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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Stochastic parametric skeletal dosimetry model for humans: General approach and application to active marrow exposure from bone-seeking beta-particle emitters

The objective of this study is to develop a skeleton model for assessing active marrow dose from bone-seeking beta-emitting radionuclides. This article explains the modeling methodology which accounts for individual variability of the macro- and microstructure of bone tissue. Bone sites with active hematopoiesis are assessed by dividing them into small segments described by simple geometric shapes. Spongiosa, which fills the segments, is modeled as an isotropic three-dimensional grid (framework) of rod-like trabeculae that “run through” the bone marrow. Randomized multiple framework deformations are simulated by changing the positions of the grid nodes and the thickness of the rods. Model grid parameters are selected in accordance with the parameters of spongiosa microstructures taken from the published papers. Stochastic modeling of radiation transport in heterogeneous media simulating the distribution of bone tissue and marrow in each of the segments is performed by Monte Carlo methods. Model output for the human femur at different ages is provided as an example. The uncertainty of dosimetric characteristics associated with individual variability of bone structure was evaluated. An advantage of this methodology for the calculation of doses absorbed in the marrow from bone-seeking radionuclides is that it does not require additional studies of autopsy material. The biokinetic model results will be used in the future to calculate individual doses to members of a cohort exposed to 89,90 Sr from liquid radioactive waste discharged to the Techa River by the Mayak Production Association in 1949–1956. Further study of these unique cohorts provides an opportunity to gain more in-depth knowledge about the effects of chronic radiation on the hematopoietic system. In addition, the proposed model can be used to assess the doses to active marrow under any other scenarios of 90 Sr and 89 Sr intake to humans.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

classLog: Logistic regression for the classification of genetic sequences

Introduction Sequencing and phylogenetic classification have become a common task in human and animal diagnostic laboratories. It is routine to sequence pathogens to identify genetic variations of diagnostic significance and to use these data in realtime genomic contact tracing and surveillance. Under this paradigm, unprecedented volumes of data are generated that require rapid analysis to provide meaningful inference. Methods We present a machine learning logistic regression pipeline that can assign classifications to genetic sequence data. The pipeline implements an intuitive and customizable approach to developing a trained prediction model that runs in linear time complexity, generating accurate output rapidly, even with incomplete data. Our approach was benchmarked against porcine respiratory and reproductive syndrome virus (PRRSv) and swine H1 influenza A virus (IAV) datasets. Trained classifiers were tested against sequences and simulated datasets that artificially degraded sequence quality at 0, 10, 20, 30, and 40%. Results When applied to a poor-quality sequence data, the classifier achieved between >85% to 95% accuracy for the PRRSv and the swine H1 IAV HA dataset and this increased to near perfect accuracy when using the full dataset. The model also identifies amino acid positions used to determine genetic clade identity through a feature selection ranking within the model. These positions can be mapped onto a maximum-likelihood phylogenetic tree, allowing for the inference of clade defining mutations. Discussion Our approach is implemented as a python package with code available at https://github.com/flu-crew/classLog .

Zeller, Michael A.↗
NASA NTRS1974

Magnetic measurements in model experiments

Plasma parameters for modeling of the interaction between the solar wind and the earth's magnetic field are selected on the limited modeling principle (plasma velocity 3 x 10 to the 7th power cm/sec, density 10 to the 13th power cm/3, electron temperature approximately 15 eV). Measurements were made with magnetic probes capable of developing all 3 components inside the magnetic field. The resulting magnetic field charts indicate that a neutral layer forms on the night side in the model experiments. A magnetosphere free of plasma streams is established on the daytime side. A shock wave forms on the boundary of the magnetosphere, its width c/omega sub 0 in accordance with theory, where omega sub 0 is the total Langmuir width.

Dubinin, E. M.↗
NASA NTRS1974

An interpretation of the carbon-oxygen to iron ratio

Data from recent measurements on the composition of primary cosmic rays above 1 GeV/nucleon are critically reviewed for information pertaining to selection of suitable energy-dependent composition models. Areas where crucial experimental information may result in selection of a suitable model are pointed out. In particular, the study of the energy distribution of VVH nuclei, and the derivation of more accurate energy spectra of 10 less than or equal to Z less than or equal to 14, 15 less than or equal to Z less than or equal to 23 and Z greater than 24 would contribute to discriminate between models which rely on interstellar propagation and those that ascribe a different source mechanism for Fe group nuclei.

