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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Consistent and reproducible computation of the glass transition temperature from molecular dynamics simulations

In many fields, from semiconductors for opto-electronic applications to ionic liquids (ILs) for separations, the glass transition temperature (Tg) of a material is a useful gauge for its potential use in practical settings. As a result, there is a great deal of interest in predicting Tg using molecular simulations. However, the uncertainty and variation in the trend shift method, a common approach in simulations to predict Tg, can be high. This is due to the need for human intervention in defining a fitting range for linear fits of density with temperature assumed for the liquid and glass phases across the simulated cooling. The definition of such fitting ranges then defines the estimate for the Tg as the intersection of linear fits. We eliminate this need for human intervention by leveraging the Shapiro–Wilk normality test and proposing an algorithm to define the fitting ranges and, consequently, Tg. Through this integration, we incorporate into our automated methodology that residuals must be normally distributed around zero for any fit, a requirement that must be met for any regression problem. Consequently, fitting ranges for realizing linear fits for each phase are statistically defined rather than visually inferred, obtaining an estimate for Tg without any human intervention. The method is also capable of finding multiple linear regimes across density vs temperature curves. We compare the predictions of our proposed method across multiple IL and semiconductor molecular dynamics simulation results from the literature and compare other proposed methods for automatically detecting Tg from density–temperature data. We believe that our proposed method would allow for more consistent predictions of Tg. We make this methodology available and open source through GitHub.

Chemistry↗
DOE OSTI2020

A neural network for determination of latent dimensionality in Nonnegative Matrix Factorization

Non-negative Matrix Factorization (NMF) has proven to be a powerful unsupervised learning method for uncovering hidden features in complex and noisy datasets with applications in data mining, text recognition, dimension reduction, face recognition, anomaly detection, blind source separation, and many other fields. An important input for NMF is the latent dimensionality of the data, that is, the number of hidden features, K, present in the explored dataset. Unfortunately, and this quantity is rarely known a priori. The existing methods for determining latent dimensionality, such as Automatic Relevance Determination (ARD), are mostly heuristic and utilize different characteristics to estimate the number of hidden features. However, all of them require human presence to make a final determination of K. Here we utilize a supervised machine learning approach in combination with a recent method for model determination, called NMFk, to determine the number of hidden features automatically. NMFk performs a set of NMF simulations on an ensemble of matrices, obtained by bootstrapping the initial dataset, and estimates which K produces stable groups of latent features that reconstruct the initial dataset well. We then train a Multi-Layter Perceptron (MLP) classifier network to determine the correct number of latent features utilizing the statistics and characteristics of the NMF solution, obtained from NMFk. In order to train the MLP classifier, a training set of 58,660 matrices with predetermined latent features were factorized with NMFk. The MLP classifier in conjunction with NMFk maintains a greater than 95% success rate when applied to a held out test set. Additionally, when applied to two well-known benchmark datasets, the swimmer and MIT face data, NMFk/MLP correctly recovers the established number of hidden features. Finally, we compare the accuracy of our method to the ARD, AIC and Stability-based methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Ensemble Simulation Techniques and Fast Randomized Algorithms

The major goals of the project were to develop and analyze new ensemble simulation techniques, including trajectory stratification and preconditioned MCMC techniques, as well as develop fast numerical linear algebra techniques closely related to ensemble simulation ideas. The trajectory stratification techniques involve simulating in parallel short trajectory fragments of a Markov process confined to a specific region of space‐time and then patching together the statistics gathered to assemble estimates of very general dynamical properties. We have also developed this approach for rare event simulation and extended the techniques to applications requiring a more general framework (such as electronic structure calculations). The preconditioned MCMC techniques involve simulating multiple Markov chains in parallel and then using information from the ensemble to speed the mixing of each individual chain. The fast randomized linear algebra methods are motivated by the diffusion Monte Carlo technique, but are applicable to finding the dominant eigenvalue of (almost) general matrices. For most non‐negative matrices, the schemes result in an error (compared to the power method) that is constant in the dimension of the problem. For more general matrices, we see a very clear sublinear cost trend in computational tests.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Where do the fish go in winter? A year of observing seasonal changes in Sequim Bay’s nearshore fish community

The use of environmental DNA (eDNA) sampling has been proposed as a complementary method to monitor fish species in marine environments. eDNA offers a non-invasive, cost-effective, and scalable way to detect aquatic species. It is appealing in environments where traditional methods are limited by access or visibility, especially in complex or sensitive habitats such as tidal channels and other marine energy sites. Before eDNA can be fully relied upon, we must verify its accuracy against established methods, like underwater photography. In this study, we collected eDNA samples and concurrently deployed a 360-degree camera near the floating dock of PNNL-Sequim in the tidal channel of Sequim Bay once a month for twelve consecutive months. During deployments, the camera remained on the seafloor for several hours overlapping slack tide and captured time-lapse photographs at ten second intervals. Counts and identifications of fish species observed in the images were used to calculate monthly Shannon diversity and Pielou evenness indices. These values were compared across the months using a Kruskal-Wallis test paired with a Conover-Iman post-hoc test. Cliff’s Delta was also calculated to quantify the effect size of the monthly differences. No fish were observed from December through April, likely due to seasonal behavior changes within the local fish community. Fish returned to the shoreline in May, with the greatest diversity and evenness recorded in September. These findings reveal substantial seasonal variation in nearshore fish communities. Many monthly comparisons were found to contain statistically significant differences within the diversity and evenness, and even more were found to have large effect sizes, signifying large ecological changes throughout the seasons. The absence of fish observed during the winter months is a key outcome of this survey that will hopefully be reflected in the eDNA results still to come, which would help validate the eDNA approach.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

EPRI Software for Estimating Decommissioning Waste Volumes - 16046

EPRI has adapted computer software that can estimate the volume and activity content of waste resulting from the dismantlement of nuclear facilities. This software, designated as Automatic Estimation of the Radiological Inventory or AERI, was originally developed by Iberdrola for use by ENRESA in Spain. The software estimates the radiological inventory for all the components and civil structures of the plant or other facility that are included in the physical inventory. The radiological inventory shows the internal and external surface contamination, as well as the volumetric contamination. The processes of the software are able to estimate both the specific activity levels (in Bq/g or pCi/g) for each radionuclide and the surface activity (Bq/cm{sup 2} or dpm/100 cm{sup 2}) for components and civil structures located in the radiological areas. AERI allows the user to extrapolate, through a statistical approach, the contamination levels obtained by sampling data in specific components and civil structures to the entire facility. The main benefits and values obtained with AERI are the following: - The radiological inventory of the plant can be obtained from a limited set of radiological sampling data. Using the mathematical models allows for the optimization of the number of sampling and analysis that needs to be done. The uncertainties of the calculations are reduced with more data. The user must balance the level of uncertainty of the calculations and the number of sample data they will use. - The automatic management of radiological data in short computing times allows for the execution of several scenarios in a reasonable time. Therefore, different radioactive waste and decommissioning hypotheses can be tested. For example, inventory and data can be split into two different set. One of them can be used for obtaining radiological inventory for early dismantling (3 years, as example) and the second one for deferred dismantling (25 years.) - The software supports the planning and implementation of decommissioning strategies for nuclear plants. For instance, several decontamination factors can be evaluated, assuming different level for decontamination of materials, and results for materials from several streams and use of different container types may be included in each evaluation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools

Here, we present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution (HOD), the conditional luminosity function (CLF), abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos, or to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. Here, the package has an optimized toolkit to make mock observations on a synthetic galaxy population, including galaxy clustering, galaxy-galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others, allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Upstreamness and downstreamness in input–output analysis from local and aggregate information

Abstract Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and countries, which are encoded in input–output (I–O) tables. In this work, we show that an accurate estimation of the role played by sectors and countries in supply chain networks can be achieved without full knowledge of the I–O tables, but only relying on local and aggregate information, e.g., the total intermediate demand per sector. Our method, based on a rank-1 approximation to the I–O table, shows consistently good performance in reconstructing rankings (i.e., upstreamness and downstreamness measures for countries and sectors) when tested on empirical data from the world input–output database. Moreover, we connect the accuracy of our approximate framework with the spectral properties of the I–O tables, which ordinarily exhibit relatively large spectral gaps. Our approach provides a fast and analytically tractable framework to rank constituents of a complex economy without the need of matrix inversions and the knowledge of finer intersectorial details.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Application of Markov Chain Monte Carlo Methods for Uncertainty Quantification in Inverse Transport Problems

