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At least 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Variable Grid Method

NETL’s Variable Grid Method (VGM) is a novel approach that leverages GIS capabilities to simultaneously visualize and quantify spatial data trends and underlying data uncertainty. The flexible VGM approach utilizes a range of spatial datasets and uncertainty quantifications, which can be calculated using data related to sample density, sample variance, interpolation error, multiple simulations, etc., to create an integrated visualization of data and uncertainty. The intuitive manner of the VGM helps communicate the relationship between uncertainty and spatial data to effectively guide research, support advanced computation analyses, and helps inform research, management and policy decisions. The VGM approach is being developed into a standalone tool designed to integrate into a variety of research workflows, providing users the capabilities to utilize the VGM for their datasets, analysis, and models in support of their decision making needs. The VGM approach was developed as part of NETL’s Offshore portfolio. It was further matured and utilized in projects associated with both NETL’s Unconventional Resources and Carbon Storage portfolios, and is now being further developed by Varigrid, LLC.

Carbon Storage↗
DOE OSTI2016

SICK: THE SPECTROSCOPIC INFERENCE CRANK

There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal-to-noise ratio spectra of M67 stars reveals atomic diffusion processes on the order of 0.05 dex, previously only measurable with differential analysis techniques in high-resolution spectra. sick is easy to use, well-tested, and freely available online through GitHub under the MIT license.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Increasing Mosquito Abundance Under Global Warming

Mosquitoes are a key virus vector that poses significant health threats globally, affecting 700 million individuals and causing 1 million deaths annually. Accurately predicting mosquito abundance and dispersion remains a challenge. Complex interactions between mosquito dynamics and various environmental factors, notably hydrology, contribute to this challenge. Existing models typically focus on precipitation and temperature and often overlook further impacts of hydrological variables within mosquito modeling. In this study, we developed an artificial intelligence‐based model for mosquito dynamics, explicitly accounting for different hydrological variables, such as precipitation, soil moisture and streamflow. Using Toronto, Canada, as a case study, we identified causal relationships between changes in mosquito populations, hydrological factors, vegetation (e.g., leaf area index), and climate variables (e.g., daylight length, precipitation, and temperature). We embedded these relationships into a Long Short‐Term Memory (LSTM) Neural Network Model capable of accurately detecting mosquito dynamics across annual, seasonal, and monthly time scales. The LSTM is able to explain, on average, approximately 40% of the variance in the observed mosquito abundance data. Using the calibrated model, we predicted that the summer season mosquito abundance would increase by ∼16% and ∼19% under an intermediate greenhouse emission scenario, Shared Socioeconomic Pathway (SSP) 2–4.5, and a high greenhouse emission scenario, SSP5‐8.5, respectively. We expect that this model can serve as a valuable tool and inform science‐based decisions affecting mosquito dynamics and public health. It can also build a foundation for future risk analysis at the regional and larger scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Analysis of Application Power and Schedule Composition in a High Performance Computing Environment

As the capacity of high performance computing (HPC) systems continues to grow, small changes in energy management have the potential to produce significant energy savings. In this paper, we employ an extensive informatics system for aggregating and analyzing real-time performance and power use data to evaluate energy footprints of jobs running in an HPC data center. We look at the effects of algorithmic choices for a given job on the resulting energy footprints, and analyze application-specific power consumption, and summarize average power use in the aggregate. All of these views reveal meaningful power variance between classes of applications as well as chosen methods for a given job. Using these data, we discuss energy-aware cost-saving strategies based on reordering the HPC job schedule. Using historical job and power data, we present a hypothetical job schedule reordering that: (1) reduces the facility's peak power draw and (2) manages power in conjunction with a large-scale photovoltaic array. Lastly, we leverage this data to understand the practical limits on predicting key power use metrics at the time of submission.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Genome-Wide Association Study for Identifying Loci that Affect Fillet Yield, Carcass, and Body Weight Traits in Rainbow Trout (Oncorhynchus mykiss)

Fillet yield (FY, %) is an economically-important trait in rainbow trout aquaculture that affects production efficiency. Despite that, FY has received little attention in breeding programs because it is difficult to measure on a large number of fish and cannot be directly measured on breeding candidates. The recent development of a high-density SNP array for rainbow trout has provided the needed tool for studying the underlying genetic architecture of this trait. A genome-wide association study (GWAS) was conducted for FY, body weight at 10 (BW10) and 13 (BW13) months post-hatching, head-off carcass weight (CAR), and fillet weight (FW) in a pedigreed rainbow trout population selectively bred for improved growth performance. The GWAS analysis was performed using the weighted single-step GBLUP method (wssGWAS). Phenotypic records of 1447 fish (1.5 kg at harvest) from 299 full-sib families in three successive generations, of which 875 fish from 196 full-sib families were genotyped, were used in the GWAS analysis. A total of 38,107 polymorphic SNPs were analyzed in a univariate model with hatch year and harvest group as fixed effects, harvest weight as a continuous covariate, and animal and common environment as random effects. A new linkage map was developed to create windows of 20 adjacent SNPs for use in the GWAS. The two windows with largest effect for FY and FW were located on chromosome Omy9 and explained only 1.0–1.5% of genetic variance, thus suggesting a polygenic architecture affected by multiple loci with small effects in this population. One window on Omy5 explained 1.4 and 1.0% of the genetic variance for BW10 and BW13, respectively. Three windows located on Omy27, Omy17, and Omy9 (same window detected for FY) explained 1.7, 1.7, and 1.0%, respectively, of genetic variance for CAR. Among the detected 100 SNPs, 55% were located directly in genes (intron and exons). Nucleotide sequences of intragenic SNPs were blasted to the Mus musculus genome to create a putative gene network. The network suggests that differences in the ability to maintain a proliferative and renewable population of myogenic precursor cells may affect variation in growth and fillet yield in rainbow trout.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Interpretation of the polarization of Venus

The linear polarization of sunlight reflected by Venus is analyzed by comparing observations with extensive multiple scattering computations. The analysis establishes that Venus is veiled by a cloud or haze layer of spherical particles. The refractive index of the particles is about 1.44 at 0.55 microns with a normal dispersion, the refractive index decreasing from about 1.46 at 0.365 microns to 1.43 at 0.99 microns. The cloud particles have a narrow size distribution with a mean radius of about 1 micron; specifically, the effective radius of the size distribution is approximately 1.05 microns and the effective variance is about 0.07. The particles exist at a high level in the atmosphere, with the optical thickness unity occurring where the pressure is about 50 mb. The particle properties deduced from the polarization eliminate all but one of the cloud compositions which have been proposed for Venus. A concentrated solution of sulfuric acid provides good agreement with the polarization data.

Hansen, J. E.↗
NASA NTRS1981

A statistical data analysis and plotting program for cloud microphysics experiments

The analysis software developed for atmospheric cloud microphysics experiments conducted in the laboratory as well as aboard a KC-135 aircraft is described. A group of four programs was developed and implemented on a Hewlett Packard 1000 series F minicomputer running under HP's RTE-IVB operating system. The programs control and read data from a MEMODYNE Model 3765-8BV cassette recorder, format the data on the Hewlett Packard disk subsystem, and generate statistical data (mean, variance, standard deviation) and voltage and engineering unit plots on a user selected plotting device. The programs are written in HP FORTRAN IV and HP ASSEMBLY Language with the graphics software using the HP 1000 Graphics. The supported plotting devices are the HP 2647A graphics terminal, the HP 9872B four color pen plotter, and the HP 2608A matrix line printer.

Jordan, A. J.↗
NASA NTRS1985

Venus topography - A harmonic analysis

A model of Venusian global topography has been obtained by fitting an eighteenth-degree harmonic series to Pioneer Venus orbiter radar altimeter data. The mean radius is (6051.45 + or - 0.04) km. The corresponding mean density is (5244.8 + or 0.5) kg/cu m. The center of figure is displaced from the center of mass by (0.339 + or - 0.088) km towards (6.6 + or 10.1) deg N, (148. 8 + or - 7.7) deg. The figure of Venus is distinctly triaxial, but the orientation and magnitudes of the principal topographic axes correlate rather poorly with the gravitational principal axes. However, the higher-degree harmonics of topography and gravity are significantly correlated. The topographic variance spectrum of Venus is very similar in form to those of the moon, Mars, and especially earth. It is suggested that this spectral similarity simply reflects a statistical balance between constructional and degradational geomorphic proceses. Venus and earth are particularly similar (and differ from the moon and Mars) in that the larger bodies both exhibit a significant low degree deficit (relative to the extrapolated trend of the higher harmonics).

Bills, B. G.↗
NASA NTRS1986

Reduced conservatism in stability robustness bounds by state transformation

This note addresses the issue of 'conservatism' in the time domain stability robustness bounds obtained by the Liapunov approach. A state transformation is employed to improve the upper bounds on the linear time-varying perturbation of an asymptotically stable linear time-invariant system for robust stability. This improvement is due to the variance of the conservatism of the Liapunov approach with respect to the basis of the vector space in which the Liapunov function is constructed. Improved bounds are obtained, using a transformation, on elemental and vector norms of perturbations (i.e., structured perturbations) as well as on a matrix norm of perturbations (i.e., unstructured perturbations). For the case of a diagonal transformation, an algorithm is proposed to find the 'optimal' transformation. Several examples are presented to illustrate the proposed analysis.

Yedavalli, R. K.↗
NASA NTRS1983

Measurements of mantle wave velocities and inversion for lateral heterogeneity and anisotropy. I - Analysis of great circle phase velocities

The global lateral heterogeneity of the upper mantle is investigated using the classical Fourier-transform method of Sato(1958) and IDA/GDSN data from 25 1980 earthquakes. The great-circle phase velocities of 200 Love and 250 Rayleigh 100-330-sec-period fundamental-mode wave paths are determined and interpreted in terms of regional phase-velocity variation, using additional data on surface tectonics to extrapolate odd-harmonic information from the even-harmonic data. The results are presented in extensive tables, maps, and graphs. Regionalized inversion using the seven-region model of Okal (1977) is found to give maximum variance reductions of 65 percent for Love waves and 85 percent for Rayleigh waves, compared to 60 and 90 percent for l(max) = 2 inversion. Significant interregion differences are found in the regionalized Love-wave phase velocities.

Nakanishi, I.↗
NASA NTRS2003

Comparison of mode estimation methods and application in molecular clock analysis

BACKGROUND: Distributions of time estimates in molecular clock studies are sometimes skewed or contain outliers. In those cases, the mode is a better estimator of the overall time of divergence than the mean or median. However, different methods are available for estimating the mode. We compared these methods in simulations to determine their strengths and weaknesses and further assessed their performance when applied to real data sets from a molecular clock study. RESULTS: We found that the half-range mode and robust parametric mode methods have a lower bias than other mode methods under a diversity of conditions. However, the half-range mode suffers from a relatively high variance and the robust parametric mode is more susceptible to bias by outliers. We determined that bootstrapping reduces the variance of both mode estimators. Application of the different methods to real data sets yielded results that were concordant with the simulations. CONCLUSION: Because the half-range mode is a simple and fast method, and produced less bias overall in our simulations, we recommend the bootstrapped version of it as a general-purpose mode estimator and suggest a bootstrap method for obtaining the standard error and 95% confidence interval of the mode.

Evolution, Molecular↗
DOE OSTI2014

The use of strain tensor to estimate thoracic tumors deformation

Purpose: Respiration-induced kinematics of thoracic tumors suggests a simple analogy with elasticity, where a strain tensor is used to characterize the volume of interests. The application of the biomechanical framework allows for the objective determination of tumor characteristics. Methods: Four-dimensional computed tomography provides the snapshots of the patient's anatomy at the end of inspiration and expiration. Image registration was used to obtain the displacement vector fields and deformation fields, which allows one for the determination of the strain tensor. Its departure from the identity matrix gauges the departure of the medium from rigidity. The tensorial characteristic of each GTV voxel was determined and averaged. To this end, the standard Euclidean matrix norm as well as the Log-Euclidean norm were employed. Tensorial anisotropy was gauged with the fractional anisotropy measure which is based on the normalized variance of the tensors eigenvalues. Anisotropy was also evaluated with the geodesic distance in the Log-Euclidean framework of a given strain tensor to its closest isotropic counterpart. Results: The averaged strain tensor was determined for each of the 15 retrospectively analyzed thoracic GTVs. The amplitude of GTV motion varied from 0.64 to 4.21 with the average of 1.20 cm. The GTV size ranged from 5.16 to 149.99 cc with the average of 43.19 cc. The tensorial analysis shows that deformation is inconsiderable and that the tensorial anisotropy is small. The Log-Euclidean distance of averaged strain tensors from the identity matrix ranged from 0.06 to 0.31 with the average of 0.19. The Frobenius distance from the identity matrix is similar and ranged from 0.06 to 0.35 with the average of 0.21. Their fractional anisotropy ranged from 0.02 to 0.12 with the average of 0.07. Their geodesic anisotropy ranged from 0.03 to 0.16 with the average of 0.09. These values also indicate insignificant deformation. Conclusions: The tensorial framework allows for direct measurements of tissue deformation. It goes beyond the evaluation of deformation via comparison of shapes. It is an independent and objective determination of tissue properties. This methodology can be used to determine possible changes in lung properties due to radiation therapy and possible toxicities.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1976

Linear minimum variance filters applied to carrier tracking

A new approach is taken to the problem of tracking a fixed amplitude signal with a Brownian-motion phase process. Classically, a first-order phase-lock loop (PLL) is used; here, the problem is treated via estimation of the quadrature signal components. In this space, the state dynamics are linear with white multiplicative noise. Therefore, linear minimum-variance filters, which have a particularly simple mechanization, are suggested. The resulting error dynamics are linear at any signal/noise ratio, unlike the classical PLL. During synchronization, and above threshold, this filter with constant gains degrades by 3 per cent in output rms phase error with respect to the classical loop. However, up to 80 per cent of the maximum possible noise improvement is obtained below threshold, where the classical loop is nonoptimum, as demonstrated by a Monte Carlo analysis. Filter mechanizations are presented for both carrier and baseband operation.

Gustafson, D. E.↗
NASA NTRS1982

Can crop types be resolved using mixture distribution components - Some initial results and implications

For the analysis of remotely sensed data, it is frequently necessary to design a classifier in order to locate a ground cover class of interest or to estimate the proportion of this ground cover class. Advantages of a mixture distribution formulation are discussed, and a description is presented of the results of estimating the proportion of small grains in ten Landsat data segments using the mixture model. It is found that the mixture model proportion estimates have a very low variance and coefficient of variation. The discussed investigation implies that the mixtures model is a viable method for determining the distributions of classes of interest in remote sensing problems and in estimating the proportions of these classes directly.

Lennington, R. K.↗
NASA NTRS1991

Optical phase-locked loop (OPLL) for free-space laser communications with heterodyne detection

Several advantages of coherent free-space optical communications are outlined. Theoretical analysis is formulated for an OPLL disturbed by shot noise, modulation noise, and frequency noise consisting of a white component, a 1/f component, and a 1/f-squared component. Each of the noise components is characterized by its associated power spectral density. It is shown that the effect of modulation depends only on the ratio of loop bandwidth and data rate, and is negligible for an OPLL with loop bandwidth smaller than one fourth the data rate. Total phase error variance as a function of loop bandwidth is displayed for several values of carrier signal to noise ratio. Optimal loop bandwidth is also calculated as a function of carrier signal to noise ratio. An OPLL experiment is performed, where it is shown that the measured phase error variance closely matches the theoretical predictions.

Win, Moe Z.↗
NASA NTRS1994

Redshift data and statistical inference

Frequency histograms and the 'power spectrum analysis' (PSA) method, the latter developed by Yu & Peebles (1969), have been widely employed as techniques for establishing the existence of periodicities. We provide a formal analysis of these two classes of methods, including controlled numerical experiments, to better understand their proper use and application. In particular, we note that typical published applications of frequency histograms commonly employ far greater numbers of class intervals or bins than is advisable by statistical theory sometimes giving rise to the appearance of spurious patterns. The PSA method generates a sequence of random numbers from observational data which, it is claimed, is exponentially distributed with unit mean and variance, essentially independent of the distribution of the original data. We show that the derived random processes is nonstationary and produces a small but systematic bias in the usual estimate of the mean and variance. Although the derived variable may be reasonably described by an exponential distribution, the tail of the distribution is far removed from that of an exponential, thereby rendering statistical inference and confidence testing based on the tail of the distribution completely unreliable. Finally, we examine a number of astronomical examples wherein these methods have been used giving rise to widespread acceptance of statistically unconfirmed conclusions.

Newman, William I.↗