Presto: a software package for the simulation of diffusion processes

Conclusion Presto is a software which automatically generates FORTRAN code corresponding to approximation procedures of the solutions of stochastic differential systems.At the present time, it answers only to a few needs, but a diversification of its users and its simple internal structure could easily permit it to be developed towards a more ambitious system.Finally, let us mention that Presto is an INRIA product, which is free for academic institutions, universities, etc, which already have MAPLE and X-Windows licences. Presto can be run on any UNIX station under X-Windows environment.     

Comment vendre un produit incertain ? Activités de détachement et d’attachement d’une firme d’agrofourniture

This article examines the sales practices of a French agricultural supplies company whose products have uncertain qualities and contested effects. We show that this characteristic leads the sales technicians responsible for selling these products to develop arguments that make extensive use of a rhetoric aiming to detach the clients from their competitors. Given how difficult it is to establish an objective assessment of products’ intrinsic qualities, we also show how attachments are built around a broad-ranging solution that in particular includes providing consulting services for farmers. More generally, in the analysis of sales work, the commerce of these uncertain products invites one to think of processes of attachment and detachment in symmetrical terms.     

Enhanced rate of resting energy expenditure in women using hormone-replacement therapy: preliminary results

Hormone-replacement therapy (HRT) attenuates the menopause-associated alterations in body composition. It is not known, however, whether this effect is a result of a concomitant increase in energy expenditure. The authors examined whether women submitted to a long-term HRT treatment presented greater energy expenditure than women who had never used HRT. We compared 13 postmenopausal women using HRT (>1 yr) with 13 age- (+/-2 yr) and body-mass-index-matched (BMI; +/-1.5 kg/m2) postmenopausal women not using HRT. Resting energy expenditure (REE; indirect calorimetry), body composition, and daily (DEE) and physical activity (PAEE) energy expenditure (accelerometry) were obtained. Although BMI, fat mass, fat-free mass, DEE, and PAEE were similar between groups, the HRT group displayed a significantly greater REE than the no-HRT group (Delta +222 kcal/day). In conclusion, the authors observed that a long-term treatment with HRT is associated with a greater REE in postmenopausal women. These results need to be confirmed.     

Capturing touch in parent–infant interaction: A comparison of methods

Naturally occurring high levels of caregiver touch promote offspring development in many animal species. Yet, caregiver touch remains a relatively understudied topic in human development, possibly due to challenges of measuring this means of interaction. While parental reports (e.g., questionnaires, diaries) are easy to collect, they may be subject to biases and memory limitations. In contrast, observing touch in a short session of parent–child interaction in the lab may not be representative of touch interaction in daily life. In the present study, we compared parent reports (one-off questionnaires and diary) and observation-based methods in a sample of German 6- to 13-month-olds and their primary caregivers (n = 71). In an attempt to characterize touching behaviors across a broad range of contexts, we measured touch both during play and while the parent was engaged in another activity. We found that context affected both the quantity and types of touch used in interaction. Parent-reported touch was moderately associated with touch observed in parent–child interactions and more strongly with touch used during play. We conclude that brief one-off questionnaires are a good indicator of touch in parent–child interaction, yet they may be biased toward representing particular daily activities and particular types of touch.     

Free Knot Splines with RJMCMC in Survival Data Analysis

The B-spline representation is a common tool to improve the fitting of smooth nonlinear functions, it offers a fitting as a piecewise polynomial. The regions that define the pieces are separated by a sequence of knots. The main difficulty in this type of modeling is the choice of the number and the locations of these knots. The Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm provides a solution to simultaneously select these two parameters by considering the knots as free parameters. This algorithm belongs to the MCMC techniques that allow simulations from target distributions on spaces of varying dimension. The aim of the present investigation is to use this algorithm in the framework of the analysis of survival time, for the Cox model in particular. In fact, the relation between the hazard ratio function and the covariates being assumed to be log-linear, this assumption is too restrictive. Thus, we propose to use the RJMCMC algorithm to model the log hazard ratio function by a B-spline representation with an unknown number of knots at unknown locations. This method is illustrated with two real data sets: the Stanford heart transplant data and lung cancer survival data. Another application of the RJMCMC is selecting the significant covariates, and a simulation study is performed.     

THE CHOICE BETWEEN SETS OF REGRESSORS

This paper examines the choice of critical values for testing both non-sequential and nested sequential sets of constraints in the standard linear regression model. Modest increases in (e.g.) t-ratio critical values relative to their one-off values are often sufficient to maintain proper size. A Bayesian decision-theoretic approach, highlighted by the Schwarz (1978) criterion, provides a framework for deriving consistency and asymptotic local power properties of both forms of testing (data mining) algorithms.     

Stochastically Modeling Listeria Monocytogenes Growth in Farm Tank Milk

This article presents a Listeria monocytogenes growth model in milk at the farm bulk tank stage. The main objective was to judge the feasibility and value to risk assessors of introducing a complex model, including a complete thermal model, within a microbial quantitative risk assessment scheme. Predictive microbiology models are used under varying temperature conditions to predict bacterial growth. Input distributions are estimated based on data in the literature, when it is available. If not, reasonable assumptions are made for the considered context. Previously published results based on a Bayesian analysis of growth parameters are used. A Monte Carlo simulation that forecasts bacterial growth is the focus of this study. Three scenarios that take account of the variability and uncertainty of growth parameters are compared. The effect of a sophisticated thermal model taking account of continuous variations in milk temperature was tested by comparison with a simplified model where milk temperature was considered as constant. Limited multiplication of bacteria within the farm bulk tank was modeled. The two principal factors influencing bacterial growth were found to be tank thermostat regulation and bacterial population growth parameters. The dilution phenomenon due to the introduction of new milk was the main factor affecting the final bacterial concentration. The results show that a model that assumes constant environmental conditions at an average temperature should be acceptable for this process. This work may constitute a first step toward exposure assessment for L. monocytogenes in milk. In addition, this partly conceptual work provides guidelines for other risk assessments where continuous variation of a parameter needs to be taken into account.