Kinetic model identification and parameters estimation from TGA experiments

The presented work is a part of an ongoing research effort on the development of a general methodology for the determination of kinetic models of solid thermal decomposition under pyrolysis conditions with thermogravimetric analysis (TGA) devices. The goal is to determine a simple and robust kinetic model for a given solid with the minimum of TGA experiments. From the latter point of view, this work can be seen as the optimal design of TGA experiments for pyrolysis kinetic modelling. In this paper, a general procedure is presented and more precise results are given about the influence of the sensitivity matrix on the estimation of the kinetic parameters and about the important influence of the specific TGA runs used for parameter estimation on the precision of the fitted parameters. The first results are shown for simulated applications; in the final part, the presented results concern cellulose pyrolysis in a Setaram TGA device

Robust capacitated trees and networks with uniform demands

We are interested in the design of robust (or resilient) capacitated rooted Steiner networks in case of terminals with uniform demands. Formally, we are given a graph, capacity and cost functions on the edges, a root, a subset of nodes called terminals, and a bound k on the number of edge failures. We first study the problem where k = 1 and the network that we want to design must be a tree covering the root and the terminals: we give complexity results and propose models to optimize both the cost of the tree and the number of terminals disconnected from the root in the worst case of an edge failure, while respecting the capacity constraints on the edges. Second, we consider the problem of computing a minimum-cost survivable network, i.e., a network that covers the root and terminals even after the removal of any k edges, while still respecting the capacity constraints on the edges. We also consider the possibility of protecting a given number of edges. We propose three different formulations: a cut-set based formulation, a flow based one, and a bilevel one (with an attacker and a defender). We propose algorithms to solve each formulation and compare their efficiency

Sliding windows and lattice algorithms for computing QR factors in the least squares theory of linear prediction

Includes bibliographical references.In this correspondence we pose a sequence of linear prediction problems that differ a little from those previously posed. The solutions to these problems introduce a family of "sliding" window techniques into the least squares theory of linear prediction. By using these techniques we are able to QR factor the Toeplitz data matrices that arise in linear prediction. The matrix Q is an orthogonal version of the data matrix and the matrix R is a Cholesky factor of the experimental correlation matrix. Our QR and Cholesky algorithms generate generalized reflection coefficients that may be used in the usual ways for analysis, synthesis, or classification.This work was supported by the Office of Naval Research, Arlington, VA, under Contract N00014-85-K-0256

EU-Silc / SRCV, ambition et apport d'une approche longitudinale et transversale des revenus et des conditions de vie, en France et en Europe

En consacrant ce numéro au dispositif EU-Silc / SRCV ("European Union - Statistics on Income and Living Conditions" / "Statistiques sur les ressources et les conditions de vie"), Économie et Statistique renoue avec une pratique qui date déjà de 2001 où le numéro 349-350 était centré dans son entier sur le Panel européen des ménages. EU-Silc/SRCV s'inscrit en effet dans la montée en puissance, en France comme en Europe et depuis environ trois décennies, des dispositifs longitudinaux d'observation et de suivi des ménages en vue de mieux connaître et analyser les conditions de vie de ces derniers. [Premier paragraphe de l'article

Prediction of responsibility for drivers and riders involved in injury road crashes

Responsibility analysis allows the evaluation of crash risk factors from crash data only, but requires a reliable responsibility assessment. The aim of the present study is to predict expert responsibility attribution (considered as a gold-standard) from explanatory variables available in crash data routinely recorded by the police, according to a data-driven process with explicit rules. Method: Driver responsibility was assessed by experts using all information contained in police reports for a sample of about 5000 injury crashes that occurred in France in 2011. Three statistical methods were used to predict expert responsibility attribution: logistic regression with L1 penalty, random forests, and boosting. Potential predictors of expert attribution referred to inappropriate driver actions and to external conditions at the time of the crash. Logistic regression was chosen to construct a score to assess responsibility for drivers and riders in crashes involving one or more motor vehicles, or involving a cyclist or pedestrian. Results: Cross-validation showed that our tool can predict expert responsibility assessments on new data sets. In addition, responsibility analyses performed using either the expert responsibility or our predicted responsibility return similar odds ratios. Our scoring process can then be used to reliably assess responsibility based on national police report databases, provided that they include the information needed to construct the score

Reactivity of Cooperative Systems: Application to ReactiveML -- extended version

Cooperative scheduling enables efficient sequential implementations of concurrency. It is widely used to provide lightweight threads facilities as libraries or programming constructs in many programming languages. However, it is up to programmers to actually cooperate to ensure the reactivity of their programs. We present a static analysis that checks the reactivity of programs by abstracting them into so-called behaviors using a type-and-effect system. Our objective is to find a good compromise between the complexity of the analysis and its precision for typical reactive programs. The simplicity of the analysis is mandatory for the programmer to be able to understand error messages and how to fix reactivity problems. Our work is applied and implemented in the functional synchronous language ReactiveML. It handles recursion, higher-order processes and first-class signals. We prove the soundness of our analysis with respect to the big-step semantics of the language: a well-typed program with reactive effects is reactive. The analysis is easy to implement and generic enough to be applicable to other models of concurrency such as coroutines. This research report is the extended version of the article published at the 21st International Static Analysis Symposium.L'ordonnancement coopératif permet l'implémentation séquentielle efficace de la concurrence. Il est largement utilisé pour fournir des threads légers sous forme de bibliothèques ou de constructions de programmation dans de nombreux langages de programmation. Toutefois, il appartient aux programmeurs d'appeler des primitives de coopération pour assurer la réactivité de leurs programmes. Nous présentons une analyse statique qui vérifie la réactivité des programmes en les abstrayant en comportements à l'aide d'un système de types à effets. Notre objectif est de trouver un bon compromis entre la complexité de l'analyse et sa précision pour les programmes réactifs typiques. La simplicité de l'analyse est obligatoire pour que le programmeur soit en mesure de comprendre les messages d'erreur et comment résoudre les problèmes de réactivité. Notre travail est appliqué et mis en oeuvre dans le langage synchrone fonctionnel ReactiveML. Il gère la récursivité, les processus d'ordre supérieur et les signaux de première classe. Nous prouvons la correction de notre analyse par rapport à la sémantique grands pas du langage~: un programme bien typé avec des effets réactifs est réactif. L'analyse est facile à mettre en oeuvre et assez générique pour être applicable à d'autres modèles de concurrence, tels que les coroutines. Ce rapport de recherche est la version étendue de l'article publié dans les actes de la 21ème édition de l'International Static Analysis Symposium

Réactivité des systèmes coopératifs : le cas de ReactiveML

International audienceLa concurrence coopérative est un modèle de programmation très répandu. On peut par exemple l'utiliser en OCaml à travers des bibliothèques comme Lwt, Async ou Equeue. Il a de nombreux avantages tels que l'absence de courses critiques et des implantations légères et efficaces. Néanmoins, un des inconvénients majeurs de ce modèle est qu'il dépend de la discipline du programmeur pour garantir que le système est réactif : un processus peut empêcher les autres de s'exécuter. ReactiveML est un langage qui étend OCaml avec des constructions de concurrence coopérative. Il propose une analyse statique, l'analyse de réactivité, qui permet de détecter les expressions qui risquent de produire des comportements non coopératifs. Dans cet article, nous présentons cette analyse statique qui se définit à l'aide d'un système de types et effets. Ainsi, comme le typage de données aide les programmeurs à détecter des erreurs d'exécution au plus tôt, l'analyse de réactivité aide à détecter des erreurs de concurrence