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Derivation of statistically significant size distributions from impactor samples of rarefield stratospheric aerosols imposes difficult sampling constraints on collector design. It is shown that it is necessary to design impactors of different size for each range of aerosol size collected so as to obtain acceptable levels of uncertainty with a reasonable amount of data reduction.
Purpose: We extended the capabilities of a low dose simulation tool to model Tube-Current Modulation (TCM). TCM is widely used in clinical practice to reduce radiation dose in CT scans. We expect the tool to be valuable for various clinical applications (e.g., optimize protocols, compare reconstruction techniques and evaluate TCM methods). Methods: The tube current is input as a function of z location, instead of a fixed value. Starting from the line integrals of a scan, a new Poisson noise realization at a lower dose is generated for each view. To validate the new functionality, we compared simulated scans with real scans in image space. Results: First we assessed noise in the difference between the low-dose simulations and the original high-dose scan. When the simulated tube current is a step function of z location, the noise at each segment matches the noise of 3 separate constant-tube-current-simulations. Secondly, with a phantom that forces TCM, we compared a low-dose simulation with an equivalent real low-dose scan. The mean CT number of the simulated scan and the real low-dose scan were 137.7±0.6 and 137.8±0.5 respectively. Furthermore, with 240 ROIs, the noise of the simulated scan and the real low-dose scan were 24.03±0.45 and 23.99±0.43 respectively, and they were not statistically different (2-sample t-test, p-value=0.28). The facts that the noise reflected the trend of the TCM curve, and that the absolute noise measurements were not statistically different validated the TCM function. Conclusion: We successfully added tube-current modulation functionality in an existing low dose simulation tool. We demonstrated that the noise reflected an input tube-current modulation curve. In addition, we verified that the noise and mean CT number of our simulation agreed with a real low dose scan. The authors are all employees of Philips. Yijun Ding is also supported by NIBIB P41EB002035 and NIBIB R01EB000803.
SSGUI is a web-based application that integrates the integrative genomics browser (IGV) with upstream alignment pipelines enabling rapid analysis of a batch of next-generation sequencing (NGS) samples. The input to SSGUI is an NGS file system directory that is organized by experiment and reference sequence. The output is an online dashboard containing various sequencing statistics for each sample and an integrated IGV plugin enabling rapid analysis of aligned NGS reads.
Methods are derived for applying the V/Vmax variable when several complete samples are analyzed simultaneously. A new, related, variable, V(e)/V(a), which is superior to V/Vmax when sufficient observational data are available, is introduced. It is shown how statistically independent complete samples can be generated from partially overlapping samples such that each observed object appears only once in the analysis. These methods make it possible to combine the information from different complete samples, and to make an efficient use of all available data. Illustrative examples for quasars are given. It is shown how the generalized V/Vmax and V(e)/V(a) can be used to find the amount of cosmological evolution of quasars and its associated range of uncertainty.
Here, we present a data-driven nonlinear uncertainty quantification and propagation framework to study the microstructure-induced stochastic performance of unidirectional (UD) carbon fiber reinforced polymer (CFRP) composites. The proposed approach integrates (1) microscopic image characterization, (2) stochastic microstructure reconstruction, and (3) efficient multiscale finite element simulations enabled by self-consistent clustering (SCA) analysis. To model the complex microstructural variability, the proposed UQ methods take the non-Gaussian uncertainty sources into account through a distribution-free sampling approach leveraging nonparametric and asymptotic statistical tools. A hierarchical conditional sampling strategy enables the simultaneous sampling of multiple sources of uncertainties. Our approach provides insights into the impact of microstructural variabilities, which are shown to have an increasing impact on the nonlinear responses of UD CFRP parts under progressive compression loading and ultimately on the failure rate over time. We discover that before CFRP parts start to fail, a characteristic time period emerges with distinctive uncertainty distributions specific to the microstructure variability and the probability of failure. Identifying the failure time period is crucial to the reliability prediction, which is an essential component of CFRP design.
The effects of a 5.8-y exposure to low-earth-orbit environment upon the mechanical properties of five commercial glasses and a low-expansion-coefficient glass-ceramic have been examined. The radiation components of the earth-orbit environment did not degrade the mechanical strength of the samples examined within the limits of experimental error. Statistical problems arising from the low frequency of micrometeorite or space debris impacts upon the samples precluded statistically valid measurement of impacted sample strengths. Upper bounds for the magnitude of the impact event damage upon the strengths for impacted samples have been determined.
Time-domain and frequency domain statistics for random step-wise sampled data systems
Pointed IRAS observations and ground based observations are used to determine the infrared properties of optically selected galaxies and quasars. The use of complete, unbiased, optically selected samples means that statistical tests can be applied to probe the underlying properties of active galactic nuclei (AGNs). The near infrared to millimeter spectral energy distributions (SEDs) were studied of the CfA Seyfert galaxies, a well defined, unbiased sample of 25 Type 1 and 23 Type 2 Seyfert galaxies selected by optical spectroscopy. Data given show strong trends in the infrared SEDs. Strong evidence is also given that the infrared spectra of Seyfert 2 galaxies are dominated by thermal emission from warm dust, while nonthermal emission is more important in the spectra of quasars and luminous Seyfert 1 nuclei.
Biochemical reaction networks are often modelled using discrete-state, continuous-time Markov chains. System statistics of these Markov chains usually cannot be calculated analytically and therefore estimates must be generated via simulation techniques. There is a well documented class of simulation techniques known as exact stochastic simulation algorithms, an example of which is Gillespie’s direct method. These algorithms often come with high computational costs, therefore approximate stochastic simulation algorithms such as the tau-leap method are used. However, in order to minimise the bias in the estimates generated using them, a relatively small value of tau is needed, rendering the computational costs comparable to Gillespie’s direct method. The multi-level Monte Carlo method (Anderson and Higham, Multiscale Model. Simul. 10:146–179, 2012) provides a reduction in computational costs whilst minimising or even eliminating the bias in the estimates of system statistics. This is achieved by first crudely approximating required statistics with many sample paths of low accuracy. Then correction terms are added until a required level of accuracy is reached. Recent literature has primarily focussed on implementing the multi-level method efficiently to estimate a single system statistic. However, it is clearly also of interest to be able to approximate entire probability distributions of species counts. We present two novel methods that combine known techniques for distribution reconstruction with the multi-level method. We demonstrate the potential of our methods using a number of examples.
Properties closures delineate the theoretical objective space for materials design problems, allowing designers to make informed trade-offs between competing constraints and target properties. In this paper, we present a new algorithm called hierarchical simplex sampling (HSS) that approximates properties closures more efficiently and faithfully than traditional optimization based approaches. By construction, HSS generates samples of microstructure statistics that span the corresponding microstructure hull. As a result, we also find that HSS can be coupled with synthetic polycrystal generation software to generate diverse sets of microstructures for subsequent mesoscale simulations. Finally, by more broadly sampling the space of possible microstructures, it is anticipated that such diverse microstructure sets will expand our understanding of the influence of microstructure on macroscale effective properties and inform the construction of higher-fidelity mesoscale structure-property models.
Existing models and simulants of tank disposition media at the Savannah River Site (SRS) have presumed the presence of high concentrations of inorganic mercury. However, quarterly Tank 50 analyses have shown that mercury is present as organomercurial species at concentrations that may present challenges to its remediation and disposition and may challenge the Waste Acceptance Criteria (WAC) for Saltstone. Todate, methylmercury analyses for Savannah River Remediation (SRR) have been performed at an off-site laboratory by Eurofins Frontier Global Sciences (FGS) in Bothell, WA. Savannah River Remediation requested the development of methylmercury and ethylmercury determination capabilities at the Savannah River National Laboratory (SRNL) to support the Liquid Waste Operations at SRS. As part of that method development, SRR requested that SRNL Analytical Development (AD) compare their results with those obtained from their outside contract laboratory, Eurofins FGS. This document reports on this method development work as well as the comparative analyses conducted between the two laboratories. A series of optimization and validation experiments has been performed at SRNL, which has resulted in the development of on-site methylmercury and ethylmercury speciation capabilities using purge and trap (P&T) gas chromatography (GC) coupled with thermal desorption (TD) cold vapor – atomic fluorescence spectroscopy (CVAFS). Speciation has been achieved for both species, with a methylmercury method reporting limit (MRL) value of 1.13 pg and an overall uncertainty (1σ) of 5.1%. An ethylmercury MRL of 11.5 pg and an overall uncertainty (1σ) of 4.8% were obtained. These analytical parameters compare favorably with Eurofins FGS, which demonstrates uncertainties (1σ) for methylmercury and ethylmercury of 13% and 9.1%, respectively. Results obtained by SRNL from the routine analysis of Tanks 21, 40, 50, and 51 samples have provided statistically indistinguishable results compared with Eurofins FGS. Based upon the work performed by SRNL-AD and the data currently available, the following recommendations can be made: Further work should be performed to determine a “best practices” for sample holding time prior to analysis (both preserved and non-preserved samples), as well as any detrimental effects sample age may have on data quality. Analysis of ethylmercury is only recommended for tank samples that are reasonably expected to have significant quantities of ethylmercury, such as from the DWPF SMECT. Trace levels of ethylmercury in the presence of large quantities of methylmercury can be difficult or impossible to quantify. SRNL-AD development of methods for total mercury, purgeable mercury (which includes Hg(0) and dimethylmercury), inorganic mercury (Hg(I) and Hg(II)), dissolved mercury, and particulate mercury are underway and will be reported upon separately.
Fully three-dimensional (3D) microstructure-explicit and void-explicit mesoscale simulations of the shock-to-detonation (SDT) process of pressed granular HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine) are performed. The overall size scale of the models is up to 3 × 3 × 15 mm3, with ∼30 000 grains and 206 265 voids. The models account for the heterogeneous material microstructure, constituent distribution, constituent morphology, and voids. Loading conditions considered involve piston velocities in the range of 600–1200 m/s or pressures in the range of 4–8 GPa. The focus is on analyzing the SDT process and the effects of microstructure and voids on the run-to-detonation distance (RDD). Companion two-dimensional (2D) simulations are also carried out to assess the differences between 2D and 3D. Statistically equivalent microstructure sample sets (SEMSSs) are generated and used for both 2D and 3D, allowing the prediction of the statistical and probabilistic Pop plots (PPs). The predictions are in general agreement with trends in available experimental data in the literature. It is found that both the microstructure (heterogeneous grain size, morphology, and size distribution) and voids significantly affect the RDD and the PPs. These effects are systematically delineated and quantified via the use of SEMSSs with different combinations of attributes. A recently developed probabilistic formulation for the PPs is used to characterize the results, allowing uncertainties in the relations between the shock pressure and RDD arising from material heterogeneities to be quantified. The probabilistic formulation is further used to quantify the confidence levels in the ranked order of influences of different combinations of microstructure and voids on the PPs.
Compilation describes characteristics of truncated and censored samples, and presents six illustrations of practical use of tables in computing mean and variance estimates for normal distribution using selected samples.
Radioastronomy, optical and X-ray data were used to probe the cause of high X-ray luminosities of 28 radio-quiet elliptical galaxies (RQE) and S0 galaxies previously scanned by the Einstein Observatory. Comparisons were made with similar data on double-lobed 3CR galaxies. Radio luminosities were highly correlated with the X-ray luminosities, agreeing with models of radio nuclear sources in early-type galaxies as accreting compact objects. Additionally, 3CR galaxies seemed to be large-scale versions of normal RQE. The significance of interstellar medium/intracluster medium interactions for high correlations between the core and total radio power from X-ray emitting galaxies is discussed.
NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA's primary physics goals are the precise measurements of neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ , determine the neutrino mass ordering, and constrain the value of $\delta_{CP}$, via the study of muon neutrino to electron neutrino oscillation. In the standard NOvA three-flavor analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data constrain the FD prediction through a ratio method. While this allows for systematic uncertainties sharing the same effects in both detectors to cancel, it remains an FD-only fit and does not fully leverage the constraining power of the high-statistics ND. This analysis proposes a simultaneous ND+FD fit using the PISCES method. PISCES (Parameter Inference with Systematic Covariance and Exact Statistics) is a framework designed to support complex configurations such as a joint ND+FD fit. This allows PISCES to take full advantage of the ND data to directly constrain systematic uncertainties across all samples. In PISCES, systematic uncertainties are encoded in a fractional covariance matrix, and statistical uncertainties are handled with a Poisson likelihood, making the approach well suited for low-statistics samples. For interpretability, we further use a Newton–Raphson + PCA method to recover per-systematic pulls from the covariance formulation. This poster presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, describes its implementation and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method.
