Development of Detailed Chemistry Models for Boundary Layer Catalytic Recombination

During the (re-)entry phase of a space vehicle, the gas flow in the shock layer can be in a state of strong thermal non-equilibrium. Under these circumstances, the population of the internal energy levels of the atoms and molecules of the gas deviates from the Boltzmann distribution. A substantial increase of the heat flux transferred from the gas to the vehicle is possible, as the thermal protection system of the vehicle acts as a catalyzer. The objective of the paper is to show how thermal non-equilibrium and catalysis can jointly influence wall heat flux predictions. In order to study thermal non-equilibrium effects a coarse-grained State-to-State model for nitrogen is used coupled with a phenomenological model for catalysis. From the numerical simulations performed, an important effect on the heat flux has been observed due to the interaction of catalysis and thermal non-equilibrium at the wall

ON THE ANALYSIS OF TURBULENT FLOW SIGNALS BY ARTIFICIAL NEURAL NETWORKS AND ADAPTIVE TECHNIQUES

ABSTRACT Artificial Neural Networks (ANNs) and evolution are applied to the analysis of turbulent signals. In a first instance, a new trainable delay based artificial neural network is used to analyze Hot Wire Anemometer (HW) signals obtained at different positions within the wake of a circular cylinder with Reynolds number values ranging from 2000 to 8000. Results show that these networks are capable of performing accurate short term predictions of the turbulent signal. In addition, the ANNs can be set in a long term prediction mode resulting in a sort of non linear filter able to extract the features having to do with the larger eddies and coherent structures. In a second stage these networks are used to reconstruct a regularly sampled signal straight from the irregularly sampled one provided by a Laser Doppler Anemometer (LDA). The irregular sampling dynamics of the LDA signals is governed by the arrival of the seeding particles, superimposing the already complex turbulent signal characteristics. To cope with this complexity, an evolutionary based strategy is used to perform an adaptive and continuous online training of the ANNs. This approach permits obtaining a regularly sampled signal not by interpolating the original one, as it is often done, but by modeling it

HLA Allele E∗01:01 Is associated with a reduced risk of EBV-related classical hodgkin lymphoma independently of HLAA∗01/∗02

Background An inefficient immune response against Epstein-Barr virus (EBV) infection is related to the pathogenesis of a subgroup of classical Hodgkin lymphomas (cHL). Some EBV immuneevasion mechanisms target HLA presentation, including the non-classical HLA-E molecule. HLA-E can be recognized by T cells via the TCR, and it also regulates natural killer (NK) cell signaling through the inhibitory CD94/NKG2A receptor. Some evidences indicate that EBVinfected B-cells promote the proliferation of NK subsets bearing CD94/NKG2A, suggesting a relevant function of these cells in EBV control. Variations in CD94/NKG2A-HLA-E interactions could affect NK cell-mediated immunity and, consequently, play a role in EBV-driven transformation and lymphomagenesis. The two most common HLA-E alleles, E*01:01 and E*01:03, differ by a single amino acid change that modifies the molecule function. We hypothesized that the functional differences in these variants might participate in the pathogenicity of EBV. Aim We studied two series of cHL patients, both with EBV-positive and-negative cases, and a cohort of unrelated controls, to assess the impact of HLA-E variants on EBV-related cHL susceptibility. Results We found that the genotypes with at least one copy of E*01:01 (i.e., E*01:01 homozygous and heterozygous) were underrepresented among cHL patients from both series compared to controls (72.6% and 71.6% vs 83%, p = 0.001). After stratification by EBV status, we found low rates of E*01:01-carriers mainly among EBV-positive cases (67.6%). These reduced frequencies are seen independently of other factors such as age, gender, HLAA* 01 and HLA-A*02, HLA alleles positively and negatively associated with the disease (adjusted OR = 0.4, p = 0.001). Furthermore, alleles from both HLA loci exert a cumulative effect on EBV-associated cHL susceptibility. Conclusions These results indicate that E*01:01 is a novel protective genetic factor in EBV-associated cHL and support a role for HLA-E recognition on the control of EBV infection and lymphomagenesisThis work was supported by Miguel Servet programs CP09/00182 (NGL) and CP08/00218 (PM) and the Spanish Cancer Network (RTICC RD 06/ 0020/0047) all from Instituto de Salud Carlos III (FEDER)

Application Domain Study of Evolutionary Algorithms in Optimization Problems

ABSTRACT This paper deals with the problem of comparing and testing evolutionary algorithms, that is, the benchmarking problem, from an analysis point of view. A practical study of the application domain of four representative evolutionary algorithms is carried out using a relevant set of real-parameter function optimization benchmarks. The four selected algorithms are the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the Differential Evolution (DE), due to their successful results in recent studies, a Genetic Algorithm with real parameter operators, used here as a reference approach because it is probably the most familiar to researchers, and the Macroevolutionary algorithm (MA), which is not widely known but it shows a very remarkable behavior in some problems. The algorithms have been compared running several tests over the benchmark function set to analyze their capabilities from a practical point of view, in other words, in terms of their usability. The characterization of the algorithms is based on accuracy, stability and time consumption parameters thus establishing their operational scope and the type of optimization problems they are more suitable for

Engaging Undergraduates in Science Research: Not Just About Faculty Willingness.

Despite the many benefits of involving undergraduates in research and the growing number of undergraduate research programs, few scholars have investigated the factors that affect faculty members' decisions to involve undergraduates in their research projects. We investigated the individual factors and institutional contexts that predict faculty members' likelihood of engaging undergraduates in their research project(s). Using data from the Higher Education Research Institute's 2007-2008 Faculty Survey, we employ hierarchical generalized linear modeling to analyze data from 4,832 science, technology, engineering, and mathematics (STEM) faculty across 194 institutions to examine how organizational citizenship behavior theory and social exchange theory relate to mentoring students in research. Key findings show that faculty who work in the life sciences and those who receive government funding for their research are more likely to involve undergraduates in their research project(s). In addition, faculty at liberal arts or historically Black colleges are significantly more likely to involve undergraduate students in research. Implications for advancing undergraduate research opportunities are discussed

<i>Gaia</i> Data Release 1. Summary of the astrometric, photometric, and survey properties

Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7. Aims. A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release. Methods. The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue. Results. Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the HIPPARCOS and Tycho-2 catalogues – a realisation of the Tycho-Gaia Astrometric Solution (TGAS) – and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ∼3000 Cepheid and RR-Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr−1 for the proper motions. A systematic component of ∼0.3 mas should be added to the parallax uncertainties. For the subset of ∼94 000 HIPPARCOS stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr−1. For the secondary astrometric data set, the typical uncertainty of the positions is ∼10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ∼0.03 mag over the magnitude range 5 to 20.7. Conclusions. Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data