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NASA NTRS2023

Territory and Population Attributes Affect Florida Scrub-Jay Fecundity in Fire Adapted Ecosystems

Fecundity, the number of young produced by a breeding pair during a breeding season, is a primary component in evolutionary and ecological theory and applications. Fecundity can be influenced by many environmental factors and requires long-term study due to the range of variation in ecosystem dynamics. Fecundity data often include a large proportion of zeros when many pairs fail to produce any young during a breeding season due to nest failure or when all young die independently after fledging. We conducted color banding and monthly censuses of Florida scrub-jays (Aphelocoma coerulescens) across 31 years, 15 populations, and 761 territories along central Florida’s Atlantic coast. We quantified how fecundity (juveniles/pair-year) was influenced by habitat quality, presence/absence of nonbreeders, population density, breeder experience, and rainfall, with a zero-inflated Bayesian hierarchical model including both a Bernoulli (e.g., brood success) and a Poisson (counts of young) submodel, and random effects for year, population, and territory. The results identified the importance of increasing “strong” quality habitat, which was a mid-successional state related to fire frequency and extent, because strong territories, and the proportion of strong territories in the overall population, influenced fecundity of breeding pairs. Populations subject to supplementary feeding also had greater fecundity. Territory size, population density, breeder experience, and rainfall surprisingly had no or small effects. Different mechanisms appeared to cause annual variation in fecundity, as estimates of random effects were not correlated between the success and count submodels. The increased fecundity for pairs with nonbreeders, compared to pairs without, identified empirical research needed to understand how the proportion of low-quality habitats influences population recovery and sustainability, because dispersal into low-quality habitats can drain nonbreeders from strong territories and decrease overall fecundity. We also describe how long term study resulted in reversals in our understanding because of complications involving habitat quality, sociobiology, and population density.

Long-term studies↗
NASA NTRSDate not supplied

Atmospheric Formaldehyde Trend and Its Source Attributions in the Recent Decades

Formaldehyde (CH 2 O) is one of the most important reactive trace gases in the atmosphere with an important role in tropospheric chemistry and in the control of surface air quality. Sources of CH 2 O includes direct emissions and in situ production from the oxidation of volatile organic compounds (VOCs). In this study, we investigate spatial-temporal variations in atmospheric CH 2 O and how changes in CH 2 O couple with tropospheric ozone variability during the past two decades using recently available retrievals from the Ozone Monitoring Instrument (OMI) aboard NASA’s Aura satellite and the RefD1 simulation from the NASA Goddard Earth Observing System Chemistry Climate Model (GEOSCCM). Our initial analysis provides evidence of significant increases in atmospheric CH 2 O concentrations over several of the world’s major anthropogenic regions, including China, India, and the Middle East, and several biomass burning regions, including South America, southern Africa, boreal regions and Indonesia. We furthermore will identify regional source contributions to CH 2 O trends, including anthropogenic VOCs emissions, biogenic isoprene emissions and open fires. In particular, we will focus on anthropogenic emission regions to examine the role of changing anthropogenic VOC emissions on CH 2 O abundance.

Atmospheric Formaldehyde↗
NASA NTRS1994

Understanding software faults and their role in software reliability modeling

This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain.

Munson, John C.↗
NASA NTRS2020

Observational Constraints on the Effective Climate Sensitivity from the Historical Period

