El Buddhisme i la medicina de l'esperit

En aquest article, el professor Ramon N. Prats, especialista en budisme, posa de manifest la relació que existeix entre aquesta saviesa oriental i les religions occidentals. Seguint alguns pensaments del Dalai Lama, el professor Prats investiga les possibilitats reals d'un diàleg interreligiós des del budisme

On gonihedric loops and quantum gravity

We present an analysis of the gonihedric loop model, a reformulation of the two dimensional gonihedric spin model, using two different techniques. First, the usual regular lattice statistical physics problem is mapped onto a height model and studied analytically. Second, the gravitational version of this loop model is studied via matrix models techniques. Both methods lead to the conclusion that the model has $c_{matter}=0$ for all values of the parameters of the model. In this way it is possible to understand the absence of a continuous transition

Analysis of uncertainty and variability in finite element computational models for biomedical engineering: characterization and propagation

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering

On global models for isolated rotating axisymmetric charged bodies; uniqueness of the exterior field

A relatively recent study by Mars and Senovilla provided us with a uniqueness result for the exterior vacuum gravitational field generated by an isolated distribution of matter in axial rotation in equilibrium in General Relativity. The generalisation to exterior electrovacuum gravitational fields, to include charged rotating objects, is presented here.Comment: LaTeX, 21 pages, uses iopart styl

Inter-individual and inter-strain differences in cognitive and social abilities of Dark Agouti and Wistar Han rats

BACKGROUND: Healthy animals showing extreme behaviours spontaneously that resemble human psychiatric symptoms are relevant models to study the natural psychobiological processes of maladapted behaviours. Healthy poor decision makers (PDMs) identified using a Rat Gambling Task, co-express a combination of cognitive and reward-based characteristics similar to symptoms observed in human patients with impulse-control disorders. The main goals of this study were to 1) confirm the existence of PDMs and their unique behavioural phenotypes in the Dark Agouti (DA) and WH, 2) to extend the behavioural profile of the PDMs to probability-based decision-making and social behaviours and 3) to discuss how the key traits of each strain could be relevant for biomedical research. METHODS: We compared cognitive abilities, natural behaviours and physiological responses in DA and WH rats using several tests. We analysed the results at the strain and the individual level. RESULTS: Previous findings in WH rats were reproduced and could be generalized to DA. Each PDM of either strain displayed a similar, naturally occurring, combination of behavioural traits, including possibly higher social rank, but no deficits in probability-based decision-making. A Random forest analysis revealed interesting discriminating traits between WH and DA. CONCLUSION: The reproducibility and conservation of the socio-cognitive and behavioural phenotypes of GDM and PDM individuals in the two genetically different strains of WH and DA support a good translational validity of these phenotypes. Both DA and WH rat strains present large phenotypic variations in behaviour pertinent for the study of the underlying mechanisms of poor decision making and associated disorders

Optimization of the experimental set-up for a turbulent separated shear flow control by plasma actuator using genetic algorithms

Since 1947, when Schubauer and Skramstad established the basis of the technology with its revolutionary work about steady state tools and mechanisms for the flow management, the progress of the flow control technology and the development of devices have progressed constantly. Anyway, the applicability of such devices is limited, and only few of them have arrived to the assembly workshop. The problem is that the range of actuation is still limited. Despite their operability limitations, flow control devices are of great interest for the aeronautical industry. The number of projects investigating this technology demonstrates the relevance of in the Fluid Dynamic field. The scientific interest focus not only on the industrial applications and the improvement of the technology, but also on the deep understanding of the physical phenomena associated to the flow separation, turbulence formation associated to the final drag reduction aim. A clear example of what has been mentioned is the EC MARS research project (MARS project, FP7 project number 266326). Its objectives are aimed to a better understanding of the Reynolds Stress and turbulent flow related to both drag reduction and flow control. The research was carried out through the analysis of several flow control devices and the optimization of the parameters for some of them was an important element of the research. When solving a traditional fluid dynamics optimisation problem numerical flowanalysis are used instead of experimental ones due to their lower cost and shorter needed time for evaluation of candidate solutions. Nevertheless, in the particular case of the selected flow control plasma devices the experimental measurement of the performance of each candidate configuration has been much quicker than a numerical analysis. For this reason, the corresponding optimisation problem has been solved by coupling an evolutionary optimization algorithm with an experimental device. This paper discusses the design quality and efficiency gained by this innovative coupling.Peer ReviewedPostprint (author's final draft

Turbulent separated shear flow control by surface plasma actuator: experimental optimization by genetic algorithm approach

The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a nonthermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step (U 0 = 15 m/s, Re h  = 30,000, Re θ  = 1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step (objective function #1) and also to measure the wall pressure fluctuation coefficients (objective function #2). An autonomous multi-variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty cycle of the high-voltage signal producing the surface plasma discharge. The single-objective optimization problems concern alternatively the minimization of the objective function #1 and the maximization of the objective function #2. The present paper demonstrates that when coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm can find the optimum forcing conditions in only a few generations. At the end of the iterative search process, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing process. The objective function #2 is maximized for an actuation at half the shear layer mode. In this specific forcing mode, time-resolved PIV shows that the vortex pairing is reduced and that the strong fluctuations of the wall pressure coefficients result from the periodic passages of flow structures whose size corresponds to the height of the step model

Multi-input genetic algorithm for experimental optimization of the reattachment downstream of a backward-facing step with surface plasma actuator

The practical interest of flow control approaches is no more debated as flow control provides an effective mean for considerably increasing the performances of ground or air transport systems, among many others applications. Here a fundamental configuration is investigated by using non-thermal surface plasma discharge. A dielectric barrier discharge is installed at the step corner of a backward-facing step (Reh=30000, Re¿=1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step. The primary objective of this paper is the coupling of a numerical optimizer with an experiment. More specifically, optimization by genetic algorithm is implemented experimentally in order to minimize the reattachment point downstream of the step model. Validation through inverse problem is firstly demonstrated. When coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm finds the optimum forcing conditions with a good convergence rate, the best control design variables being in agreement with the literature that uses other types of control devices than plasma. Indeed, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing phenomena. At the best forcing conditions, the mean flow reattachment is reduced by 20%. This article, with its experiment-based approach, demonstrates the robustness of a single-objective multi-design optimization method, and its feasibility for wind tunnel experiments.Postprint (published version

Experimental optimization by genetic algorithm for flow separation control with surface plasma actuator

Postprint (published version

Computational evaluation of cochlear implant surgery outcomes accounting for uncertainty and parameter variability

Cochlear implantation (CI) is a complex surgical procedure that restores hearing in patients with severe deafness. The successful outcome of the implanted device relies on a group of factors, some of them unpredictable or difficult to control. Uncertainties on the electrode array position and the electrical properties of the bone make it difficult to accurately compute the current propagation delivered by the implant and the resulting neural activation. In this context, we use uncertainty quantification methods to explore how these uncertainties propagate through all the stages of CI computational simulations. To this end, we employ an automatic framework, encompassing from the finite element generation of CI models to the assessment of the neural response induced by the implant stimulation. To estimate the confidence intervals of the simulated neural response, we propose two approaches. First, we encode the variability of the cochlear morphology among the population through a statistical shape model. This allows us to generate a population of virtual patients using Monte Carlo sampling and to assign to each of them a set of parameter values according to a statistical distribution. The framework is implemented and parallelized in a High Throughput Computing environment that enables to maximize the available computing resources. Secondly, we perform a patient-specific study to evaluate the computed neural response to seek the optimal post-implantation stimulus levels. Considering a single cochlear morphology, the uncertainty in tissue electrical resistivity and surgical insertion parameters is propagated using the Probabilistic Collocation method, which reduces the number of samples to evaluate. Results show that bone resistivity has the highest influence on CI outcomes. In conjunction with the variability of the cochlear length, worst outcomes are obtained for small cochleae with high resistivity values. However, the effect of the surgical insertion length on the CI outcomes could not be clearly observed, since its impact may be concealed by the other considered parameters. Whereas the Monte Carlo approach implies a high computational cost, Probabilistic Collocation presents a suitable trade-off between precision and computational time. Results suggest that the proposed framework has a great potential to help in both surgical planning decisions and in the audiological setting process