Modeling and analysis of water-hammer in coaxial pipes

The fluid-structure interaction is studied for a system composed of two coaxial pipes in an annular geometry, for both homogeneous isotropic metal pipes and fiber-reinforced (anisotropic) pipes. Multiple waves, traveling at different speeds and amplitudes, result when a projectile impacts on the water filling the annular space between the pipes. In the case of carbon fiber-reinforced plastic thin pipes we compute the wavespeeds, the fluid pressure and mechanical strains as functions of the fiber winding angle. This generalizes the single-pipe analysis of J. H. You, and K. Inaba, Fluid-structure interaction in water-filled pipes of anisotropic composite materials, J. Fl. Str. 36 (2013). Comparison with a set of experimental measurements seems to validate our models and predictions

A mechanical model for guided motion of mammalian cells

We introduce a generic, purely mechanical model for environment sensitive motion of mammalian cells that is applicable to chemotaxis, haptotaxis, and durotaxis as modes of motility. It is able to theoretically explain all relevant experimental observations, in particular, the high efficiency of motion, the behavior on inhomogeneous substrates, and the fixation of the lagging pole during motion. Furthermore, our model predicts that efficiency of motion in following a gradient depends on cell geometry (with more elongated cells being more efficient).Comment: 5 pages, 5 figures, 5 pages Supplemental Materia

Bayesian model selection in logistic regression for the detection of adverse drug reactions

Motivation: Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, exploring such databases requires statistical methods. In this context, disproportionality measures are used. However, by projecting the data onto contingency tables, these methods become sensitive to the problem of co-prescriptions and masking effects. Recently, logistic regressions have been used with a Lasso type penalty to perform the detection of associations between drugs and adverse events. However, the choice of the penalty value is open to criticism while it strongly influences the results. Results: In this paper, we propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion. Thus, we avoid the calibration of penalty or threshold. During our application on the French pharmacovigilance database, the proposed method is compared to well established approaches on a reference data set, and obtains better rates of positive and negative controls. However, many signals are not detected by the proposed method. So, we conclude that this method should be used in parallel to existing measures in pharmacovigilance.Comment: 7 pages, 3 figures, submitted to Biometrical Journa

Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method

Hedonic house price models typically impose a constant price structure on housing characteristics throughout an entire market area. However, there is increasing evidence that the marginal prices of many important attributes vary over space, especially within large markets. In this paper, we compare two approaches to examine spatial heterogeneity in housing attribute prices within the Tucson, Arizona housing market: the spatial expansion method and geographically weighted regression (GWR). Our results provide strong evidence that the marginal price of key housing characteristics varies over space. GWR outperforms the spatial expansion method in terms of explanatory power and predictive accuracy.