The observed warming in the atmosphere and ocean can be used to estimate the climate sensitivity linked to present-day feedbacks, which is referred to as the effective climate sensitivity (Shist). However, such an estimate is affected by uncertainty in the radiative forcing, particularly aerosols, over the historical period. Here, we make use of detection and attribution techniques to derive the surface air temperature and ocean warming that can be attributed directly to greenhouse gas increases. These serve as inputs to a simple energy budget to infer the likelihood of Shist in response to observed greenhouse gases increases over two time periods (1862–2012 and 1955–2012). The benefit of using greenhouse gas attributable quantities is that they are not subject to uncertainties in the aerosol forcing (other than uncertainty in the attribution to greenhouse gas versus aerosol forcing not captured by the multi-model aerosol response pattern). The resulting effective climate sensitivity estimate, Shist, ranges from 1.3 °Cto 3.1 °C(5%–95% range) over the full instrumental period (1862–2012) for our best estimate, and gets slightly wider when considering further uncertainties. This estimate increases to 1.7 °C–4.6 °C if using the shorter period (1955–2012).We also evaluate the climate model simulated surface air temperature and ocean heat content increase in response to greenhouse gas forcing over the same periods, and compare them with the observationally-constrained values.We find that that the ocean warming simulated in greenhouse gas only simulations in models considered here is consistent with that attributed to greenhouse gas increases from observations, while one model simulates more greenhouse gas-induced surface air warming than observed. However, other models with sensitivity outside our range show greenhouse gas warming that is consistent with that attributed in observations, emphasising that feedbacks during the historical period may differ from the feedbacks at CO2 doubling and from those at true equilibrium.

Tokarska, Katarzyna B↗
NASA NTRS2022

Global Premature Mortality By Dust and Pollution PM 2.5 Estimated From Aerosol Reanalysis of the Modern-Era Retrospective Analysis for Research and Applications, Version 2

This study quantifies global premature deaths attributable to long-term exposure of ambient PM 2.5 , or PM 2.5 -attributable mortality, by dust and pollution sources. We used NASA’s Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol reanalysis product for PM 2.5 and the cause-specific relative risk (RR) from the integrated exposure-response (IER) model to estimate global PM2.5-attributable mortality for five causes of deaths, namely ischaemic heart disease (IHD), cerebrovascular disease (CEV) or stroke, lung cancer (LC), chronic obstructive pulmonary disease (COPD), and acute lower respiratory infection (ALRI). The estimated yearly global PM 2.5 -attributable mortality in 2019 amounts to 2.89 (1.38–4.48) millions, which is composed of 1.19 (0.73–1.84) million from IHD, 1.01 (0.35–1.55) million from CEV, 0.29 (0.11–0.48) million from COPD, 0.23 (0.14–0.33) million from ALRI, and 0.17 (0.04–0.28) million from LC (the numbers in parentheses represent the estimated mortality range due corresponding to RR spread at the 95% confidence interval). The mortality counts vary with geopolitical regions substantially, with the highest number of deaths occurring in Asia. China and India account for 40% and 23% of the global PM 2.5 -attributable deaths, respectively. In terms of sources of PM 2.5 , about 22% of the global all-cause PM 2.5 -attributable deaths are caused by desert dust. The largest dust attribution is 37% for ALRI. The relative contributions of dust and pollution sources vary with the causes of deaths and geographical regions. Enforcing air pollution regulations to transfer areas from PM 2.5 nonattainment to PM2.5 attainment can have great health benefits. Being attainable with the United States air quality standard (AQS) of 15 μg/m 3 globally would have avoided nearly 40% or 1.2 million premature deaths. The most recent update of PM 2.5 guideline from 10 to 5 μg/m 3 by the World Health Organization (WHO) would potentially save additional one million lives. Our study highlights the importance of distinguishing aerodynamic size from geometric size in accurately assessing the global health burden of PM 2.5 and particularly for dust. A use of geometric size in diagnosing dust PM 2.5 from the model simulation, a common approach in current health burden assessment, could overestimate the PM 2.5 level in the dust belt by 40–170%, leading to an overestimate of global all-cause mortality by 1 million or 32%.

