated using inverse probability of treatment weighting. To ensure that the estimated weights adequately control confounding, it is possible to check for residual imbalance between treatment groups in the weighted data. Several balance metrics have been developed and compared in the cross-sectional case but have not yet been evaluated and compared in longitudinal studies with time-varying treatment. We have first extended the definition of several balance metrics to the case of a time-varying treatment, with or without censoring. We then compared the performance of these balance metrics in a simulation study by assessing the strength of the association between their estimated level of imbalance and bias. We found that the Mahalanobis balance performed best.Finally, the method was illustrated for estimating the cumulative effect of statins exposure over one year on the risk of cardiovascular disease or death in people aged 65 and over in population-wide administrative data. This illustration confirms the feasibility of employing our proposed metrics in large databases with multiple time-points

A graphical perspective of marginal structural models : an application for the estimation of the effect of physical activity on blood pressure

Estimating causal effects requires important prior subject-matter knowledge and, sometimes, sophisticated statistical tools. The latter is especially true when targeting the causal effect of a time-varying exposure in a longitudinal study. Marginal structural models (MSMs) are a relatively new class of causal models which effectively deal with the estimation of the effects of time-varying exposures. MSMs have traditionally been embedded in the counterfactual framework to causal inference. In this paper, we use the causal graph framework to enhance the implementation of MSMs. We illustrate our approach using data from a prospective cohort study, the Honolulu Heart Program. These data consist of 8006 men at baseline. To illustrate our approach, we focused on the estimation of the causal effect of physical activity on blood pressure, which were measured at three time-points. First, a causal graph is built to encompass prior knowledge. This graph is then validated and improved utilizing structural equation models. We estimated the aforementioned causal effect using MSMs for repeated measures and guided the implementation of the models with the causal graph. Employing the causal graph framework, we also show the validity of fitting conditional MSMs for repeated measures in the context implied by our data