An Experimental and Theoretical Study of Radiative Extinction of Diffusion Flames

In a recent paper on 'Observations of candle flames under various atmospheres in microgravity' by Ross et al., it was found that for the same atmosphere, the burning rate per unit wick surface area and the flame temperature were considerably reduced in microgravity as compared with normal gravity. Also, the flame (spherical in microgravity) was much thicker and further removed from the wick. It thus appears that the flame becomes 'weaker' in microgravity due to the absence of buoyancy generated flow which serves to transport the oxidizer to the combustion zone and remove the hot combustion products from it. The buoyant flow, which may be characterized by the strain rate, assists the diffusion process to execute these essential functions for the survival of the flame. Thus, the diffusion flame is 'weak' at very low strain rates and as the strain rate increases the flame is initially 'strengthened' and eventually it may be 'blown out'. The computed flammability boundaries of T'ien show that such a reversal in material flammability occurs at strain rates around 5 sec. At very low or zero strain rates, flame radiation is expected to considerably affect this 'weak' diffusion flame because: (1) the concentration of combustion products which participate in gas radiation is high in the flame zone; and (2) low strain rates provide sufficient residence time for substantial amounts of soot to form which is usually responsible for a major portion of the radiative heat loss. We anticipate that flame radiation will eventually extinguish this flame. Thus, the objective of this project is to perform an experimental and theoretical investigation of radiation-induced extinction of diffusion flames under microgravity conditions. This is important for spacecraft fire safety

An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning

A variety of meta-heuristic search algorithms have been introduced for optimising software release planning. However, there has been no comprehensive empirical study of different search algorithms across multiple different real-world datasets. In this article, we present an empirical study of global, local, and hybrid meta- and hyper-heuristic search-based algorithms on 10 real-world datasets. We find that the hyper-heuristics are particularly effective. For example, the hyper-heuristic genetic algorithm significantly outperformed the other six approaches (and with high effect size) for solution quality 85% of the time, and was also faster than all others 70% of the time. Furthermore, correlation analysis reveals that it scales well as the number of requirements increases

Development of the CNPP Prices Database

Data are available at: http://www.cnpp.usda.gov/USDAFoodPlansCostofFood.htmfood prices, USDA Food Plans, NHANES, CNPP Prices Database, Consumer/Household Economics, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety,

Military Medicine Interest Groups in U.S. Medical Schools

Medical student interest groups are organizations that help expose students to different medical specialties and fields of medicine while in medical school. Military medicine interest groups (MMIGs) are a particular type of interest group that spreads information about military medicine, fosters mentorship, and camaraderie between students and military faculty, and increases the opportunities for leadership while in medical school. Surveys were sent to all U.S. medical schools to determine how many schools had an MMIG. If a medical school had a group, a second survey was sent to the student leader to determine more information about how their group operated (such as type of participants, funding sources, activities, faculty involvement, military health care provider involvement, etc.). Fifty-six percent of U.S. medical schools who responded were found to have an MMIG and most participants were students in the Health Professions Scholarship Program. Information about military medicine was found to be the biggest impact of having a group at a medical school and student leaders expressed they wished to have more military health care provider involvement. The results of this study could help start MMIGs at other medical schools, as well as give ideas to current MMIGs on how other groups operate

Military Medicine Interest Groups in U.S. Medical Schools

Medical student interest groups are organizations that help expose students to different medical specialties and fields of medicine while in medical school. Military medicine interest groups (MMIGs) are a particular type of interest group that spreads information about military medicine, fosters mentorship, and camaraderie between students and military faculty, and increases the opportunities for leadership while in medical school. Surveys were sent to all U.S. medical schools to determine how many schools had an MMIG. If a medical school had a group, a second survey was sent to the student leader to determine more information about how their group operated (such as type of participants, funding sources, activities, faculty involvement, military health care provider involvement, etc.). Fifty-six percent of U.S. medical schools who responded were found to have an MMIG and most participants were students in the Health Professions Scholarship Program. Information about military medicine was found to be the biggest impact of having a group at a medical school and student leaders expressed they wished to have more military health care provider involvement. The results of this study could help start MMIGs at other medical schools, as well as give ideas to current MMIGs on how other groups operate

The Tropical Forest and Fire Emissions Experiment: Emission, Chemistry, and Transport of Biogenic Volatile Organic Compounds in the Lower Atmosphere Over Amazonia

