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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Understanding the formation and evolution of interstellar ices: a Bayesian approach

Understanding the physical conditions of dark molecular clouds and star-forming regions is an inverse problem subject to complicated chemistry that varies nonlinearly with both time and the physical environment. In this paper, we apply a Bayesian approach based on a Markov chain Monte Carlo (MCMC) method for solving the nonlinear inverse problems encountered in astrochemical modeling. We use observations for ice and gas species in dark molecular clouds and a time-dependent, gas-grain chemical model to infer the values of the physical and chemical parameters that characterize quiescent regions of molecular clouds. We show evidence that in high-dimensional problems, MCMC algorithms provide a more efficient and complete solution than more classical strategies. The results of our MCMC method enable us to derive statistical estimates and uncertainties for the physical parameters of interest as a result of the Bayesian treatment.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Design-Space Data: Informing Common Design Decisions with Pre-Simulated Data

Design Space Exploration (DSE) analysis techniques represent a data-centric approach to integrating performance analysis in early design phases when there is the greatest potential to cheaply improve the energy efficiency of a building. We focus on a novel extension of DSE called Universal Design Space Exploration (UDSE), which leverages massive databases of pre-simulated analysis that represent all possible outcomes of common analysis workflows. These databases, called Design Spaces, become “universal” when a single pre-simulated design space can be re-applied to future unknown projects. Unlike current simulation methods, which require a design to exist before it can be analyzed and often take minutes or hours to simulate, UDSE leverages pre-simulation to deliver rapid and relevant insight as new designs are conceptualized. The data underpinning UDSE enables advanced statistical and Artificial Intelligence methods, allowing UDSE to deliver a greater understanding of the larger problem being explored, rather than simply delivering analysis of several pre-conceived design options. We believe that UDSE can provide instantaneous, relevant analysis for all building design projects at negligible cost. This paper has two main goals, to develop a relevant Universal Design Space that showcases the potential of UDSE and to release this data freely to industry and academia; thereby lowering the barrier to entry to digital literacy in statistics, ML and AI within the architecture, engineering, construction (AEC) industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Combination of cluster number counts and two-point correlations: validation on mock Dark Energy Survey

ABSTRACT We present a method of combining cluster abundances and large-scale two-point correlations, namely galaxy clustering, galaxy–cluster cross-correlations, cluster autocorrelations, and cluster lensing. This data vector yields comparable cosmological constraints to traditional analyses that rely on small-scale cluster lensing for mass calibration. We use cosmological survey simulations designed to resemble the Dark Energy Survey Year 1 (DES-Y1) data to validate the analytical covariance matrix and the parameter inferences. The posterior distribution from the analysis of simulations is statistically consistent with the absence of systematic biases detectable at the precision of the DES-Y1 experiment. We compare the χ2 values in simulations to their expectation and find no significant difference. The robustness of our results against a variety of systematic effects is verified using a simulated likelihood analysis of DES-Y1-like data vectors. This work presents the first-ever end-to-end validation of a cluster abundance cosmological analysis on galaxy catalogue level simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Two-Year Mortality After Angioplasty of the Femoro-Popliteal Artery with Uncoated Balloons and Paclitaxel-Coated Balloons—A Pooled Analysis of Four Randomized Controlled Multicenter Trials

Purpose: In view of a recent meta-analysis reporting increased mortality following angioplasty with paclitaxel-coated devices in peripheral arteries, we performed a patient-level 2-year mortality analysis based on pooled original data of four randomized controlled trials (THUNDER, FEMPAC, PACIFIER and CONSEQUENT). Methods and Results: Clinical data of four randomized controlled trial were pooled to assess 2-year mortality following paclitaxel-coated balloon (PCB) angioplasty compared to angioplasty without paclitaxel (control group). A logistic regression model was applied to identify potential predictors of mortality. At two years, 13 of 185 (7.0%) patients had died in the control group and 16/184 (8.7%) in the PBC group, p = 0.55. Kaplan–Meier analysis revealed no significant difference from all-cause death at 2 years (log rank p = 0.54). Causes of death were well balanced between the groups with no pattern or trend in favour of any specific causes in the PBC group. Logistic regression revealed that treatment groups (controls or PBC) were not a predictor of 2-year mortality. The only predictor for mortality was patient age ≥ 75 years. The delivered paclitaxel doses per patient were not significantly different in patients that died and those who did not die during the 24-month follow-up (5.300 ± 4.224 μg vs. 6.248 ± 4.629 μg, p = 0.433). Conclusions: Based on original patient-level data of four pooled randomized controlled trials, we found no increase in 2-year mortality in patients treated with PCB compared to control patients treated with uncoated balloons. Causes of death were well balanced between PCB and control patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Nuclear Data Uncertainty Quantification for Adjoint-Weighted Quantities via XGPT in Monte Carlo Codes

The Total Monte Carlo method (TMC) is an innovative approach to nuclear data uncertainty propagation. In TMC, a large number of independent ENDF files are randomly generated starting from nuclear models parameters and their uncertainties. These files are then processed with NJOY or similar codes to produce a set of formatted continuous-energy cross sections (i.e., ACE files), and independent Monte Carlo neutron transport simulations are run with these different ACE files as input. Finally the distributions of the considered response functions are obtained directly from the results of the Monte Carlo runs. Despite several improvements of the TMC method, this approach is often considered an inefficient way of propagating nuclear data uncertainties, as it requires several computationally expensive Monte Carlo runs. Covariance-based methods present important advantages for several applications, especially when different sources of uncertainties are to be investigated. For this reason, sensitivity analysis and uncertainty quantification capabilities are being implemented in several Monte Carlo neutron transport codes. On the other hand, the fact that no artificial multi-group covariance matrices are required and that higher moments of the responses distributions can be easily obtained, are definite advantages of the TMC method. In an effort to overcome the limitations of both approaches, a new method, called eXtended Generalized Perturbation Theory (XGPT) is under development. When performing nuclear data uncertainty propagation, such XGPT approach does not require generating discretized (i.e., multigroup) covariance matrices, and allows to estimate higher moments of the response probability distributions, via linear perturbation theory. One of the promising applications of the XGPT method involves its adoption in conjunction with the Proper Orthogonal Decomposition (POD) of nuclear data, to build a Reduced Order Model (ROM) for the approximation of thousands of independent TMC results in a single criticality source simulation. In the present work, this approach is employed for the first time to evaluate the impact of uncertainty in nuclear data on adjoint-weighted quantities. The method has been implemented in an extended version of the Serpent-2.1.24 Monte Carlo code and is demonstrated on two fast spectrum benchmarks, focusing on {sup 208}Pb nuclear data. A new method for propagating nuclear data uncertainties in criticality source Monte Carlo simulations has been developed. This eXtended Generalized Perturbation Theory (XGPT) approach allows to accurately reproduce reference Total Monte Carlo (TMC) uncertainty estimates in a single criticality source simulation. The present work aims to demonstrate that the proposed method can quantify nuclear data uncertainty based on adjoint-weighted quantities. The XGPT approach was applied to evaluate the impact of {sup 208}Pb nuclear data uncertainties on the effective prompt neutron lifetime (l{sub eff}) in two fast spectrum systems. The obtained XGPT results show a good agreement with the reference TMC results, obtained from independent Monte Carlo runs with {sup 208}Pb nuclear data from random TENDL-2013 evaluations. The XGPT method, implemented in a extended version of the Serpent Monte Carlo code and adopted along with the Proper Orthogonal Decomposition (POD) of the nuclear data, is able to provide an accurate reduced order model to approximate thousands TMC results within a single run, ensuring a large saving in computational requirements. The effect of {sup 208}Pb nuclear data uncertainties appears to be rather large. This is consistent with previous studies that adopted the same input random evaluations data from TENDL. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Adoption of Hypofractionated Whole-Breast Irradiation for Early-Stage Breast Cancer: A National Cancer Data Base Analysis

Purpose: To evaluate the relationship of patient, hospital, and cancer characteristics with the adoption of hypofractionation in a national sample of patients diagnosed with early-stage breast cancer. Methods and Materials: We performed a retrospective study of breast cancer patients in the National Cancer Data Base from 2004-2011 who were treated with radiation therapy and met eligibility criteria for hypofractionation. We used logistic regression to identify factors associated with receipt of hypofractionation (vs conventional fractionation). Results: We identified 13,271 women (11.7%) and 99,996 women (88.3%) with early-stage breast cancer who were treated with hypofractionation and conventional fractionation, respectively. The use of hypofractionation increased significantly, with 5.4% of patients receiving it in 2004 compared with 22.8% in 2011 (P<.001 for trend). Patients living ≥50 miles from the cancer reporting facility had increased odds of receiving hypofractionation (odds ratio 1.57 [95% confidence interval 1.44-1.72], P<.001). Adoption of hypofractionation was associated with treatment at an academic center (P<.001) and living in an area with high median income (P<.001). Hypofractionation was less likely to be used in patients with high-risk disease, such as increased tumor size (P<.001) or poorly differentiated histologic grade (P<.001). Conclusions: The use of hypofractionation is rising and is associated with increased travel distance and treatment at an academic center. Further adoption of hypofractionation may be tempered by both clinical and nonclinical concerns.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Next generation sequencing data of a defined microbial mock community

Generating sequence data of a defined community composed of organisms with complete reference genomes is indispensable for the benchmarking of new genome sequence analysis methods, including assembly and binning tools. Moreover the validation of new sequencing library protocols and platforms to assess critical components such as sequencing errors and biases relies on such datasets. We here report the next generation metagenomic sequence data of a defined mock community (Mock Bacteria ARchaea Community; MBARC-26), composed of 23 bacterial and 3 archaeal strains with finished genomes. These strains span 10 phyla and 14 classes, a range of GC contents, genome sizes, repeat content and encompass a diverse abundance profile. Short read Illumina and long-read PacBio SMRT sequences of this mock community are described. These data represent a valuable resource for the scientific community, enabling extensive benchmarking and comparative evaluation of bioinformatics tools without the need to simulate data. As such, these data can aid in improving our current sequence data analysis toolkit and spur interest in the development of new tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Real-time neutron multiplicity and source localization for criticality safety during fuel debris removal

Advancing neutron detection and analysis techniques for complex radiation environments is an ongoing focus in nuclear instrumentation and monitoring. This proposal presents research and development of a generalized real-time neutron monitoring and analysis system, applicable to any detector capable of producing time-tagged neutron count data. While the work is demonstrated using the Neutron Multiplication Analysis Detector (NoMAD), a modular 15-tube helium-3 (He-3) array, due to its availability, spatial resolution, and flexible deployment, the methods developed are extensible to other systems, including organic scintillators and fast digital detectors. This research investigates two complementary analytical techniques for real-time characterization of neutron emitting sources: neutron multiplicity estimation based on the Hage-Cifarelli formalism and spatial localization using supervised machine learning applied to spatial count rate patterns. These methods are designed to operate under dynamic, evolving conditions such as fuel debris retrieval or reactor startup, where neutron-emitting material geometries may be partially unknown or changing over time. By integrating statistical neutron emission data with spatial localization, this research aims to develop and evaluate methods for real time neutron monitoring, source characterization, and material verification. Key contributions include implementation of a low-latency data pipeline for continuous neutron multiplicity analysis, development and validation of machine learning models for spatial inference, and experimental evaluation of system performance under variable measurement conditions. The outcomes are intended to support applications in nuclear safeguards, verification, emergency response, and reactor startup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Using IR spectroscopy to determine biodiesel conversion

This project’s aim was to develop a way to use Infrared (IR) Spectroscopy to determine the extent of the conversion of oil to biodiesel produced during a typical transesterification process. The most reliable method of biodiesel analysis is gas chromatography with flame ionization detector (GC-FID) analysis. This analysis is not ideal for the educational or field applications since it is time intensive and requires expensive, specialized equipment. The use of the FT-IR is faster and more affordable. A calibration curve was created by comparing data on the purity of biodiesel samples obtained from the GC-FID analysis to the ratio of the absorbances at 1197 cm -1 to 1166 cm -1 from the FT-IR spectrum. For field application, a similar method was developed using a portable IR spectrometer, the InfraSpecTM VFA-IR spectrometer. As a result, the data collected gave a good linear fit for % purity of the samples and can be used as a method for monitoring the conversion of different types of oils to biodiesel and determining the purity of biodiesel produced from oil.

09 BIOMASS FUELS↗
DOE OSTI2018

Discrimination of Medicine Radix Astragali from Different Geographic Origins Using Multiple Spectroscopies Combined with Data Fusion Methods

Raman spectra and ultraviolet–visible absorption spectra of four different geographic origins of Radix Astragali were collected. These data were analyzed using kernel principal component analysis combined with sparse representation classification. The results showed that the recognition rate reached 70.44% using Raman spectra for data input and 90.34% using ultraviolet–visible absorption spectra for data input. A new fusion method based on Raman combined with ultraviolet–visible data was investigated and the recognition rate was increased to 96.43%. The experimental results suggested that the proposed data fusion method effectively improved the utilization rate of the original data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

On the measurement of shape: With applications to lunar regolith

With the renewed commitment from NASA and other commercial entities for a presence on the Moon, the importance of understanding the characteristics of lunar regolith and how to utilize it have become the target of increasing scrutiny. Much of what is known about lunar regolith was collected during and immediately after the Apollo program, however, analytical techniques and instrumentation have advanced in leaps and bounds in the subsequent decades. Specifically, dynamic image analysis systems have advanced to the point that millions of particles can have morphological characteristics automatically determined in relatively short time frames. Particle morphological data was collected on several lunar samples and a pair of widely used regolith simulants to ascertain the accuracy of these simulants and to explore statistical analysis methods of these large datasets. It is found that these morphology datasets can vary widely depending on the particle size of the particles, and simple averaging of the data skews the results heavily towards the numerically abundant size ranges, the fines. Different reporting methods are suggested to ameliorate these problems. When applied to the lunar regolith, the particles are noted to be less morphologically complex than initially suspected. Compared to the lunar material, the simulants are found to contain some more morphological variability and angular grains. Such difference is likely due to the wildly different comminution processes that these different powder systems are subjected to.

2D shape↗
DOE OSTI2024

Radioisotope Identification with List-Mode Gamma Ray Data: A rigorous assessment on the value of temporal information applied to radioisotope identification.

This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with “confuser” sources, or spectra with similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research rigorously examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and information theory. We further propose a basic classification model that can utilize spectral or temporal data (or both) to determine if the incorporation of temporal information can improve radioisotope identification. The findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2011

Comparison of Feynman Variance-To-Mean Measurements Using Measured and Simulated Data

Measured and simulated list-mode data from the NPOD detector system were compared for subcritical systems. Five BeRP ball configurations reflected by polyethylene are investigated in this work. The simulated data were calculated using the MCNP multiplication patch developed at LANL. In addition to comparing the moments of the counting distributions, a full analysis was conducted to infer the multiplication of each system using both the Hansen-Dowdy and Hage-Cifarelli formulisms. Several of the parameters that are input into these methods were also varied to determine their affect on the system multiplication. The main parameters investigated included the system source strength, reflector transmission, and the average number of neutrons released per 239 Pu fission ($\nu$). It is shown that when comparing the measured and simulated data while using the same method and parameters the inferred multiplication values agree within 5%. The measured and simulated multiplication values differ more as the reflector thickness increases. The moments for the simulated data were higher than the measured data for all of the configurations and also disagreed more as the reflector thickness increased. The analysis method and parameters used can lead to a large spread of inferred multiplication values. In the future more configurations will be analyzed in a similar manner and a detailed uncertainty analysis will be performed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Evaluation of Weakly Informed Priors for FLEX Data

On behalf of the U. S. Nuclear Regulatory Commission (NRC), Idaho National Laboratory (INL) has reviewed the development and use of “weakly informed prior” (WIP) probability distributions to obtain industry-wide failure probability and rate distributions for portable “FLEX” equipment, as documented in the Pressurized Water Reactor Owner’s Group (PWROG) report, FLEX Equipment Data Collection and Analysis (PWROG 18043-P) by MM Degonish. The review includes a description of the methods used by the PWROG analysts. It also includes an implementation of those methods for comparison purposes and an implementation of two constrained-noninformative (CN) distributions for further WIP comparisons. The report documents several issues that the review identified in the development of the WIP distributions. They relate to the quality and representativeness of the current portable “FLEX” component data, the choice of informed distributions for related installed components, and the lack of verification for the factors chosen to weaken the information from the installed components. Two errors occurred in the computations: the posterior distribution of the WIP was used to modify the WIP itself, resulting in a lack of independence between the WIP and the data; and an error was made in the conversion of WIP mean and variance values to beta distributions. The INL reviewers think the WIP distributions presented in the report are untenable for use in risk assessments at the present time because of these issues.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Chapter 5: Modulation Excitation Spectroscopy with Phase-Sensitive Detection for Surface Analysis

