Complex Obtuse Random Walks and their Continuous-Time Limits

We study a particular class of complex-valued random variables and their associated random walks: the complex obtuse random variables. They are the generalization to the complex case of the real-valued obtuse random variables which were introduced in \cite{A-E} in order to understand the structure of normal martingales in \RR^n.The extension to the complex case is mainly motivated by considerations from Quantum Statistical Mechanics, in particular for the seek of a characterization of those quantum baths acting as classical noises. The extension of obtuse random variables to the complex case is far from obvious and hides very interesting algebraical structures. We show that complex obtuse random variables are characterized by a 3-tensor which admits certain symmetries which we show to be the exact 3-tensor analogue of the normal character for 2-tensors (i.e. matrices), that is, a necessary and sufficient condition for being diagonalizable in some orthonormal basis. We discuss the passage to the continuous-time limit for these random walks and show that they converge in distribution to normal martingales in \CC^N. We show that the 3-tensor associated to these normal martingales encodes their behavior, in particular the diagonalization directions of the 3-tensor indicate the directions of the space where the martingale behaves like a diffusion and those where it behaves like a Poisson process. We finally prove the convergence, in the continuous-time limit, of the corresponding multiplication operators on the canonical Fock space, with an explicit expression in terms of the associated 3-tensor again

Entanglement of Bipartite Quantum Systems driven by Repeated Interactions

We consider a non-interacting bipartite quantum system $\mathcal H_S^A\otimes\mathcal H_S^B$ undergoing repeated quantum interactions with an environment modeled by a chain of independant quantum systems interacting one after the other with the bipartite system. The interactions are made so that the pieces of environment interact first with $\mathcal H_S^A$ and then with $\mathcal H_S^B$. Even though the bipartite systems are not interacting, the interactions with the environment create an entanglement. We show that, in the limit of short interaction times, the environment creates an effective interaction Hamiltonian between the two systems. This interaction Hamiltonian is explicitly computed and we show that it keeps track of the order of the successive interactions with $\mathcal H_S^A$ and $\mathcal H_S^B$. Particular physical models are studied, where the evolution of the entanglement can be explicitly computed. We also show the property of return of equilibrium and thermalization for a family of examples

High-throughput in-situ characterization and modelling of precipitation kinetics in compositionally graded alloys

The development of new engineering alloy chemistries is a time consuming and iterative process. A necessary step is characterization of the nano/microstructure to provide a link between the processing and properties of each alloy chemistry considered. One approach to accelerate the identification of optimal chemistries is to use samples containing a gradient in composition, ie. combinatorial samples, and to investigate many different chemistries at the same time. However, for engineering alloys, the final properties depend not only on chemistry but also on the path of microstructure development which necessitates characterization of microstructure evolution for each chemistry. In this contribution we demonstrate an approach that allows for the in-situ, nanoscale characterization of the precipitate structures in alloys, as a function of aging time, in combinatorial samples containing a composition gradient. The approach uses small angle x-ray scattering (SAXS) at a synchrotron beamline. The Cu-Co system is used for the proof-of-concept and the combinatorial samples prepared contain a gradient in Co from 0% to 2%. These samples are aged at temperatures between 450{\textdegree}C and 550{\textdegree}C and the precipitate structures (precipitate size, volume fraction and number density) all along the composition gradient are simultaneously monitored as a function of time. This large dataset is used to test the applicability and robustness of a conventional class model for precipitation that considers concurrent nucleation, growth and coarsening and the ability of the model to describe such a large dataset.Comment: Published in Acta Materiali

A NTU-Based Model to Estimate Suction Superheating In Scroll Compressors

Suction superheating plays a major role in determining the efficiency degradation of hermetic scroll compressors. Current models to predict superheating are usually experimentally calibrated and therefore can only be applied to existing compressors. This paper presents a thermal model to estimate suction superheating in scroll compressors, based on the NTU method for heat exchangers design. The model considers an isothermal surface exchanging heat with the gas in the suction path and in the discharge plenum. Compared to other models, the new approach described herein has the advantage of not requiring any experimental input data. The thermal model is coupled to a thermodynamic model and applied to evaluate the performance of a scroll compressor. The model was capable to predict the suction gas temperature in good agreement with experimental data, making it particularly useful for compressor design

Postural control and cognitive decline in older adults: Position versus velocity implicit motor strategy

The present study explored the impact of cognitive decline on postural control strategies in older adults with and without cognitive decline from mild cognitive impairment (MCI) to mild-to-moderate Alzheimer disease (MMAD). We hypothesized that the cognitive decline affected the postural control leading to higher bounding limits of COP velocity dynamics. Based on a cross-sectional design, 175 non-faller older adults were recruited in Angers University Hospital, France, including 50 cognitively healthy individuals [CHI] (mean age 76.42 +/- 4.84 years; 30% women), 64 age-and body mass index-matched participants with MCI (mean age 77.51 +/- 6.32 years; 39% women), and 61 age- and body mass index-matched participants with MMAD (mean age 78.44 +/- 3.97 years; 62% women). For all data collection of postural sway, the participants were asked to maintain quiet stance on force platform. The postural test consisted of two trials of quiet stance, with eyes open and with eyes closed. The COP parameters were mean and standard deviation (SD) of position, velocity and average absolute maximal velocity (AAMV) in anteroposterior and medio-lateral directions. Overall, the analysis concerning all COP parameters revealed a significant main effect of cognitive status on velocity-based variables, with post hoc comparisons evidencing that SD velocity and AAMV increased with cognitive impairment. The current findings suggest an active control (or corrective process) of COP velocity dynamics for CHI, whereas MCI and MMAD are affected by COP movements. (C) 2013 Elsevier B.V. All rights reserved

Vies moyennes de quelques niveaux du noyau 19F

Les énergies d'excitations et les vies moyennes de 9 niveaux du 19 F d'énergie inférieure à 6 MeV ont été déterminées à l'aide de la réaction 18O(d, nγ) 19F. De ces vies moyennes, mesurées à partir de la méthode du déplacement Doppler, ont été déduites certaines largeurs de transition M1 qui sont comparées aux prédictions de modèles en couches

A Neural Network to Predict the Temperature Distribution in Hermetic Refrigeration Compressors

The understanding of heat transfer interactions in refrigeration compressors is of fundamental importance to characterize their overall performance. Certain temperatures, such as those of the motor, oil, shell, and at suction and discharge chambers, have strong influence on the compressor electrical consumption and reliability. Experimental and numerical approaches have been successfully employed to characterize the thermal profile of compressors under different operating conditions. This paper presents a multi-layered feed-forward neural network developed to predict the main temperatures of a hermetic reciprocating compressor. Such a model can be used for different compressor layouts without major modifications, being a fast method for estimating temperatures without the solution of the compression cycle. Predictions of the neural network were compared with experimental data and numerical results from comprehensive thermodynamic simulations, and good agreement was observed in a wide range of evaporating and condensing temperatures. The neural network was found to predict the temperature distribution with sufficient accuracy for compressor analysis and development

A Combined Experimental-Numerical Procedure to Estimate Leakage Gap of Compressor Valves

Leakage through valves can significantly reduce the volumetric and isentropic efficiencies of compressors. Despite its importance for compressor design, the dimensional characterization of leakage gaps is not a trivial task. In this paper, we present a combined experimental-numerical method to estimate leakage gap of compressor valves. Measurements of leakage were carried out via the constant volume method, which is widely employed in the analysis of gas flow through microchannels. Additionally, numerical predictions were obtained with a one-dimensional flow model, taking into account viscous friction, slip at the walls, and gas compressibility. The leakage gap in the simulation model is adjusted so that predictions match the experimental results of leakage for different pressure differences. The procedure is applied for the analysis of three valve designs of refrigeration compressors