This Technical Publication (TP) is part 2 of a two-part study of the North Atlantic basin tropical cyclones that occurred during the weather satellite era, 1960-2013. In particular, this TP examines the inferred statistical relationships between 25 tropical cyclone parameters and 9 specific climate-related factors, including the (1) Oceanic Niño Index (ONI), (2) Southern Oscillation Index (SOI), (3) Atlantic Multidecadal Oscillation (AMO) index, (4) Quasi-Biennial Oscillation (QBO) index, (5) North Atlantic Oscillation (NAO) index of the Climate Prediction Center (CPC), (6) NAO index of the Climate Research Unit (CRU), (7) Armagh surface air temperature (ASAT), (8) Global Land-Ocean Temperature Index (GLOTI), and (9) Mauna Loa carbon dioxide (CO2) (MLCO2) index. Part 1 of this two-part study examined the statistical aspects of the 25 tropical cyclone parameters (e.g., frequencies, peak wind speed (PWS), accumulated cyclone energy (ACE), etc.) and provided the results of statistical testing (i.e., runs-testing, the t-statistic for independent samples, and Poisson distributions). Also, the study gave predictions for the frequencies of the number of tropical cyclones (NTC), number of hurricanes (NH), number of major hurricanes (NMH), and number of United States land-falling hurricanes (NUSLFH) expected for the 2014 season, based on the statistics of the overall interval 1960-2013, the subinterval 1995-2013, and whether the year 2014 would be either an El Niño year (ENY) or a non-El Niño year (NENY).
Commercially available software packages today allow users to quickly perform the routine evaluations of (1) descriptive statistics to numerically and graphically summarize both sample and population data, (2) inferential statistics that draws conclusions about a given population from samples taken of it, (3) probability determinations that can be used to generate estimates of reliability allowables, and finally (4) the setup of designed experiments and analysis of their data to identify significant material and process characteristics for application in both product manufacturing and performance enhancement. This paper presents examples of analysis and experimental design work that has been conducted using Statgraphics®(Registered Trademark) statistical software to obtain useful information with regard to solid rocket motor propellants and internal insulation material. Data were obtained from a number of programs (Shuttle, Constellation, and Space Launch System) and sources that include solid propellant burn rate strands, tensile specimens, sub‐scale test motors, full‐scale operational motors, rubber insulation specimens, and sub‐scale rubber insulation analog samples. Besides facilitating the experimental design process to yield meaningful results, statistical software has demonstrated its ability to quickly perform complex data analyses and yield significant findings that might otherwise have gone unnoticed. One caveat to these successes is that useful results not only derive from the inherent power of the software package, but also from the skill and understanding of the data analyst.
Estimates from TRMM satellite data of monthly total rainfall over an area are subject to substantial sampling errors due to the limited number of visits to the area by the satellite during the month. Quantitative comparisons of TRMM averages with data collected by other satellites and by ground-based systems require some estimate of the size of this sampling error. A method of estimating this sampling error based on the actual statistics of the TRMM observations and on some modeling work has been developed. "Sampling error" in TRMM monthly averages is defined here relative to the monthly total a hypothetical satellite permanently stationed above the area would have reported. "Sampling error" therefore includes contributions from the random and systematic errors introduced by the satellite remote sensing system. As part of our long-term goal of providing error estimates for each grid point accessible to the TRMM instruments, sampling error estimates for TRMM based on rain retrievals from TRMM microwave (TMI) data are compared for different times of the year and different oceanic areas (to minimize changes in the statistics due to algorithmic differences over land and ocean). Changes in sampling error estimates due to changes in rain statistics due 1) to evolution of the official algorithms used to process the data, and 2) differences from other remote sensing systems such as the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I), are analyzed.