Balasubrahmanyan, V. K.↗
DOE OSTI2021

Where’s Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data

Abstract We present the Swimmy (Subaru WIde-field Machine-learning anoMalY) survey program, a deep-learning-based search for unique sources using multicolored (grizy) imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). This program aims to detect unexpected, novel, and rare populations and phenomena, by utilizing the deep imaging data acquired from the wide-field coverage of the HSC-SSP. This article, as the first paper in the Swimmy series, describes an anomaly detection technique to select unique populations as “outliers” from the data-set. The model was tested with known extreme emission-line galaxies (XELGs) and quasars, which consequently confirmed that the proposed method successfully selected $\sim\!\! 60\%$–$70\%$ of the quasars and $60\%$ of the XELGs without labeled training data. In reference to the spectral information of local galaxies at z = 0.05–0.2 obtained from the Sloan Digital Sky Survey, we investigated the physical properties of the selected anomalies and compared them based on the significance of their outlier values. The results revealed that XELGs constitute notable fractions of the most anomalous galaxies, and certain galaxies manifest unique morphological features. In summary, deep anomaly detection is an effective tool that can search rare objects, and, ultimately, unknown unknowns with large data-sets. Further development of the proposed model and selection process can promote the practical applications required to achieve specific scientific goals.

Astronomy & Astrophysics↗
DOE OSTI2020

Pore-Space-Partition-Enabled Exceptional Ethane Uptake and Ethane-Selective Ethane–Ethylene Separation

A modell material for C 2 H 6 /C 2 H 4 separation would simultaneously have the highest C 2 H 6 uptake capacity and the highest C 2 H 6 /C 2 H 4 selectivity. But such material is elusive. A benchmark material for ethane-selective C 2 H 6 /C 2 H 4 separation is peroxo-functionalized MOF-74-Fe that exhibits the best known separation performance due to its high C 2 H 6 /C 2 H 4 selectivity (4.4), although its C 2 H 6 uptake capacity is moderate (74.3 cm 3 /g). Here, we report a family of pore-space-partitioned crystalline porous materials (CPMs) with exceptional C 2 H 6 uptake capacity and C 2 H 6 /C 2 H 4 separation potential (i.e., C 2 H 4 recovered from the mixture) despite their moderate C 2 H 6 /C 2 H 4 selectivity (up to 1.75). The ethane uptake capacity as high as 166.8 cm 3 /g at 1 atm and 298 K, more than twice that of peroxo-MOF-74-Fe, was previously achieved even though the isosteric heat of adsorption (21.9–30.4 kJ/mol) for these CPMs is as low as about one-third of that for peroxo-MOF-74-Fe (66.8 kJ/mol). While the overall C 2 H 6 /C 2 H 4 separation potentials have not yet surpassed peroxo-MOF-74-Fe, these robust CPMs exhibit outstanding properties including high thermal stability (up to 450 °C) and aqueous stability, low regeneration energy, and a high degree of chemical and geometrical tunability within the same isoreticular framework.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1981

Determination of model structure and parameters of an airplane from pre- and post-stall flight data

A procedure for airplane model structure determination from flight data based on modified stepwise regression (MSR), several decision criteria and postulated aerodynamic model equations is presented. The MSR is constructed to force a linear model for the aerodynamic coefficient first, then select significant nonlinear terms and reject nonsignificant terms from the model. In addition to the statistical criteria in the stepwise regression, the prediction sum of squares (PRESS) criterion and analysis of residuals are examined for the selection of an adequate model. The procedure is used in examples with simulated and real flight data. It is shown that the MSR performs better than the ordinary stepwise regression and that the technique can be also applied to the large amplitude maneuvers.

Klein, V.↗
DOE OSTI2025

Using Boosted Decision Trees to Select High Quality Measurements in the Mu2e Experiment at Fermilab

This thesis presents the implementation and evaluation of a Boosted Decision Tree (BDT) model to improve the selection of high-quality track measurements in the Mu2e experiment at Fermilab. The Mu2e experiment is a high-energy physics experiments seeking to observe a rare theoretical physics process known as Charged Lepton Flavor Violation. A significant challenge faced by the Mu2e experiment are so-called background events, which are events whose data mimics that of the rare physics process the experiment seeks to observe. Without a mechanism to reduce background, it would be impossible to know whether Charged Lepton Flavor Violation occurred or not. To this end, high-quality track measurements must be distinguished from low-quality track measurements. A track can be conceived of as the reconstructed path of a particle that traveled through the Mu2e detector. In addition to other data, data about such tracks is stored using a C++-based framework, specific to the domain of high-energy physics, known as ROOT. A boosted decision tree model was trained using ROOT’s Toolkit For Multivariate Analysis by leveraging variables ancillary to track quality. In evaluation, the BDT achieves a ROC-AUC of 0.927 in discriminating good-quality tracks from poor-quality tracks. Such a score is indicative of both strong discrimination and strong generalization. Subsequently, it is shown that applying a BDT-based quality cut to the distribution of particle momenta significantly enhances the signal-to-background distinction for signal electrons, paving the way for improved sensitivity to Charged Lepton Flavor Violation.

Mullany, Brendan T. [Drew U.] (ORCID:0009000818888↗
DOE OSTI2021

Probing Active Sites in Cu x Pd y Cluster Catalysts by Machine-Learning-Assisted X-ray Absorption Spectroscopy

Size-selected clusters are important model catalysts because of their narrow size and compositional distributions, as well as enhanced activity and selectivity in many reactions. Still, their structure-activity relationships are, in general, elusive. The main reason is the difficulty in identifying and quantitatively characterizing the catalytic active site in the clusters when it is confined within subnanometric dimensions and under the continuous structural changes the clusters can undergo in reaction conditions. Using machine learning approaches for analysis of the operando X-ray absorption near-edge structure spectra, we obtained accurate speciation of the Cu x Pd y cluster types during the propane oxidation reaction and the structural information about each type. As a result, we elucidated the information about active species and relative roles of Cu and Pd in the clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Discovering Ni/Cu Single-Atom Alloy as a Highly Active and Selective Catalyst for Direct Methane Conversion to Ethylene: A First-Principles Kinetic Study

Direct methane conversion to liquid fuels or value-added chemicals is a promising technology to utilize natural resources without resorting to further petroleum extraction. However, discovering efficient catalysts for this reaction is challenging due to either coke formation or unfavorable C-H bond activation. Herein, we design single-atom alloy (SAA) catalysts to simultaneously eliminate the above two bottlenecks based on mechanism-guided strategies: (1) the active single atom enables favorable C-H bond breaking and (2) the less reactive host metal facilitates C-C coupling and thus avoids strong binding of carbonaceous species. Employing electronic structure theory calculations, we screened the stability of multiple SAAs with 3d-5d transition metals atomically dispersed on a copper surface in terms of avoiding dopant aggregation and segregation. We then evaluated reactivities of the stable SAAs as catalysts for direct methane conversion to C2 products, including methane dehydrogenation and C-C coupling mechanisms. Combining selectivity analysis with kinetic modeling, we predicted that nickel dispersed on copper, i.e., Ni/Cu SAA, is a highly active and selective catalyst that can efficiently transform methane to ethylene. This work designs efficient SAA catalysts for direct methane activation and provides chemical insights into engineering compositions of SAAs to tune their catalytic performances.

Kothakonda, Manish↗
NASA NTRS1972

Kullback-Leibler information function and the sequential selection of experiments to discriminate among several linear models

The error variance of the process prior multivariate normal distributions of the parameters of the models are assumed to be specified, prior probabilities of the models being correct. A rule for termination of sampling is proposed. Upon termination, the model with the largest posterior probability is chosen as correct. If sampling is not terminated, posterior probabilities of the models and posterior distributions of the parameters are computed. An experiment was chosen to maximize the expected Kullback-Leibler information function. Monte Carlo simulation experiments were performed to investigate large and small sample behavior of the sequential adaptive procedure.

Sidik, S. M.↗
NASA NTRS1972

Kullback-Leibler information function and the sequential selection of experiments to discriminate among several linear models

A sequential adaptive experimental design procedure for a related problem is studied. It is assumed that a finite set of potential linear models relating certain controlled variables to an observed variable is postulated, and that exactly one of these models is correct. The problem is to sequentially design most informative experiments so that the correct model equation can be determined with as little experimentation as possible. Discussion includes: structure of the linear models; prerequisite distribution theory; entropy functions and the Kullback-Leibler information function; the sequential decision procedure; and computer simulation results. An example of application is given.

Sidik, S. M.↗
NASA NTRS1979

The response of selected terrestrial organisms to the Martian environment - A modeling study

An energy balance model has been developed to investigate how the Martian atmospheric environment could influence a community of photosynthetic microorganisms with properties similar to those of a cyanophyte (blue-green algal mat) and a lichen. Surface moisture and soil nutrients are assumed to be available. The model was developed to approximate equatorial equinox conditions and includes parameters for solar and thermal radiation, convective and conductive energy transport, and evaporative cooling. Calculations include the diurnal variation of organism temperature and transpiration and photosynthetic rates. The influences of different wind speeds and organism size and resistivity are also studied. The temperature of organisms in mats less than a few millimeters thick will not differ from the ground temperature by more than 10 K. Water loss is actually retarded at higher wind speeds, since the organism temperature is lowered, thus reducing the saturation vapor pressure. Typical photosynthetic rates lead to the production of 1 millionth to 100 billionths mole O2 per sq cm/day.

Kuhn, W. R.↗
NASA NTRS1986

Two component model for X-ray emission of radio selected QSO's

Using a large database of radio, optical, and x ray luminosities of AGNs with survival analysis, it was found that the x ray emission of the radio selected quasars has two components. One is related to the optical luminosity and the other is related to the radio luminosity.

Isobe, T.↗
NASA NTRS2004

Magnetic Circuit Model of PM Motor-Generator to Predict Radial Forces

A magnetic circuit model is developed for a PM motor for flywheel applications. A sample motor is designed and modeled. Motor configuration and selection of materials is discussed, and the choice of winding configuration is described. A magnetic circuit model is described, which includes the stator back iron, rotor yoke, permanent magnets, air gaps and the stator teeth. Iterative solution of this model yields flux linkages, back EMF, torque, power, and radial force at the rotor caused by eccentricity. Calculated radial forces are then used to determine motor negative stiffness.

McLallin, Kerry↗