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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 OSTI2016

Technical Note: SCUDA: A software platform for cumulative dose assessment

Purpose: Accurate tracking of anatomical changes and computation of actually delivered dose to the patient are critical for successful adaptive radiation therapy (ART). Additionally, efficient data management and fast processing are practically important for the adoption in clinic as ART involves a large amount of image and treatment data. The purpose of this study was to develop an accurate and efficient Software platform for CUmulative Dose Assessment (SCUDA) that can be seamlessly integrated into the clinical workflow. Methods: SCUDA consists of deformable image registration (DIR), segmentation, dose computation modules, and a graphical user interface. It is connected to our image PACS and radiotherapy informatics databases from which it automatically queries/retrieves patient images, radiotherapy plan, beam data, and daily treatment information, thus providing an efficient and unified workflow. For accurate registration of the planning CT and daily CBCTs, the authors iteratively correct CBCT intensities by matching local intensity histograms during the DIR process. Contours of the target tumor and critical structures are then propagated from the planning CT to daily CBCTs using the computed deformations. The actual delivered daily dose is computed using the registered CT and patient setup information by a superposition/convolution algorithm, and accumulated using the computed deformation fields. Both DIR and dose computation modules are accelerated by a graphics processing unit. Results: The cumulative dose computation process has been validated on 30 head and neck (HN) cancer cases, showing 3.5 ± 5.0 Gy (mean±STD) absolute mean dose differences between the planned and the actually delivered doses in the parotid glands. On average, DIR, dose computation, and segmentation take 20 s/fraction and 17 min for a 35-fraction treatment including additional computation for dose accumulation. Conclusions: The authors developed a unified software platform that provides accurate and efficient monitoring of anatomical changes and computation of actually delivered dose to the patient, thus realizing an efficient cumulative dose computation workflow. Evaluation on HN cases demonstrated the utility of our platform for monitoring the treatment quality and detecting significant dosimetric variations that are keys to successful ART.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Phase transitions and critical properties in the antiferromagnetic Ising model on a layered triangular lattice with allowance for intralayer next-nearest-neighbor interactions

The phase transitions (PTs) and critical properties of the antiferromagnetic Ising model on a layered (stacked) triangular lattice have been studied by the Monte Carlo method using a replica algorithm with allowance for the next-nearest-neighbor interactions. The character of PTs is analyzed using the histogram technique and the method of Binder cumulants. It is established that the transition from the disordered to paramagnetic phase in the adopted model is a second-order PT. Static critical exponents of the heat capacity (α), susceptibility (γ), order parameter (β), and correlation radius (ν) and the Fischer exponent η are calculated using the finite-size scaling theory. It is shown that (i) the antiferromagnetic Ising model on a layered triangular lattice belongs to the XY universality class of critical behavior and (ii) allowance for the intralayer interactions of next-nearest neighbors in the adopted model leads to a change in the universality class of critical behavior.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Critical Properties of the Anisotropic Ising Model with Competitive Interactions in the Region of a Phase Transition from the Modulated Phase to the Paramagnetic One

Monte Carlo methods are used to study the anisotropic Ising model with competing interactions in the region of the phase transition from the modulated to the paramagnetic phase. Using histogram analysis of Monte Carlo data and the theory of finite-size scaling, it is shown that the transition from the modulated state to the paramagnetic one is a second-order phase transition. The critical parameters and temperatures of phase transitions in this region are calculated. It is shown that the modulated–paramagnetic phase transition cannot be described within the framework of the known universality classes of critical behavior.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Phase Transitions and Thermodynamic Properties of the Potts Model with Spin States Number q = 4 on a Hexagonal Lattice

Phase transitions and thermodynamic properties of the 2D ferromagnetic Potts model with the number of spin states q = 4 on a hexagonal lattice are investigated by the Mote Carlo method based on the Wang–Landau algorithm. The orders of the phase transitions are investigated using the Binder fourth-order cumulant method and histogram analysis of data. It is established that a first-order transition is observed in the model being investigated.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Structure and optical properties of ZnO with silver nanoparticles

