Simulation of the Emotion Dynamics in a Group of Agents in an Evacuation Situation

International audienceNowadays, more and more emergency evacuation simulations are used to evaluate the safety level of a building during an emergency evacuation after an accident. The heart of this kind of simulations is the simulation of human behavior because simulation results depend for a big part on how this behavior is simulated. However, human behaviors in a real emergency situation are determined by a lot of cognitive mechanisms. In order to make the simulation more realistic, plenty of factors (e.g. innate characteristics, perception of the environment, internal rules, personality and even emotions) that affect human behaviors must be taken into account. This paper focuses on the influence of emotions, and more precisely on the influence of their dynamics and propagation from an agent to another. The main contribution of this work is the development of a model of emotions taking into account their dynamics and their propagation and its integration in an evacuation simulation. The first results of the simulation show the benefits of considering emotion propagation

Lepton number violation and neutrino masses in 3-3-1 models

ABSTRACT: Lepton number violation and its relation to neutrino masses are investigated in several versions of the (3) ⊗ (3) ⊗ (1) model. Spontaneous and explicit violation and conservation of the lepton number are considered. In one of the models (the socalled economical one), the lepton number is spontaneously violated and it is found that the would be Majoron is not present because it is gauged away, providing in this way the longitudinal polarization component to a now massive gauge field

ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, ROOT offers packages for complex data modeling and fitting, as well as multivariate classification based on machine learning techniques. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks - e.g. data mining in HEP - by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way

Use of multivariate analysis to evaluate antigenic relationships between US BVDV vaccine strains and non-US genetically divergent isolates

Bovine viral diarrhea virus (BVDV) comprises two species, BVDV-1 and BVDV-2. But given the genetic diversity among pestiviruses, at least 22 subgenotypes are described for BVDV-1 and 3-4 for BVDV-2. Genetic characterization is generally accomplished through complete or partial sequencing and phylogeny, but it is not a reliable method to define antigenic relationships. The traditional method for evaluating antigenic relationships between pestivirus isolates is the virus neutralization (VN) assay, but interpretation of the data to define antigenic relatedness can be difficult to discern for BVDV isolates within the same BVDV species. Data from this study utilized a multivariate analysis for visualization of VN results to analyze the antigenic relationships between US vaccine strains and field isolates from Switzerland, Italy, Brazil, and the UK. Polyclonal sera were generated against six BVDV strains currently contained in vaccine formulations, and each serum was used in VNs to measure the titers against seven vaccine strains (including the six homologous strains) and 23 BVDV field isolates. Principal component analysis (PCA) was performed using VN titers, and results were interpreted from PCA clustering within the PCA dendrogram and scatter plot. The results demonstrated clustering patterns among various isolates suggesting antigenic relatedness. As expected, the BVDV-1 and BVDV-2 isolates did not cluster together and had the greatest spatial distribution. Notably, a number of clusters representing antigenically related BVDV-1 subgroups contain isolates of different subgenotypes. The multivariate analysis may be a method to better characterize antigenic relationships among BVDV isolates that belong to the same BVDV species and do not have distinct antigenic differences. This might be an invaluable tool to ameliorate the composition of current vaccines, which might well be important for the success of any BVDV control program that includes vaccination in its scheme

Use of multivariate analysis to evaluate antigenic relationships between US BVDV vaccine strains and non-US genetically divergent isolates.

Bovine viral diarrhea virus (BVDV) comprises two species, BVDV-1 and BVDV-2. But given the genetic diversity among pestiviruses, at least 22 subgenotypes are described for BVDV-1 and 3-4 for BVDV-2. Genetic characterization is generally accomplished through complete or partial sequencing and phylogeny, but it is not a reliable method to define antigenic relationships. The traditional method for evaluating antigenic relationships between pestivirus isolates is the virus neutralization (VN) assay, but interpretation of the data to define antigenic relatedness can be difficult to discern for BVDV isolates within the same BVDV species. Data from this study utilized a multivariate analysis for visualization of VN results to analyze the antigenic relationships between US vaccine strains and field isolates from Switzerland, Italy, Brazil, and the UK. Polyclonal sera were generated against six BVDV strains currently contained in vaccine formulations, and each serum was used in VNs to measure the titers against seven vaccine strains (including the six homologous strains) and 23 BVDV field isolates. Principal component analysis (PCA) was performed using VN titers, and results were interpreted from PCA clustering within the PCA dendrogram and scatter plot. The results demonstrated clustering patterns among various isolates suggesting antigenic relatedness. As expected, the BVDV-1 and BVDV-2 isolates did not cluster together and had the greatest spatial distribution. Notably, a number of clusters representing antigenically related BVDV-1 subgroups contain isolates of different subgenotypes. The multivariate analysis may be a method to better characterize antigenic relationships among BVDV isolates that belong to the same BVDV species and do not have distinct antigenic differences. This might be an invaluable tool to ameliorate the composition of current vaccines, which might well be important for the success of any BVDV control program that includes vaccination in its scheme

Contract Aware Components, 10 years after

The notion of contract aware components has been published roughly ten years ago and is now becoming mainstream in several fields where the usage of software components is seen as critical. The goal of this paper is to survey domains such as Embedded Systems or Service Oriented Architecture where the notion of contract aware components has been influential. For each of these domains we briefly describe what has been done with this idea and we discuss the remaining challenges.Comment: In Proceedings WCSI 2010, arXiv:1010.233

Evidence for Type Ia Supernova Diversity from Ultraviolet Observations with the Hubble Space Telescope

We present ultraviolet (UV) spectroscopy and photometry of four Type Ia supernovae (SNe 2004dt, 2004ef, 2005M, and 2005cf) obtained with the UV prism of the Advanced Camera for Surveys on the Hubble Space Telescope. This dataset provides unique spectral time series down to 2000 Angstrom. Significant diversity is seen in the near maximum-light spectra (~ 2000--3500 Angstrom) for this small sample. The corresponding photometric data, together with archival data from Swift Ultraviolet/Optical Telescope observations, provide further evidence of increased dispersion in the UV emission with respect to the optical. The peak luminosities measured in uvw1/F250W are found to correlate with the B-band light-curve shape parameter dm15(B), but with much larger scatter relative to the correlation in the broad-band B band (e.g., ~0.4 mag versus ~0.2 mag for those with 0.8 < dm15 < 1.7 mag). SN 2004dt is found as an outlier of this correlation (at > 3 sigma), being brighter than normal SNe Ia such as SN 2005cf by ~0.9 mag and ~2.0 mag in the uvw1/F250W and uvm2/F220W filters, respectively. We show that different progenitor metallicity or line-expansion velocities alone cannot explain such a large discrepancy. Viewing-angle effects, such as due to an asymmetric explosion, may have a significant influence on the flux emitted in the UV region. Detailed modeling is needed to disentangle and quantify the above effects.Comment: 17 pages, 13 figures, accepted by Ap

Binary systems and their nuclear explosions

Peer ReviewedPreprin

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe