PIB potentiel et écart de PIB : quelques évaluations pour la France.

This Study and Research Paper is devoted to different estimates of the French economy's potential output and output gap. Several methods, which are presented in detail, are put forward to measure these indicators. The first two sections of the paper profile statistical univariate approaches: smoothing using the Hodrick-Prescott filter; and estimation of a trend, potentially including breaks. The next two sections extend the discussion on statistical techniques to multivariate cases. To be precise, they involve the analysis of structural VAR models and unobserved component models. The final section proposes a structural method for estimating potential output, where business sector output is described by a Cobb-Douglas function, while that of the non-business sector is assumed to be exogenous. For this structural method, the NAIRU has to be calculated before estimating the short to medium-term level of potential output.

Estimation of Fiber Orientations Using Neighborhood Information

Data from diffusion magnetic resonance imaging (dMRI) can be used to reconstruct fiber tracts, for example, in muscle and white matter. Estimation of fiber orientations (FOs) is a crucial step in the reconstruction process and these estimates can be corrupted by noise. In this paper, a new method called Fiber Orientation Reconstruction using Neighborhood Information (FORNI) is described and shown to reduce the effects of noise and improve FO estimation performance by incorporating spatial consistency. FORNI uses a fixed tensor basis to model the diffusion weighted signals, which has the advantage of providing an explicit relationship between the basis vectors and the FOs. FO spatial coherence is encouraged using weighted l1-norm regularization terms, which contain the interaction of directional information between neighbor voxels. Data fidelity is encouraged using a squared error between the observed and reconstructed diffusion weighted signals. After appropriate weighting of these competing objectives, the resulting objective function is minimized using a block coordinate descent algorithm, and a straightforward parallelization strategy is used to speed up processing. Experiments were performed on a digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data for both qualitative and quantitative evaluation. The results demonstrate that FORNI improves the quality of FO estimation over other state of the art algorithms.Comment: Journal paper accepted in Medical Image Analysis. 35 pages and 16 figure

Analytic Metaphysics versus Naturalized Metaphysics: The Relevance of Applied Ontology

The relevance of analytic metaphysics has come under criticism: Ladyman & Ross, for instance, have suggested do discontinue the field. French & McKenzie have argued in defense of analytic metaphysics that it develops tools that could turn out to be useful for philosophy of physics. In this article, we show first that this heuristic defense of metaphysics can be extended to the scientific field of applied ontology, which uses constructs from analytic metaphysics. Second, we elaborate on a parallel by French & McKenzie between mathematics and metaphysics to show that the whole field of analytic metaphysics, being useful not only for philosophy but also for science, should continue to exist as a largely autonomous field

Monitoring of ultrafine particles in French regional air quality network

Monitoring of ultrafine particles (UFP) in the ambient air is ongoing since 2012 in France. A national working group was created in 2014, including nowadays five French regional air quality monitoring networks. The main instrument selected to monitor UFP is the particle sizer “UFP-3031” (TSI Inc.). It measures the particle number concentration between 20 and 800 nm with six size channels. Two intercomparisons were organized in 2014 and 2015, which evaluated the accuracy of this instrument through a comparison with other techniques (such as Scanning Mobility Particle Sizer, SMPS), and through uncertainty calculations. Recently, several networks have been also equipped with CPC (condensation particle counter) to be able to measure the total UFP number concentration from 7 nm. This work presents the main results of short and long-term measurement of UFP which have been carried out in various environments: urban/traffic sites, near heavy industry zones (Dunkerque and Fos-sur-Mer in northern and southern France, respectively), near harbor area (Nice)… For urban/ traffic environment, the number concentration and size distribution are compared at the national level; it appears that they vary significantly depending on the influence of road traffic around the site. The concentration levels near traffic sites are at least twice than in the urban area, especially for UFP smaller than 50 nm. Additionally, the UFP measurement also makes it possible to improve the identification of specific sources and to understand the atmospheric physicochemical phenomena. The relationship between UFP and industrial emissions, ferries, forest fires was clearly identified in different places in France. During summer, the UFP monitoring also shows the formation of new particles (between 20-30 nm or smaller) in the afternoon, due to photochemical reactions. From 2019, the French national strategy on UFP will start putting a particular emphasis on the impact of UFP on human health

Nanoscale temperature measurements using non-equilibrium Brownian dynamics of a levitated nanosphere

Einstein realised that the fluctuations of a Brownian particle can be used to ascertain properties of its environment. A large number of experiments have since exploited the Brownian motion of colloidal particles for studies of dissipative processes, providing insight into soft matter physics, and leading to applications from energy harvesting to medical imaging. Here we use optically levitated nanospheres that are heated to investigate the non-equilibrium properties of the gas surrounding them. Analysing the sphere's Brownian motion allows us to determine the temperature of the centre-of-mass motion of the sphere, its surface temperature and the heated gas temperature in two spatial dimensions. We observe asymmetric heating of the sphere and gas, with temperatures reaching the melting point of the material. This method offers new opportunities for accurate temperature measurements with spatial resolution on the nanoscale, and a new means for testing non-equilibrium thermodynamicsComment: 5 pages, 4 figures, supplementary material available upon reques

Repeatability of IVIM biomarkers from diffusion-weighted MRI in head and neck:Bayesian probability versus neural network

Purpose: The intravoxel incoherent motion (IVIM) model for DWI might provide useful biomarkers for disease management in head and neck cancer. This study compared the repeatability of three IVIM fitting methods to the conventional nonlinear least-squares regression: Bayesian probability estimation, a recently introduced neural network approach, IVIM-NET, and a version of the neural network modified to increase consistency, IVIM-NETmod. Methods: Ten healthy volunteers underwent two imaging sessions of the neck, two weeks apart, with two DWI acquisitions per session. Model parameters (ADC, diffusion coefficient (Formula presented.), perfusion fraction (Formula presented.), and pseudo-diffusion coefficient (Formula presented.)) from each fit method were determined in the tonsils and in the pterygoid muscles. Within-subject coefficients of variation (wCV) were calculated to assess repeatability. Training of the neural network was repeated 100 times with random initialization to investigate consistency, quantified by the coefficient of variance. Results: The Bayesian and neural network approaches outperformed nonlinear regression in terms of wCV. Intersession wCV of (Formula presented.) in the tonsils was 23.4% for nonlinear regression, 9.7% for Bayesian estimation, 9.4% for IVIM-NET, and 11.2% for IVIM-NETmod. However, results from repeated training of the neural network on the same data set showed differences in parameter estimates: The coefficient of variances over the 100 repetitions for IVIM-NET were 15% for both (Formula presented.) and (Formula presented.), and 94% for (Formula presented.); for IVIM-NETmod, these values improved to 5%, 9%, and 62%, respectively. Conclusion: Repeatabilities from the Bayesian and neural network approaches are superior to that of nonlinear regression for estimating IVIM parameters in the head and neck

Diffusion-weighted whole-body imaging with background body signal suppression (DWIBS): features and potential applications in oncology

Diffusion-weighted magnetic resonance imaging (DWI) provides functional information and can be used for the detection and characterization of pathologic processes, including malignant tumors. The recently introduced concept of “diffusion-weighted whole-body imaging with background body signal suppression” (DWIBS) now allows acquisition of volumetric diffusion-weighted images of the entire body. This new concept has unique features different from conventional DWI and may play an important role in whole-body oncological imaging. This review describes and illustrates the basics of DWI, the features of DWIBS, and its potential applications in oncology

Effects of microperfusion in hepatic diffusion weighted imaging

Clinical hepatic diffusion weighted imaging (DWI) generally relies on mono-exponential diffusion. The aim was to demonstrate that mono-exponential diffusion in the liver is contaminated by microperfusion and that the bi-exponential model is required. Nineteen fasting healthy volunteers were examined with DWI (seven b-values) using fat suppression and respiratory triggering (1.5 T). Five different regions in the liver were analysed regarding the mono-exponentially fitted apparent diffusion coefficient (ADC), and the bi-exponential model: molecular diffusion (D (slow) ) microperfusion (D (fast) ) and the respective fractions (f (slow/fast)). Data were compared using ANOVA and Kruskal-Wallis tests. Simulations were performed by repeating our data analyses, using just the DWI series acquired with b-values approximating those of previous studies. Median mono-exponentially fitted ADCs varied significantly (P <0.001) between 1.107 and 1.423 x 10(-3) mm(2)/s for the five regions. Bi-exponential fitted D-slow varied between 0.923 and 1.062 x 10(-3) mm(2)/s without significant differences (P = 0.140). D (fast) varied significantly, between 17.8 and 46.8 x 10(-3) mm(2)/s (P <0.001). F-tests showed that the diffusion data fitted the bi-exponential model significantly better than the mono-exponential model (F > 21.4, P <0.010). These results were confirmed by the simulations. ADCs of normal liver tissue are significantly dependent on the measurement location because of substantial microperfusion contamination; therefore the bi-exponential model should be used. Diffusion weighted MR imaging helps clinicians to differentiate tumours by diffusion properties Fast moving water molecules experience microperfusion, slow molecules diffusion Hepatic diffusion should be measured by bi-exponential models to avoid microperfusion contamination Mono-exponential models are contaminated with microperfusion, resulting in apparent regional diffusion differences Bi-exponential models are necessary to measure diffusion and microperfusion in the liver