Determination of the components of a radioactive source/shield system using the system’s radiation signature is of great importance in homeland security, material safeguards, and waste management. Although significant progress has been made toward solving this inverse transport problem in recent years, work remains to be done to quantify the uncertainty in reconstructed results. In this article we apply two Markov chain Monte Carlo (MCMC) approaches, the delayed rejection adaptive metropolis (DRAM) and differential evolution adaptive metropolis (DREAM) methods, to solve inverse problems and quantify uncertainty. The DRAM method uses delayed rejection combined with global adaptation of the proposal covariance matrix. Furthermore, the DREAM method hybridizes MCMC sampling with the differential evolution (DE) algorithm. In numerical test cases, the DRAM and DREAM methods are shown to be superior to a first-order inverse Hessian approach for problems with noisy data and multiple unknown quantities, with DREAM converging to the posterior distribution more quickly than DRAM. The DREAM and DRAM results indicate that a full posterior distribution is required to quantify uncertainty in many inverse transport problems.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Graph neural networks for efficient learning of mechanical properties of polycrystals

Herein we present graph neural networks (GNNs) as an efficient and accurate machine learning approach to predict mechanical properties of polycrystalline materials. Here, a GNN was developed based on graph representation of polycrystals incorporating only fundamental features of grains including their crystallographic orientation, size, and grain neighbor connectivity information. We tested our method on modeling stiffness and yield strength of α -Ti microstructures, varying in their crystallographic texture. We find the GNN predicts both properties with high accuracy with mean relative errors of ~1% for unseen microstructures from a given set of textures and < 2% for microstructures of unseen texture, even when presented with limited training data. This accuracy is comparable to methods that require high-resolution three-dimensional (3D) microstructure data, such as 3D convolutional neural networks (3D-CNNs) and models that depend on the computation of spatial statistics. The present results show that graph-based deep learning is a promising framework for property prediction, especially considering the high cost associated with obtaining high-resolution 3D microstructure data and the general scarcity of experimental materials datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Bayesian mass estimates of the milky way: including measurement uncertainties with hierarchical bayes

We present a hierarchical Bayesian method for estimating the total mass and mass profile of the Milky Way Galaxy. The new hierarchical Bayesian approach further improves the framework presented by Eadie et al. and Eadie and Harris and builds upon the preliminary reports by Eadie et al. The method uses a distribution function f(E,L) to model the Galaxy and kinematic data from satellite objects, such as globular clusters (GCs), to trace the Galaxy’s gravitational potential. A major advantage of the method is that it not only includes complete and incomplete data simultaneously in the analysis, but also incorporates measurement uncertainties in a coherent and meaningful way. We first test the hierarchical Bayesian framework, which includes measurement uncertainties, using the same data and power-law model assumed in Eadie and Harris and find the results are similar but more strongly constrained. Next, we take advantage of the new statistical framework and incorporate all possible GC data, finding a cumulative mass profile with Bayesian credible regions. This profile implies a mass within 125 kpc of 4.8×10{sup 11}M{sub ⊙} with a 95% Bayesian credible region of (4.0--5.8)×10{sup 11}M{sub ⊙}. Our results also provide estimates of the true specific energies of all the GCs. By comparing these estimated energies to the measured energies of GCs with complete velocity measurements, we observe that (the few) remote tracers with complete measurements may play a large role in determining a total mass estimate of the Galaxy. Thus, our study stresses the need for more remote tracers with complete velocity measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗
DOE OSTI2017

Galaxy Evolution in the Radio Band: The Role of Star-forming Galaxies and Active Galactic Nuclei

We investigate the astrophysics of radio-emitting star-forming galaxies and active galactic nuclei (AGNs) and elucidate their statistical properties in the radio band, including luminosity functions, redshift distributions, and number counts at sub-mJy flux levels, which will be crucially probed by next-generation radio continuum surveys. Specifically, we exploit the model-independent approach by Mancuso et al. to compute the star formation rate functions, the AGN duty cycles, and the conditional probability of a star-forming galaxy to host an AGN with given bolometric luminosity. Coupling these ingredients with the radio emission properties associated with star formation and nuclear activity, we compute relevant statistics at different radio frequencies and disentangle the relative contribution of star-forming galaxies and AGNs in different radio luminosity, radio flux, and redshift ranges. Finally, we highlight that radio-emitting star-forming galaxies and AGNs are expected to host supermassive black holes accreting with different Eddington ratio distributions and to occupy different loci in the galaxy main-sequence diagrams. These specific predictions are consistent with current data sets but need to be tested with larger statistics via future radio data with multiband coverage on wide areas, as will become routinely achievable with the advent of the Square Kilometre Array and its precursors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A Bayesian analysis of classical shadows

Abstract The method of classical shadows proposed by Huang, Kueng, and Preskill heralds remarkable opportunities for quantum estimation with limited measurements. Yet its relationship to established quantum tomographic approaches, particularly those based on likelihood models, remains unclear. In this article, we investigate classical shadows through the lens of Bayesian mean estimation (BME). In direct tests on numerical data, BME is found to attain significantly lower error on average, but classical shadows prove remarkably more accurate in specific situations—such as high-fidelity ground truth states—which are improbable in a fully uniform Hilbert space. We then introduce an observable-oriented pseudo-likelihood that successfully emulates the dimension-independence and state-specific optimality of classical shadows, but within a Bayesian framework that ensures only physical states. Our research reveals how classical shadows effect important departures from conventional thinking in quantum state estimation, as well as the utility of Bayesian methods for uncovering and formalizing statistical assumptions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

The incidence of stellar mergers and mass gainers among massive stars

Because the majority of massive stars are born as members of close binary systems, populations of massive main-sequence stars contain stellar mergers and products of binary mass transfer. We simulate populations of massive stars accounting for all major binary evolution effects based on the most recent binary parameter statistics and extensively evaluate the effect of model uncertainties. Assuming constant star formation, we find that 8{sub −4}{sup +9}% of a sample of early-type stars are the products of a merger resulting from a close binary system. In total we find that 30{sub −15}{sup +10}% of massive main-sequence stars are the products of binary interaction. We show that the commonly adopted approach to minimize the effects of binaries on an observed sample by excluding systems detected as binaries through radial velocity campaigns can be counterproductive. Systems with significant radial velocity variations are mostly pre-interaction systems. Excluding them substantially enhances the relative incidence of mergers and binary products in the non-radial velocity variable sample. This poses a challenge for testing single stellar evolutionary models. It also raises the question of whether certain peculiar classes of stars, such as magnetic O stars, are the result of binary interaction and it emphasizes the need to further study the effect of binarity on the diagnostics that are used to derive the fundamental properties (star-formation history, initial mass function, mass-to-light ratio) of stellar populations nearby and at high redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Numerical methods and hypoexponential approximations for gamma distributed delay differential equations

Abstract Gamma distributed delay differential equations (DDEs) arise naturally in many modelling applications. However, appropriate numerical methods for generic gamma distributed DDEs have not previously been implemented. Modellers have therefore resorted to approximating the gamma distribution with an Erlang distribution and using the linear chain technique to derive an equivalent system of ordinary differential equations (ODEs). In this work, we address the lack of appropriate numerical tools for gamma distributed DDEs in two ways. First, we develop a functional continuous Runge–Kutta (FCRK) method to numerically integrate the gamma distributed DDE without resorting to Erlang approximation. We prove the fourth-order convergence of the FCRK method and perform numerical tests to demonstrate the accuracy of the new numerical method. Nevertheless, FCRK methods for infinite delay DDEs are not widely available in existing scientific software packages. As an alternative approach to solving gamma distributed DDEs, we also derive a hypoexponential approximation of the gamma distributed DDE. This hypoexponential approach is a more accurate approximation of the true gamma distributed DDE than the common Erlang approximation but, like the Erlang approximation, can be formulated as a system of ODEs and solved numerically using standard ODE software. Using our FCRK method to provide reference solutions, we show that the common Erlang approximation may produce solutions that are qualitatively different from the underlying gamma distributed DDE. However, the proposed hypoexponential approximations do not have this limitation. Finally, we apply our hypoexponential approximations to perform statistical inference on synthetic epidemiological data to illustrate the utility of the hypoexponential approximation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-E-T-531: Large--Scale DVH Quality Study: Correlated Aims Lead Relaxations

Purpose: Intensity modulated radiation therapy plans are designed to optimally target a tumor while sparing surrounding tissue. Desired dose distributions are iteratively approached via inverse planning. This leads to tradeoffs between clinical objectives for the planning target volume (PTV), organs at risk, and normal tissues. Dose volume histogram (DVHs) related aims are followed that are either institutional or internationally recommended. We analyze common goals and identify potential reasons that often lead to tradeoffs. Methods: 524 IMRT plans for various tumor sites were analyzed based on the main institutional DVH goal for PTV (D95) and the recommendations by ICRU-83 (D2, D50, and D98). Robust statistical tools are developed and applied to ensure that the results are immune to data uncertainties. The probability of violation was measured for each of the DVH goals based on the frequency of not meeting recommended doses. Conditional probabilities for satisfying and/or violating DVH aims were computed to test the hypothesized pair-wise relations between DVH aims. For example, for plans that satisfied D50, the probability of violating D98 was computed via P(D98 < 95% | 98% ≤ D50 ≤ 102%). The equality constraint D50 = 100% was relaxed to encompass the range [98,102]%. Results: A large majority of cases (88%) satisfied the institutional goal for PTV of D95 ≤ 95%. Similar consensus existed for D98. 51% of cases satisfied D2 ≥ 107%. However, only 18% of cases satisfied D50. The conditional probability showed correlations amongst the studied DVH goals. In fact, a negative correlation was revealed between D50 and D95 (and D98), suggesting that these competing goals cannot be satisfied concurrently. Conclusion: The majority of plans followed the institutional guidelines. The reason for their deviation from international recommendations seems to be that the latter goals are competing and cannot be satisfied concurrently in clinical practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Enhancing anaerobic digestion of lignocellulosic biomass by mechanical cotreatment

Abstract Background The aim of this study was to increase the accessibility and accelerate the breakdown of lignocellulosic biomass to methane in an anaerobic fermentation system by mechanicalcotreatment: milling during fermentation, as an alternative to conventionalpretreatment prior to biological deconstruction. Effluent from a mesophilic anaerobic digester running with unpretreated senescent switchgrass as the predominant carbon source was collected and subjected to ball milling for 0.5, 2, 5 and 10 min. Following this, a batch fermentation test was conducted with this material in triplicate for an additional 18 days with unmilled effluent as the ‘status quo’ control. Results The results indicate 0.5 – 10 min of cotreatment increased sugar solubilization by 5– 13% when compared to the unmilled control, with greater solubilization correlated with increased milling duration. Biogas concentrations ranged from 44% to 55.5% methane with the balance carbon dioxide. The total biogas production was statistically higher than the unmilled control for all treatments with 2 or more minutes of milling (α = 0.1). Cotreatment also decreased mean particle size. Energy consumption measurements of a lab-scale mill indicate that longer durations of milling offer diminishing benefits with respect to additional methane production. Conclusions Cotreatment in anaerobic digestion systems, as demonstrated in this study, provides an alternative approach to conventional pretreatments to increase biogas production from lignocellulosic grassy material.

Biotechnology & Applied Microbiology↗
DOE OSTI2020

Using the Monte Carlo Method to Evaluate the Reliability of Screening Multifamily Housing for Radon

When screening for radon in a multifamily housing complex using a fixed sample density (e.g., testing 1 in 10 (10%) or 1 in 4 units (25%)), the statistical confidence is dependent upon the assumed elevated radon frequency. For example, testing 25% of units in a complex estimated to have eight units with elevated radon levels will provide 90% confidence. However, if it is assumed that, in the same complex, there are only three units with elevated radon levels, the confidence drops to around 58% for the same sample density. Furthermore, in the previous example, if elevated radon levels are not found during the screening, all that can be stated is that the screening provides 90% confidence that there are no more than seven units in the complex with elevated radon levels. To more fully illustrate this uncertainty, ten separate multifamily housing radon data sets with 1 to 10 units with radon levels ≥4 pCi/L (Table 1) were selected for analysis using the Monte Carlo statistical method. Unlike other mathematically based statistical approaches, the Monte Carlo statistical method relies on repeated analysis of randomly selected data. from a 100% sampled complex at various sample densities. Success for each simulation is defined as finding at least one unit with elevated radon levels. In this statistical method, confidence in the overarching conclusion can be greatly enhanced by repeating the simulated screening hundreds or even thousands of times. For this study, each of the ten data sets was simulated 1,000 times. For each simulation, the data set was randomized three times before the fixed percentage of data was selected.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