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Institution

Mines ParisTech

EducationParis, France
About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.


Papers
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Journal ArticleDOI
TL;DR: Experimental data show that the pseudo second-order rate equation fits the kinetic profiles well, while sorption isotherms are described by the Langmuir model and Thermodynamic constants demonstrate the spontaneous and endothermic nature of sorption.
Abstract: Cysteine-functionalized chitosan magnetic nano-based particles were synthesized for the sorption of light and heavy rare earth (RE) metal ions (La(III), Nd(III) and Yb(III)). The structural, surface, and magnetic properties of nano-sized sorbent were investigated by elemental analysis, FTIR, XRD, TEM and VSM (vibrating sample magnetometry). Experimental data show that the pseudo second-order rate equation fits the kinetic profiles well, while sorption isotherms are described by the Langmuir model. Thermodynamic constants (ΔG°, ΔH°) demonstrate the spontaneous and endothermic nature of sorption. Yb(III) (heavy RE) was selectively sorbed while light RE metal ions La(III) and Nd(III) were concentrated/enriched in the solution. Cationic species RE(III) in aqueous solution can be adsorbed by the combination of chelating and anion-exchange mechanisms. The sorbent can be efficiently regenerated using acidified thiourea.

100 citations

Journal ArticleDOI
TL;DR: It turns out that linearizable driftless systems with two inputs can be linearized using only invertible feedbacks, and can also be put into a chained form by (invertible) static feedback.
Abstract: The problem of dynamic feedback linearization is recast using the notion of dynamic immersion. We investigate here a “generic” property which holds at every point of a dense open subset, but may fail at some points of interest, such as equilibrium points. Linearizable systems are then systems that can be immersed into linear controllable ones. This setting is used to study the linearization of driftless systems: a geometric sufficient condition in terms of Lie brackets is given; this condition is also shown to be necessary when the number of inputs equals two. Though noninvertible feedbacks are nota priori excluded, it turns out that linearizable driftless systems with two inputs can be linearized using only invertible feedbacks, and can also be put into a chained form by (invertible) static feedback. Most of the developments are done within the framework of differential forms and Pfaffian systems.

100 citations

Journal ArticleDOI
TL;DR: This work addresses the problem of adaptive output-feedback stabilization of general first-order hyperbolic partial integro-differential equations (PIDE) with non-local (in space) terms by introducing a pre-transformation of the system into an observer canonical form and presenting the non-adaptive/baseline controller.

100 citations

Journal ArticleDOI
TL;DR: In this article, a heat recovery ventilation (HRV) unit is modeled, using TRNSYS, and studied in order to characterize its annual performance when integrated into residential and commercial low energy buildings.

99 citations

Journal ArticleDOI
01 Jul 2008
TL;DR: This work proposes a new method for supervised classification of arrayCGH data that incorporates the biological specificities of DNA copy number variations along the genome as prior knowledge and demonstrates that the introduction of the new prior on the classifier leads not only to more accurate predictions, but also to the identification of known and new regions of interest in the genome.
Abstract: Motivation: Array-based comparative genomic hybridization (arrayCGH) has recently become a popular tool to identify DNA copy number variations along the genome. These profiles are starting to be used as markers to improve prognosis or diagnosis of cancer, which implies that methods for automated supervised classification of arrayCGH data are needed. Like gene expression profiles, arrayCGH profiles are characterized by a large number of variables usually measured on a limited number of samples. However, arrayCGH profiles have a particular structure of correlations between variables, due to the spatial organization of bacterial artificial chromosomes along the genome. This suggests that classical classification methods, often based on the selection of a small number of discriminative features, may not be the most accurate methods and may not produce easily interpretable prediction rules. Results: We propose a new method for supervised classification of arrayCGH data. The method is a variant of support vector machine that incorporates the biological specificities of DNA copy number variations along the genome as prior knowledge. The resulting classifier is a sparse linear classifier based on a limited number of regions automatically selected on the chromosomes, leading to easy interpretation and identification of discriminative regions of the genome. We test this method on three classification problems for bladder and uveal cancer, involving both diagnosis and prognosis. We demonstrate that the introduction of the new prior on the classifier leads not only to more accurate predictions, but also to the identification of known and new regions of interest in the genome. Availability: All data and algorithms are publicly available. Contact: franck.rapaport@curie.fr

99 citations


Authors

Showing all 6591 results

NameH-indexPapersCitations
Francis Bach11048454944
Olivier Delattre10349039258
Richard M. Murray9771169016
Bruno Latour9636494864
George G. Malliaras9438228533
George S. Wilson8871633034
Zhong-Ping Jiang8159724279
F. Liu8042823869
Kazu Suenaga7532926287
Carlo Adamo7544436092
Edith Heard7519623899
Enrico Zio73112723809
John J. Jonas7037921544
Bernard Asselain6940923648
Eric Guibal6929416397
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202315
202264
2021274
2020260
2019250
2018249