Advancements in in situ spectroscopic techniques have led to significant progress being made in elucidating heterogeneous reaction mechanisms. The potential of these progressive methods is often limited only by the complexity of the system and noise in the data. Short-lived intermediates can be challenging, if not impossible, to identify with conventional spectra analysis means. Often equally difficult is separating signals that arise from active and inactive species. Modulation excitation spectroscopy combined with phase-sensitive detection analysis is a powerful tool for removing noise from the data while simultaneously revealing the underlying kinetics of the reaction. A stimulus is applied at a constant frequency to the reaction system, for example, a reactant cycled with an inert phase. Through mathematical manipulation of the data, any signal contributing to the overall spectra but not oscillating with the same frequency as the stimulus will be dampened or removed. With phase-sensitive detection, signals oscillating with the stimulus frequency but with various lag times are amplified providing valuable kinetic information. In this chapter, some examples are provided from the literature that have successfully used modulation excitation spectroscopy with phase-sensitive detection to uncover previously unobserved reaction intermediates and kinetics. Examples from a broad range of spectroscopic methods are included to provide perspective to the reader.

36 MATERIALS SCIENCE↗
DOE OSTI2015

AUTONOMOUS GAUSSIAN DECOMPOSITION

We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

On single-crystal total scattering data reduction and correction protocols for analysis in direct space

Data reduction and correction steps and processed data reproducibility in the emerging single-crystal total-scattering-based technique of three-dimensional differential atomic pair distribution function (3D-ΔPDF) analysis are explored. All steps from sample measurement to data processing are outlined using a crystal of CuIr 2 S 4 as an example, studied in a setup equipped with a high-energy X-ray beam and a flat-panel area detector. Computational overhead as pertains to data sampling and the associated data-processing steps is also discussed. Various aspects of the final 3D-ΔPDF reproducibility are explicitly tested by varying the data-processing order and included steps, and by carrying out a crystal-to-crystal data comparison. Situations in which the 3D-ΔPDF is robust are identified, and caution against a few particular cases which can lead to inconsistent 3D-ΔPDFs is noted. Although not all the approaches applied herein will be valid across all systems, and a more in-depth analysis of some of the effects of the data-processing steps may still needed, the methods collected herein represent the start of a more systematic discussion about data processing and corrections in this field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

SU-E-T-352: Why Is the Survival Rate Low in Oropharyngeal Squamous Cell Carcinoma?

Purpose: Tumors are composed of a large number of clonogens that have the capability of indefinite reproduction. Even when there is complete clinical or radiographic regression of the gross tumor mass after treatment, tumor recurrence can occur if the clonogens are not completely eradicated by radiotherapy. This study was to investigate the colonogen number and its association with the tumor control probability (TCP) in oropharyngeal squamous cell carcinoma (OSCCA). Methods: A literature search was conducted to collect clinical information of patients with OSCCA, including the prescription dose, tumor volume and survival rate. The linear-quadratic (LQ) model was incorporated into TCP model for clinical data analysis. The total dose ranged from 60 to 70 Gy and tumor volume ranged from 10 to 50 cc. The TCP was calculated for each group according to tumor size and dose. The least χ{sup 2} method was used to fit the TCP calculation to clinical data while other LQ model parameters (α, β) were adopted from the literature, due to the limited patient data. Results: A total of 190 patients with T2–T4 OSCCA were included. The association with HPV was not available for all the patients. The 3-year survival rate was about 82% for T2 squamous cell carcinoma and 40% for advanced tumors. Fitting the TCP model to the survival data, the average clonogen number was 1.56×10{sup 12}. For the prescription dose of 70 Gy, the calculated TCP ranged from 40% to 90% when the tumor volume varied from 10 to 50 cc. Conclusion: Our data suggests variation between the clonogen number and TCP in OSCCA. Tumors with larger colonogen number tend to have lower TCP and therefore dose escalation above 70 Gy may be indicated in order to improve the TCP and survival rate. Our result will require future confirmation with a large number of patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

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