Textured nanocrystalline ZnO thin films are synthesized by ion beam assisted deposition. According to X-ray diffraction data, the crystallite size is ∼25 nm. Thin (∼15 nm) ZnO layers containing Ag nanoparticles are formed in a thin surface region of the films by the implantation of Ag ions with an energy of 30 keV and a dose in the range (0.25–1) × 10{sup 17} ion/cm{sup 2}. The structure and optical properties of the layers are studied. Histograms of the size distribution of Ag nanoparticles are obtained. The average size of the Ag nanoparticles varies from 0.5 to 1.5–2 nm depending on the Ag-ion implantation dose. The optical transmittance of the samples in the visible and ultraviolet regions increases, as the implantation dose is increased. The spectra of the absorption coefficient of the implanted films are calculated in the context of the (absorbing film)/(transparent substrate) model. It is found that the main changes in the optical-density spectra occur in the region of ∼380 nm, in which the major contribution to absorption is made by Ag nanoparticles smaller than 0.75 nm in diameter. In this spectral region, absorption gradually decreases, as the Ag-ion irradiation dose is increased. This is attributed to an increase in the average size of the Ag nanoparticles. It is established that the broad surface-plasmon-resonance absorption bands typical of nanocomposite ZnO films with Ag nanoparticles synthesized by ion implantation are defined by the fact that the size of the nanoparticles formed does not exceed 1.5–2 nm.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Density of States and the Ground State Structure in the Ising Model on a Kagome Lattice with Consideration for Next-Nearest-Neighbor Interaction

Phase transitions and thermodynamic properties have been studied in the two-dimensional antiferromagnetic Ising model on a Kagome lattice by the Monte Carlo method with consideration for both nearest- and next-nearest-neighbor interaction. Using the histogram data analysis method, it has been shown that the studied model is characterized by a second-order phase transition. The temperature dependence of thermodynamic parameters has been revealed to exhibit abnormal behavior.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Phase Transitions in the Antiferromagnetic Heisenberg Model on a Body-Centered Cubic Lattice with Allowance for the Next-Nearest-Neighbor Interactions

Based on the replica exchange Monte Carlo algorithm and histogram analysis of data, the phase transitions in the three-dimensional antiferromagnetic Heisenberg model on a body-centered cubic lattice with allowance for the next-nearest-neighbor interaction are studied. The study is performed for the nextnearest- neighbor exchange interaction ratio of r = 1. It is established that, for this model, the transition from the antiferromagnetic to paramagnetic phase is a first-order phase transition.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Studying Thermodynamic Properties of the Ising Model on a Body-Centered Cubic Lattice with Competing Exchange Interactions

Using the Monte Carlo method, magnetic structures of the ground state and thermodynamic properties of the antiferromagnetic Ising model on a body-centered cubic lattice with competing exchange interactions are studied. The investigations are carried out for the ratio of the exchange interactions of next and nearest neighbors r = J{sub 2}/J{sub 1} = 2/3. All possible magnetic structures of the ground state for this ratio of exchange interactions have been obtained. It has been shown that at r = 2/3 the competition of exchange interactions does not lead to the appearance of frustration and degeneracy of the ground state. Based on the histogram data analysis, it has been shown that the phase transition of the second kind is observed in the model under study at r = 2/3.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Phase Transitions and the Critical Properties of the Heisenberg Model on a Body-Centered Cubic Lattice

The Monte Carlo replica technique is used to study phase transitions and the thermodynamic and critical properties of the three-dimensional Heisenberg antiferromagnetic model on the body-centered cubic lattice with the inclusion of the interaction of the nearest and the next-nearest neighbors. The studies are performed for the proportions of the values of the exchange interactions of the nearest and the next-nearest neighbors in the range of values of k [0.0, 0.6]. The phase transition character is analyzed based on the histogram method. The overall set of static critical indices is calculated in terms of the theory of the finite-dimensional scaling. It is shown that the class of the critical behavior universality of this model is conserved in the above range of values of k.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Phase Transitions and the Thermodynamic Properties of the Potts Model with the Number of Spin States q = 4 on a Triangular Lattice

The phase transitions and the thermodynamic properties of the two-dimensional ferromagnetic Potts model with the number of spin states q = 4 on a triangular lattice are studied on the base of the Wang–Landau algorithm of the Monte Carlo method. The phase transition characters are analyzed using the method of the four-order Binder cumulants and the histogram analysis of the data. It is found that a first-order phase transition is observed in the model under study.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

A Locally Adapting Technique for Edge Detection using Image Segmentation

Rapid growth in the field of quantitative digital image analysis is paving the way for scientific researchers to make precise measurements about objects in an image. To compute quantities from an image such as the density of compressed materials or the velocity of a shockwave, object boundaries must first be determined. Images containing regions that each have a spatial trend in intensity are of particular interest here. For edge detection, we present a supervised, statistical image segmentation method that incorporates spatial information to locate boundaries between regions with overlapping intensity histograms, specifically for images where the regions are known but precise boundary locations are unknown. The segmentation of a pixel is determined by comparing its intensity to distributions from nearby pixel intensities, and a gradient of the segmented image indicates edge locations. Because of the statistical nature of the algorithm, we use maximum likelihood estimation to quantify uncertainty about each boundary. We demonstrate the success of this algorithm at locating boundaries and providing uncertainty bands on a radiograph of a multicomponent cylinder and on an optical image of a laser-induced shockwave.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A practical guide to unbinned unfolding

Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data.

Canelli, Florencia [Univ. of Zurich (Switzerland)]↗
DOE OSTI2025

Poster Abstract: Leveraging Large Language Models to Reveal Interpretable Cooling Behaviors from Smart Thermostat Data

Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.

Nihar, Kopal↗
DOE OSTI2019

Troubleshooting deep-learner training data problems using an evolutionary algorithm on Summit

Architectural and hyper-parameter design choices can influence deep-learner (DL) model fidelity but can also be affected by malformed training and validation data. However, practitioners may spend significant time refining layers and hyper-parameters before discovering that distorted training data was impeding training progress. We found that an evolutionary algorithm (EA) can be used to diagnose this kind of DL problem. An EA evaluated thousands of DL configurations on Summit that yielded no overall improvement in DL performance, which suggested problems with the training and validation data. We suspected that Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement that was applied to previously generated digital surface models (DSMs), for which we were training DLs to find errors, had damaged the training data. Subsequent runs with an alternative global normalization yielded significantly improved DL performance. However, the DL Intersection Over Union (IOU) still exhibited consistent sub-par performance, which suggested further problems with the training data and DL approach. Nonetheless, we were able to diagnose this problem within a 12-hour span via Summit runs, which prevented several weeks of unproductive trial-and-error DL configuration refinement, and allowed for a more timely convergence on an ultimately viable solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Observation of a single protein by ultrafast X-ray diffraction

* runs/ - The raw data from the pnCCD * histograms.h5 - Histogram of the values recorded by each pixel throughout the data acquisition. Used for pedestal and gain correction. * mask.h5 - Detector bad pixels mask. * 1SS8.pdb - PDB model of the assembled GroEL 14-mer, from the 1SS8 PDB entry. * 1SS8_H2O.pdb - Hydrated version of the assembled GroEL 14-mer, derived from the 1SS8 PDB entry. The various hydrated models are created by removing water from this initial model. * rotations.h5 - Quaternions of the rotations used for template matching

European XFEL↗
DOE OSTI2019

numericalFunctions

The numericalFunction C library is designed to handle point-wise interpolative data representing a mathematical function. The library currently handles 1-d functions (i.e., functions of the format f(x)). The main object is the ptwXYPoints C struct that holds list of x,y pairs sorted by ascending x-values with an interpolation rule. That is, it contains the 1-d function f(x) = y(x). Functions are included that support common unitary and binary operations (e.g., negation, addition, multiplication). As example, if f(x) and g(x) are two ptwXYPoints instances, then routines to calculate f(x) + g(x) and f(x) * g(x) are included. The library support storage of data with lin-lin, lin-log, log-lin, log-log and flat (or histogram) interpolation. Functions to convert any of the supported interpolation to lin-lin are included. However, many of the binary functions only support lin-lin interpolation. The library also contains a C struct for storing a Legendre series representation of a 1-d function, and functions to convert Legendre series to and from a ptwXYPoints instance. A python interface to the library is included, with the ptwXYPoints C struct treated as a class. This class has many of the standard python math methods (e.g., __add__, __mul__).

Beck, Bret↗
DOE OSTI2020

XRD-PUAT (X-Ray Diffraction - Parameter Uncertainty Analysis Toolkit)

This XRD uncertainty toolkit was built to investigate uncertainty and local least squares topology over a specified parameter space on a single histogram in a gpx GSAS-II file. The parameter uncertainty can be investigated using either a frequentist F-test approach or a Bayesian Inference statistical inversion method on a weighted least squares or peak fit refinements.SAND Number: SAND2020-12227 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Moore, Alexander↗
DOE OSTI2021

HUNTRESS: a fast heuristic for reconstructing phylogenetic trees of tumor evolution (HUNTRESS) v0.1

We introduce HUNTRESS (Histogrammed UNion Tree REconStruction heuriStic), a computational method for tumor phylogeny reconstruction from noisy genotype matrices derived from single-cell sequencing data, whose running time is linear with the number of cells and quadratic with the number of mutations. Provided that the input genotype matrix includes no false positives, each cellular subpopulation is at least a user defined fraction of the total number of cells, and the number of cells are much bigger than the number of mutations considered, HUNTRESS computes the ground truth tumor phylogeny with high probability. On simulated data HUNTRESS is faster than available alternatives with comparable or better accuracy. Additionally, the phylogenies reconstructed by HUNTRESS on two single-cell sequencing data sets agree with the best known evolutionary scenarios for the associated tumors.

Buluc, Aydin↗