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At least 721 records · Page 40

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NASA NTRS1978

A study of the effect of forcing function characteristics on human operator dynamics in manual control

The effect of the spectrum of the forcing function on the human pilot dynamics in manual control was investigated. A simple compensatory tracking experiment was conducted, where the controlled element was of a second-order dynamics and the forcing function was a random noise having a dominant frequency. The dominant frequency and the power of the forcing function were two variable parameters during the experiment. The results show that the human pilot describing functions are dependent not only on the dynamics of the controlled element, but also on the characteristics of the forcing function. This suggests that the human pilot behavior should be expressed by the transfer function taking into consideration his ability to sense and predict the forcing function.

Washizu, K.↗

NASA NTRS1989

The role of refractive interstellar scintillation in the low frequency variability of extragalactic radio sources

A statistical analysis is presented of the 408 MHz flux density time series of 39 extragalactic radio sources, to study the role of refractive interstellar scintillation in the decimeter wavelength variability of compact extragalactic radio sources. It is found that the product of the scintillation index and the variability timescale is systematically larger for sources at lower galactic latitudes. This variation is obscured by much larger random variations. Possible explanations of these results are discussed.

Spangler, S.↗

NASA NTRS1991

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 17: The relationship between seven variables and the use of US government technical reports by US aerospace engineers and scientists

A study was undertaken to investigate the relationship between the use of U.S. government technical reports by U.S. aerospace engineers and scientists and seven selected sociometric variables. Data were collected by means of a self-administered mail survey which was distributed to a randomly drawn sample of American Institute of Aeronautics and Astronautics (AIAA) members. Two research questions concerning the use of conference meeting papers, journal articles, in-house technical reports, and U.S. government technical reports were investigated. Relevance, technical quality, and accessibility were found to be more important determinants of the overall extent to which U.S. government technical reports and three other information products were used by U.S. aerospace engineers and scientists.

Pinelli, Thomas E.↗

NASA NTRS1998

Ocean Turbulence V: Mesoscale Modeling in Level Coordinates. The Effect of Random Nature of Density

The main result of this paper is the derivation of a new expression for the tracer subgrid term in level coordinates S(l) to be employed in O-GCM. The novel feature is the proper account of the random nature of the density field which strongly affects the transformation from isopycnal to level coordinates of the variables of interest, velocity and tracer fields, their correlation functions and ultimately the subgrid terms. In deriving our result we made use of measured properties of vertical ocean turbulence. The major new results are: 1) the new subgrid expression is different from that of the heuristic GM model, 2) u++(tracer)=1/2u+(thickness), where u++ and u+ are the tracer and thickness bolus velocities. In previous models, u++ = u+, 2) the subgrid for a tracer tau is not the same as that for the density rho even when one accounts for the obvious absence of a diffusion term in the latter. The difference stems from a new treatment of the stochastic nature of the density, 3) the mesoscale diffusivity enters both locally and non-locally, as the integral over all z's from the bottom of the ocean to the level z.

Canuto, V. M.↗

NASA NTRS2004

Alternative Approaches to Land Initialization for Seasonal Precipitation and Temperature Forecasts

The seasonal prediction system of the NASA Global Modeling and Assimilation Office is used to generate ensembles of summer forecasts utilizing realistic soil moisture initialization. To derive the realistic land states, we drive offline the system's land model with realistic meteorological forcing over the period 1979-1993 (in cooperation with the Global Land Data Assimilation System project at GSFC) and then extract the state variables' values on the chosen forecast start dates. A parallel series of forecast ensembles is performed with a random (though climatologically consistent) set of land initial conditions; by comparing the two sets of ensembles, we can isolate the impact of land initialization on forecast skill from that of the imposed SSTs. The base initialization experiment is supplemented with several forecast ensembles that use alternative initialization techniques. One ensemble addresses the impact of minimizing climate drift in the system through the scaling of the initial conditions, and another is designed to isolate the importance of the precipitation signal from that of all other signals in the antecedent offline forcing. A third ensemble includes a more realistic initialization of the atmosphere along with the land initialization. The impact of each variation on forecast skill is quantified.

Koster, Randal↗

NASA NTRS2002

A geostatistical analysis of small-scale spatial variability in bacterial abundance and community structure in salt marsh creek bank sediments

Small-scale variations in bacterial abundance and community structure were examined in salt marsh sediments from Virginia's eastern shore. Samples were collected at 5 cm intervals (horizontally) along a 50 cm elevation gradient, over a 215 cm horizontal transect. For each sample, bacterial abundance was determined using acridine orange direct counts and community structure was analyzed using randomly amplified polymorphic DNA fingerprinting of whole-community DNA extracts. A geostatistical analysis was used to determine the degree of spatial autocorrelation among the samples, for each variable and each direction (horizontal and vertical). The proportion of variance in bacterial abundance that could be accounted for by the spatial model was quite high (vertical: 60%, horizontal: 73%); significant autocorrelation was found among samples separated by 25 cm in the vertical direction and up to 115 cm horizontally. In contrast, most of the variability in community structure was not accounted for by simply considering the spatial separation of samples (vertical: 11%, horizontal: 22%), and must reflect variability from other parameters (e.g., variation at other spatial scales, experimental error, or environmental heterogeneity). Microbial community patch size based upon overall similarity in community structure varied between 17 cm (vertical) and 35 cm (horizontal). Overall, variability due to horizontal position (distance from the creek bank) was much smaller than that due to vertical position (elevation) for both community properties assayed. This suggests that processes more correlated with elevation (e.g., drainage and redox potential) vary at a smaller scale (therefore producing smaller patch sizes) than processes controlled by distance from the creek bank. c2002 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

Non-NASA Center↗

NASA NTRS1999

Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction. TRACE Investigators. TRAndolapril Cardiac Evaluation

A number of new methods have been recently developed to quantify complex heart rate (HR) dynamics based on nonlinear and fractal analysis, but their value in risk stratification has not been evaluated. This study was designed to determine whether selected new dynamic analysis methods of HR variability predict mortality in patients with depressed left ventricular (LV) function after acute myocardial infarction (AMI). Traditional time- and frequency-domain HR variability indexes along with short-term fractal-like correlation properties of RR intervals (exponent alpha) and power-law scaling (exponent beta) were studied in 159 patients with depressed LV function (ejection fraction <35%) after an AMI. By the end of 4-year follow-up, 72 patients (45%) had died and 87 (55%) were still alive. Short-term scaling exponent alpha (1.07 +/- 0.26 vs 0.90 +/- 0.26, p <0.001) and power-law slope beta (-1.35 +/- 0.23 vs -1.44 +/- 0.25, p <0.05) differed between survivors and those who died, but none of the traditional HR variability measures differed between these groups. Among all analyzed variables, reduced scaling exponent alpha (<0.85) was the best univariable predictor of mortality (relative risk 3.17, 95% confidence interval 1.96 to 5.15, p <0.0001), with positive and negative predictive accuracies of 65% and 86%, respectively. In the multivariable Cox proportional hazards analysis, mortality was independently predicted by the reduced exponent alpha (p <0.001) after adjustment for several clinical variables and LV function. A short-term fractal-like scaling exponent was the most powerful HR variability index in predicting mortality in patients with depressed LV function. Reduction in fractal correlation properties implies more random short-term HR dynamics in patients with increased risk of death after AMI.

Non-NASA Center↗

NASA NTRS2008

The Helium Abundance at Quiescent Current Sheets and the Slow Solar Wind

Ulysses MAG data were used to identify current sheets during sunspot minimum years of 1994-1997 and 2004-2006. The purpose of limiting the dates was to focus attention on 'quiescent current sheets' with as little influence from ICMEs as possible. SWOOPS data were then used in a superposed epoch analysis to study Helium abundance in the vicinity of the current sheet, similar to the study done by Borrini et al. (1981). That earlier study found a narrow (ca. 2 day) minimum in He/H around the current sheet that is extremely variable from one year to the next in the period 1971-1978. A similar result is found here for data at all latitudes and distances in 2004-2006. Conversely, data from 1994-1997 produce a deep minimum several times wider (ca. 10 days). The reason for this is found to be that low He/H is more closely associated with slow wind than the current sheet per se. There are thus apparently at least two sources of slow wind, one associated with very low He/H of 0-0.02 and one associated with moderate abundance of 0.03-0.05. The large variability is a consequence of the relatively small number of current sheet encounters around solar minimum and the random distribution of low He/H intervals, lasting less than 1 day to more than 7 days, throughout slow wind.

Suess, Steven T.↗

DOE OSTI2022

Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages

The significance of the impact of weather on the electric grid has grown as climate change continues to increase the frequency and intensity of extreme weather events. In recent years (2021-2022) in particular, extreme winter weather has affected the grid in locations in the US rarely exposed to extreme low temperatures, snow and icing conditions. Here we analyze the correlation between cold weather meteorological variables and electricity outages during two large winter storm events, Uri (February 2021) and Landon (February 2022) using Random Forest machine learning and Pearson’s correlation coefficient. Our geographical focus across the two storms is the state of Texas. Extrapolation of the method to winter weather impacts over other years and additional locations is proposed.

Dumas, Melissa↗

DOE OSTI2022

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗

NASA NTRS1991

Evolution of bioconvective patterns in variable gravity

Measurements are reported of the evolution of bioconvective patterns in shallow, dense cultures of microorganisms subjected to varying gravity. Various statistical properties of this random, quasi-two-dimensional structure have been found: Aboav's law is obeyed, the average vertex angles follow predictions for regular polygons, and the area of a pattern varies linearly with its number of sides. As gravity varies between 1 g and 1.8 g, these statistical properties continue to hold despite a tripling of the number of polygons and a reduced average polygon dimension by a third. This work compares with experiments on soap foams, Langmuir monolayer foams, metal grains, and simulations.

Noever, David A.↗

DOE OSTI2022

Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the uvgriz multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first-year observation of Mephisto W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with purity = 95.4% and completeness = 96.9% for the RR Lyrae RFC and purity = 91.4% and completeness = 90.2% for the quasar RFC. In conclusion, we have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.

(galaxies:) quasars: general↗

NASA NTRS2010

Forced and Unforced Variability of Twentieth Century North American Droughts and Pluvials

Research on the forcing of drought and pluvial events over North America is dominated by general circulation model experiments that often have operational limitations (e.g., computational expense, ability to simulate relevant processes, etc). We use a statistically based modeling approach to investigate sea surface temperature (SST) forcing of the twentieth century pluvial (1905-1917) and drought (1932-1939, 1948-1957, 1998-2002) events. A principal component (PC) analysis of Palmer Drought Severity Index (PDSI) from the North American Drought Atlas separates the drought variability into five leading modes accounting for 62% of the underlying variance. Over the full period spanning these events (1900-2005), the first three PCs significantly correlate with SSTs in the equatorial Pacific (PC 1), North Pacific (PC 2), and North Atlantic (PC 3), with spatial patterns (as defined by the empirical orthogonal functions) consistent with our understanding of North American drought responses to SST forcing. We use a large ensemble statistical modeling approach to determine how successfully we can reproduce these drought/pluvial events using these three modes of variability. Using Pacific forcing only (PCs 1-2), we are able to reproduce the 1948-1957 drought and 1905-1917 pluvial above a 95% random noise threshold in over 90% of the ensemble members; the addition of Atlantic forcing (PCs 1-2-3) provides only marginal improvement. For the 1998-2002 drought, Pacific forcing reproduces the drought above noise in over 65% of the ensemble members, with the addition of Atlantic forcing increasing the number passing to over 80%. The severity of the drought, however, is underestimated in the ensemble median, suggesting this drought intensity can only be achieved through internal variability or other processes. Pacific only forcing does a poor job of reproducing the 1932-1939 drought pattern in the ensemble median, and less than one third of ensemble members exceed the noise threshold (28%). Inclusion of Atlantic forcing improves the ensemble median drought pattern and nearly doubles the number of ensemble members passing the noise threshold (52%). Even with the inclusion of Atlantic forcing, the intensity of the simulated 1932-1939 drought is muted, and the drought itself extends too far into the southwest and southern Great Plains. To an even greater extent than the 1998-2002 drought, these results suggest much of the variance in the 1932-1939 drought is dependent on processes other than SST forcing. This study highlights the importance of internal noise and non SST processes for hydroclimatic variability over North America, complementing existing research using general circulation models.

Cook, Benjamin I.↗

DOE OSTI2022

Random strain induced correlations in materials with intertwined nematic and magnetic orders

Electronic nematicity is rarely observed as an isolated instability of a correlated electron system. Instead, in iron pnictides and in certain cuprates and heavy-fermion materials, nematicity is intertwined with an underlying spin-stripe or charge-stripe state. As a result, random strain, ubiquitous in any real crystal, creates both random-field disorder for the nematic degrees of freedom and random-bond disorder for the spin or charge ones. Here, we put forward an Ashkin-Teller model with random Baxter fields to capture the dual role of random strain in nematic systems for which nematicity is a composite order arising from a stripe state. Using Monte Carlo to simulate this random Baxter-field model, we find not only the expected break-up of the system into nematic domains, but also the emergence of nontrivial disorder-promoted magnetic correlations. Such correlations enhance and tie up the fluctuations associated with the two degenerate magnetic stripe states from which nematicity arises, leaving characteristic signatures in the spatial profile of the magnetic domains, in the configurational space of the spin variables, and in the magnetic noise spectrum. We discuss possible experimental manifestations of these effects in iron-pnictide superconductors. Furthermore, our work establishes the random Baxter-field model as a more complete alternative to the random-field Ising model to describe complex electronic nematic phenomena in the presence of disorder.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Probabilistic and maximum entropy modeling of chemical reaction systems: Characteristics and comparisons to mass action kinetic models

We demonstrate and characterize a first-principles approach to modeling the mass action dynamics of metabolism. Starting from a basic definition of entropy expressed as a multinomial probability density using Boltzmann probabilities with standard chemical potentials, we derive and compare the free energy dissipation and the entropy production rates. We express the relation between entropy production and the chemical master equation for modeling metabolism, which unifies chemical kinetics and chemical thermodynamics. Because prediction uncertainty with respect to parameter variability is frequently a concern with mass action models utilizing rate constants, we compare and contrast the maximum entropy model, which has its own set of rate parameters, to a population of standard mass action models in which the rate constants are randomly chosen. We show that a maximum entropy model is characterized by a high probability of free energy dissipation rate and likewise entropy production rate, relative to other models. We then characterize the variability of the maximum entropy model predictions with respect to uncertainties in parameters (standard free energies of formation) and with respect to ionic strengths typically found in a cell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2004

Multi-Agent Methods for the Configuration of Random Nanocomputers

As computational devices continue to shrink, the cost of manufacturing such devices is expected to grow exponentially. One alternative to the costly, detailed design and assembly of conventional computers is to place the nano-electronic components randomly on a chip. The price for such a trivial assembly process is that the resulting chip would not be programmable by conventional means. In this work, we show that such random nanocomputers can be adaptively programmed using multi-agent methods. This is accomplished through the optimization of an associated high dimensional error function. By representing each of the independent variables as a reinforcement learning agent, we are able to achieve convergence must faster than with other methods, including simulated annealing. Standard combinational logic circuits such as adders and multipliers are implemented in a straightforward manner. In addition, we show that the intrinsic flexibility of these adaptive methods allows the random computers to be reconfigured easily, making them reusable. Recovery from faults is also demonstrated.

Lawson, John W.↗

DOE OSTI2023

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE ($\sqrt{CAPE}$) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

RXTE Observations of the Seyfert 2 Galaxy MrK 348

We present RXTE monitoring observations of the Seyfert 2 galaxy Mrk 348 spanning a 6 month period. The time-averaged spectrum in the 3-20 keV band shows many features characteristic of a Compton-thin Seyfert 2 galaxy, namely a hard underlying power-law continuum (Gamma approximately equal 1.8) with heavy soft X-ray absorption (N(sub H) approximately 10(exp 23)/sq cm) plus measurable iron K.alpha emission (equivalent width approximately 100 eV) and, at high energy, evidence for a reflection component (R approximately < 1). During the first half of the monitoring period the X-ray continuum flux from Mrk 348 remained relatively steady. However this was followed by a significant brightening of the source (by roughly a factor of 4) with the fastest change corresponding to a doubling of its X-ray flux on a timescale of about 20 days. The flux increase was accompanied by a marked softening of X-ray spectrum most likely attributable to a factor approximately 3 decline in the intrinsic line-of-sight column density. In contrast the iron K.alpha line and the reflection components showed no evidence of variability. These observations suggest a scenario in which the central X-ray source is surrounded by a patchy distribution of absorbing material located within about a light-week of the nucleus of Mrk 348. The random movement of individual clouds within the absorbing screen, across our line of sight, produces substantial temporal variations in the measured column density on timescales of weeks to months and gives rise to the observed X-ray spectral variability. However, as viewed from the nucleus the global coverage and typical thickness of the cloud layer remains relatively constant.

Smith, David A.↗