PM2.5↗
NASA NTRS1988

The 2.5-12 micrometers spectrum of comet Halley from the IKS-VEGA experiment

The infrared instrument IKS flown on board the VEGA space probes was designed for the detection of emission bands of parent molecules, and for a measurement of the size and temperature of the thermal emitting nuclear region. The instrument had three channels with cooled detectors: an "imaging channel" designed to modulate the signal of the nucleus and two spectroscopic channels operating at 2.5-5 and 6-12 micrometers, respectively, equipped with circular variable filters of resolving power approximately 50. This paper presents and discusses the results from the spectral channels. On VEGA 1, usable spectra were obtained at distances D from the comet nucleus ranging from 250,000 to 40,000 km corresponding to fields of view 4000 and 700 km in diameter, respectively. The important internal background signal caused by the instrument itself, which could not be cooled, had to be eliminated. Since no sky chopping was performed, we obtain difference spectra between the current spectrum and a reference spectrum with little or no cometary signal taken at the beginning of the observing sequence (D approximately 200,000 km). Final discrimination between cometary signal and instrumental background is achieved using their different time evolution, since the instrumental background is proportional to the slow temperature drift of the instrument, and the cometary signal due to parent molecules or dust grains is expected to vary in first order as D-1. The 2.5-5 micrometers IKS spectra definitely show strong narrow signals at 2.7 and 4.25 micrometers, attributed to the nu 3 vibrational bands of H2O and CO2, respectively, and a broader signal in the region 3.2-3.5 micrometers, which may be attributed to CH-bearing molecules. All these signals present the expected D-1 intensity variation. Weaker emission features at 3.6 and 4.7 micrometers could correspond to the nu 1 and nu 5 bands of H2CO and the (1 - 0) band of CO, respectively. Molecular production rates are derived from the observed emissions, assuming that they are due to resonance fluorescence excited by the Sun's infrared radiation. For the strong bands of H2O and CO2, the rovibrational lines are optically thick, and radiative transfer is taken into account. We derive production rates, at the moment of the VEGA 1 flyby, of approximately 10(30) sec-1 for H2O, approximately 2.7 x 10(28) sec-1 for CO2, approximately 5 x 10(28) sec-1 for CO, and 4 x 10(28) sec-1 for H2CO, if attributions to CO and H2CO are correct. The production rate of carbon atoms in CH-bearing molecules is approximately 9 x 10(29) sec-1 assuming fluorescence of molecules in the gas phase, but could be much less if the 3.2-3.5 micrometers emission is attributed to C-H stretch in polycyclic aromatic hydrocarbons or small organic grains. In addition, marginal features are present at 4.85 and 4.45 micrometers, tentatively attributed to OCS and molecules with the CN group, respectively. Broad absorption at 2.8-3.0 micrometers, as well as a narrow emission at 3.15 micrometers, which follow well the D-1 intensity variation, might be due to water ice. Emission at 2.8 micrometers is also possibly present, and might be due to OH created in vibrationally excited states after water photodissociation. The 6-12 micrometers spectrum does not show any molecular emission, nor emission in the 7.5-micrometers region. The spectrum is dominated by silicate emission showing a double structure with maxima at 9.0 and 11.2 micrometers, which suggests the presence of olivine.

NASA Discipline Number 52-10↗
NASA NTRS2009

Influence Diagram Use With Respect to Technology Planning and Investment

Influence diagrams are relatively simple, but powerful, tools for assessing the impact of choices or resource allocations on goals or requirements. They are very general and can be used on a wide range of problems. They can be used for any problem that has defined goals, a set of factors that influence the goals or the other factors, and a set of inputs. Influence diagrams show the relationship among a set of results and the attributes that influence them and the inputs that influence the attributes. If the results are goals or requirements of a program, then the influence diagram can be used to examine how the requirements are affected by changes to technology investment. This paper uses an example to show how to construct and interpret influence diagrams, how to assign weights to the inputs and attributes, how to assign weights to the transfer functions (influences), and how to calculate the resulting influences of the inputs on the results. A study is also presented as an example of how using influence diagrams can help in technology planning and investment. The Space Propulsion Synergy Team (SPST) used this technique to examine the impact of R&D spending on the Life Cycle Cost (LCC) of a space transportation system. The question addressed was the effect on the recurring and the non-recurring portions of LCC of the proportion of R&D resources spent to impact technology objectives versus the proportion spent to impact operational dependability objectives. The goals, attributes, and the inputs were established. All of the linkages (influences) were determined. The weighting of each of the attributes and each of the linkages was determined. Finally the inputs were varied and the impacts on the LCC determined and are presented. The paper discusses how each of these was accomplished both for credibility and as an example for future studies using influence diagrams for technology planning and investment planning.

Levack, Daniel J. H.↗
NASA NTRS1983

Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.

Logan, T. L.↗
NASA NTRS1984

Remote sensing as a mineral prospecting technique

Remote sensing and its application as an alternative technique to mineral resource exploration are reviewed. Emphasis is given here to the analysis of the three basic attributes of remote sensing, i.e., spatial attributes related to regional structural mapping, spectral attributes related to rock discrimination and seasonal attributes related to geobotanic anomalies mapping, all of which are employed in mineral exploration. Special emphasis is given to new developments of the Thematic Mapper of the LANDSAT-5, principally with reference to the application of the bands 1.6 and 2.2 microns to map hydrothermally altered rocks and the band of red and blue shift to geobotanical anomalies mapping.

Meneses, P. R.↗
NASA NTRS1987

Radiation and temperature effects in gallium arsenide, indium phosphide and silicon solar cells

The effects of radiation on performance are determined for both n(+)p and p(+)n GaAs and InP cells and for silicon n(+)p cells. It is found that the radiation resistance of InP is greater than that of both GaAs and Si under 1 MeV electron irradiation. For silicon, the observed decreased radiation resistance with decreased resistivity is attributed to the presence of a radiation induced boron-oxygen defect. Comparison of radiation damage in both p(+)n and n(+)p GaAs cells yields a decreased radiation resistance for the n(+)p cell attributable to increased series resistance, decreased shunt resistance, and relatively greater losses in the cell's p-region. For InP, the n(+)p configuration is found to have greater radiation resistance than the p(+)n cell. The increased loss in this latter cell is attributed to losses in the cell's emitter region. Temperature dependency results are interpreted using a theoretical relation for dVoc/cT which predicts that increased Voc should results in decreased numerical values for dPm/dT. The predicted correlation is observed for GaAs but not for InP a result which is attributed to variations in cell processing.

Weinberg, I.↗
NASA NTRS1987

Stochastic nature of Landsat MSS data

A multiple series generalization of the ARIMA models is used to model Landsat MSS scan lines as sequences of vectors, each vector having four elements (bands). The purpose of this work is to investigate if Landsat scan lines can be described by a general multiple series linear stochastic model and if the coefficients of such a model vary as a function of satellite system and target attributes. To accomplish this objective, an exploratory experimental design was set up incorporating six factors, four representing target attributes - location, cloud cover, row (within location), and column (within location) - and two factors representing system attributes - satellite number and detector bank. Each factor was included in the design at two levels and, with two replicates per treatment, 128 scan lines were analyzed. The results of the analysis suggests that a multiple AR(4) model is an adequate representation across all scan lines. Furthermore, the coefficients of the AR(4) model vary with location, particularly changes in physiography (slope regimes), and with percent cloud cover, but are insensitive to changes in system attributes.

Labovitz, M. L.↗
NASA NTRS1987

Radiation and temperature effects in gallium arsenide, indium phosphide, and silicon solar cells

The effects of radiation on performance are determined for both n+p and p+n GaAs and InP cells and for silicon n+p cells. It is found that the radiation resistance of InP is greater than that of both GaAs and Si under 1-MeV electron irradiation. For silicon, the observed decreased radiation resistance with decreased resistivity is attributed to the presence of a radiation-induced boron-oxygen defect. Comparison of radiation damage in both p+n and n+p GaAs cells yields a decreased radiation resistance for the n+p cell attributable to increased series resistance, decreased shunt resistance, and relatively greater losses in the cell's p-region. For InP, the n+p configuration is found to have greater radiation resistance than the p+n cell. The increased loss in this latter cell is attributed to losses in the cell's emitter region. Temperature dependency results are interpreted using a theoretical relation for dVoc/dT, which predicts that increased Voc should result in decreased numerical values for dPm/dT. The predicted correlation is observed for GaAs but not for InP, a result which is attributed to variations in cell processing.

Weinberg, I.↗