Airborne and ground-based mixing ratio and flux measurements using eddy covariance (EC) and for the first time the mixed layer gradient (MLG) and mixed layer variance (MLV) techniques are used to assess the impact of isoprene and monoterpene emissions on atmospheric chemistry in the Amazon basin. Average noon isoprene (7.8 +/- 2.3 mg/m(2)/h) and monoterpene fluxes (1.2 +/- 0.5 mg/m(2)/h)compared well between ground and airborne measurements and are higher than fluxes estimated in this region during other seasons. The biogenic emission model, Model of Emissions of Gases and Aerosols from Nature (MEGAN), estimates fluxes that are within the model and measurement uncertainty and can describe the large observed variations associated with land-use change in the region north-west of Manaus. Isoprene and monoterpenes accounted for similar to 75% of the total OH reactivity in this region and are important volatile organic compounds (VOCs) for modeling atmospheric chemistry in Amazonia. The presence of fair weather clouds ( cumulus humilis) had an important impact on the vertical distribution and chemistry of VOCs through the planetary boundary layer (PBL), the cloud layer, and the free troposphere (FT). Entrainment velocities between 10: 00 and 11: 30 local time ( LT) are calculated to be on the order of 8-10 cm/s. The ratio of methyl-vinyl-ketone (MVK) and methacrolein (MAC) ( unique oxidation products of isoprene chemistry) with respect to isoprene showed a pronounced increase in the cloud layer due to entrainment and an increased oxidative capacity in broken cloud decks. A decrease of the ratio in the lower free troposphere suggests cloud venting through activated clouds. OH modeled in the planetary boundary layer using a photochemical box model is much lower than OH calculated from a mixed layer budget approach. An ambient reactive sesquiterpene mixing ratio of 1% of isoprene would be sufficient to explain most of this discrepancy. Increased OH production due to increased photolysis in the cloud layer balances the low OH values modeled for the planetary boundary layer. The intensity of segregation ( Is) of isoprene and OH, defined as a relative reduction of the reaction rate constant due to incomplete mixing, is found to be significant: up to 39 +/- 7% in the similar to 800-m-deep cloud layer. The effective reaction rate between isoprene and OH can therefore vary significantly in certain parts of the lower atmosphere

The rocket experiment demonstration of a soft x-ray polarimeter (REDSoX Polarimeter)

The Rocket Experiment Demonstration of a Soft X-ray Polarimeter (REDSoX Polarimeter) is a sounding rocket instrument that can make the first measurement of the linear X-ray p olarization of an extragalactic source in the 0.2-0.8 keV band as low as 10%. We employ multilayer-coated mirrors as Bragg reflectors at the Brewster angle. By matching the dispersion of a spectrometer using replicated optics from MSFC and critical angle transmission gratings from MIT to three laterally graded multilayer mirrors (LGMLs), we achieve polarization modulation factors over 90%. We present a novel arrangement of gratings, designed optimally for the purpose of polarimetry with a converging beam. The entrance aperture is divided into six equal sectors; pairs of blazed gratings from opposite sectors are oriented to disperse to the same LGML. The LGML position angles are 120 degrees to each other. CCD detectors then measure the intensities of the dispersed spectra after reflection and polarizing by the LGMLs, giving the three Stokes parameters needed to determine a source's linear polarization fraction and orientation. A current grant is funding further development to improve the LGMLs. Sample gratings for the project have been fabricated at MIT and the development team continues to improve them under separate funding. Our technological approach is the basis for a possible orbital mission. Keywords: X-ray, polarimeter, astronomy, multilayer, mirror, gratingUnited States. National Aeronautics and Space Administration (Grant NNX17AE11G)United States. National Aeronautics and Space Administration (Grant NNX12AH12G)Kavli Institute for Astrophysics and Space Research (Research Investment Grant)United States. National Aeronautics and Space Administration (Grant NNX17AG43G)United States. National Aeronautics and Space Administration (Grant NNX15AC43G

A new paradigm of quantifying ecosystem stress through chemical signatures

Stress-induced emissions of biogenic volatile organic compounds (VOCs) from terrestrial eco- systems may be one of the dominant sources of VOC emissions worldwide. Understanding the ecosystem stress response could reveal how ecosystems will respond and adapt to climate change and, in turn, quan- tify changes in the atmospheric burden of VOC oxidants and secondary organic aerosols. Here, we argue, based on preliminary evidence from several opportunistic measurement sources, that chemical signatures of stress can be identified and quantified at the ecosystem scale. We also outline future endeavors that we see as next steps toward uncovering quantitative signatures of stress, including new advances in both VOC data collection and analysis of "big data.

Satellite-derived Constraints on the Effect of Drought Stress on Biogenic Isoprene Emissions in the Southeast US

While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, there remain large uncertainties in isoprene emissions under stressed conditions. Here we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the Southeast U.S. (SE US), a region of high isoprene emissions and prone to drought. OMI HCHO column density is found to be 5.3 % (mild drought) &ndash; 19.8 % (severe drought) higher than that in no-drought conditions. A global chemical transport model, GEOS-Chem, with the MEGAN2.1 emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.4&ndash;2.0 times of OMI HCHO, suggesting the need for a drought-stress algorithm in the model. By minimizing the model-to-OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (&gamma;d_OMI) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6 % (mild drought) &ndash; 20.7 % (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With &gamma;d_OMI the model predicts a non-uniform trend of increase in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site&rsquo;s isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellite-based drought stress factor performs better in capturing the regional scale drought-isoprene responses as indicated by the close-to-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model&rsquo;s high bias in organic aerosols (OA) simulations by 6.60 % (mild drought) to 11.71 % (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene suppressing effect on oxidants, with an &lt;1 ppb increase in O3 in high-isoprene regions and a 1&ndash;3 ppbv decrease in O3 in low-isoprene regions. This study demonstrates the unique value of exploiting long-term satellite observations to develop empirical stress algorithms on biogenic emissions where in situ flux measurements are limited.</p

Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses

This is the publisher's version, also available electronically from http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00031/abstractThe brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience