Comparison of modelled and monitored deposition fluxes of sulphur and nitrogen to ICP-forest sites in Europe

The EMEP MSC-W Eulerian chemical transport model, and its predictions of deposition of acidifying and eutrophying pollutants over Europe, play a key role in the development of emission control strategies for Europe. It is important that this model is tested against observational data. Here we compare the results of the EMEP model with measured data from 160 sites of the European Union/ICP Forest (Level II) monitoring network, for the years 1997 and 2000. This comparison comprises: (a) Precipitation amount, (b) Total deposition of SO42- to coniferous and deciduous forests, (c) Wet deposition of SO42-, NO3- and NH4+ in open field sites, and (d) Concentrations of SO42-, NO3- and NH4+ in precipitation. Concerning precipitation, the EMEP model and ICP network showed very similar overall levels (within 4% for 1997 and 11% for 2000). The correlation was, however, poor (r2=0.15-0.23). This can be attributed largely to the influence of a few outliers, combined with a small range of rainfall amounts for most points. Correlations between modelled and observed deposition values in this study were rather high (r2 values between 0.4-0.8 for most components and years), with mean values across all sites being within 30%. The EMEP model tends to give somewhat lower values for SO42-, NO3- and NH4+ wet deposition to ICP, but differences in mean values were within 20% in 1997 and 30% in 2000. Modelled and observed concentrations of SO 42-, NO3- and NH4+ in precipitation are very similar on average (differences of 0-14%), with good correlation between modelled and observed data (r 2=0.50-0.78). Differences between the EMEP model and ICP measurements are thought to arise from a mixture of problems with both the observations and model. However, the overall conclusion is that the EMEP model performs rather well in reproducing patterns of S and N deposition to European forests

Minimisation du Content par une méthode d'active set pour les équations d'équilibrage hydraulique conduites par la pression

International audienceA new content-based, box-constrained, active-set projected Newton method is presented that solves for the heads, the pipe flows, and the nodal outflows of a water distribution system in which nodal outflows are pressure dependent. The new method is attractive because, by comparison with the previously published weighted least-squares energy and mass residuals (EMR) damped Newton method, (1) it typically takes fewer iterations, (2) it does not require damping, (3) it takes less wall-clock time, (4) it does not require the addition of any virtual elements, and (5) it is algorithmically easier to deal with. Various pressure-outflow relationships (PORs), which model nodal outflows, were considered and two new PORs are presented. The new method is shown, by application to eight previously published case study networks with up to about 20,000 pipes and 18,000 nodes, to be up to five times faster than the EMR method and to take between 34% and 70% fewer iterations than the EMR method

Estimation de la demande pour les réseaux d'alimentation en eau potable : résolution d'un problème sous-déterminé par des algorithmes génétiques

International audienceModeling of water distribution systems is fundamental for the design, analysis and operation of any water network. As with all hydraulic models, water demands are one of the most important input components in the model. However, estimation of the demand parameters is usually complicated due to the stochastic behavior of the water consumptions. Several methods have been proposed for estimating water demands. Most of them have been developed based on given frameworks where the number of unknown parameters is assumed to be equal or less than the number of measurements. The outcomes, therefore, rely on this assumption, which can lead to significant approximation errors in real water distribution systems. The approach proposed in this paper does not require the number of known inputs to be equal to the number of variables. In fact, nodes in the model could each have a different demand pattern. The genetic algorithm approach adopted here shows that the average results of multiple GA runs can estimate the demand patterns at each node. Moreover, the model can also be used to estimate the flow rates and nodal heads at non-measured locations of the water network, although the accuracy of the estimation depends on number, type and location of the measurements. Results are shown and discussed for a literature case study tested for a 24-hour time period

A Minimum-Labeling Approach for Reconstructing Protein Networks across Multiple Conditions

The sheer amounts of biological data that are generated in recent years have driven the development of network analysis tools to facilitate the interpretation and representation of these data. A fundamental challenge in this domain is the reconstruction of a protein-protein subnetwork that underlies a process of interest from a genome-wide screen of associated genes. Despite intense work in this area, current algorithmic approaches are largely limited to analyzing a single screen and are, thus, unable to account for information on condition-specific genes, or reveal the dynamics (over time or condition) of the process in question. Here we propose a novel formulation for network reconstruction from multiple-condition data and devise an efficient integer program solution for it. We apply our algorithm to analyze the response to influenza infection in humans over time as well as to analyze a pair of ER export related screens in humans. By comparing to an extant, single-condition tool we demonstrate the power of our new approach in integrating data from multiple conditions in a compact and coherent manner, capturing the dynamics of the underlying processes.Comment: Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013

The role of cosmic ray pressure in accelerating galactic outflows

We study the formation of galactic outflows from supernova explosions (SNe) with the moving-mesh code AREPO in a stratified column of gas with a surface density similar to the Milky Way disk at the solar circle. We compare different simulation models for SNe placement and energy feedback, including cosmic rays (CR), and find that models that place SNe in dense gas and account for CR diffusion are able to drive outflows with similar mass loading as obtained from a random placement of SNe with no CRs. Despite this similarity, CR-driven outflows differ in several other key properties including their overall clumpiness and velocity. Moreover, the forces driving these outflows originate in different sources of pressure, with the CR diffusion model relying on non-thermal pressure gradients to create an outflow driven by internal pressure and the random-placement model depending on kinetic pressure gradients to propel a ballistic outflow. CRs therefore appear to be non-negligible physics in the formation of outflows from the interstellar medium.Comment: 8 pages, 4 figures, accepted for publication in ApJL; movie of simulated gas densities can be found here: http://www.h-its.org/tap-images/galactic-outflows