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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Pre- and post-treatment image-based dosimetry in 90 Y-microsphere radioembolization using the TOPAS Monte Carlo toolkit

Objective.To evaluate the pre-treatment and post-treatment imaging-based dosimetry of patients treated with 90Y-microspheres, including accurate estimations of dose to tumor, healthy liver and lung. To do so, the Monte Carlo (MC) TOPAS platform is in this work extended towards its utilization in radionuclide therapy. Approach. In this study, five patients treated at the Massachusetts General Hospital were selected for this study. All patients had data for both pre-treatment SPECT-CT imaging using 99mTc-MAA as a surrogate of the 90Y-microspheres treatment and SPECT-CT imaging immediately after the 90Y activity administration. Pre- and post-treatment doses were computed with TOPAS using the SPECT images to localize the source positions and the CT images to account for tissue inhomoegeneities. We compared our results with analytical calculations following the voxel-based MIRD scheme. Main results. TOPAS results largely agreed with the MIRD-based calculations in soft tissue regions: the average difference in mean dose to the liver was 0.14 Gy GBq -1 (2.6%). However, dose distributions in the lung differed considerably: absolute differences in mean doses to the lung ranged from 1.2 to 6.3 Gy GBq -1 and relative differences from 153% to 231%. We also found large differences in the intra-hepatic dose distributions between pre- and post-treatment imaging, but only limited differences in the pulmonary dose. Significance. Doses to lung were found to be higher using TOPAS with respect to analytical calculations which may significantly underestimate dose to the lung, suggesting the use of MC methods for 90Y dosimetry. According to our results, pre-treatment imaging may still be representative of dose to lung in these treatments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Hazma: a python toolkit for studying indirect detection of sub-GeV dark matter

With several proposed MeV gamma-ray telescopes on the horizon, it is of paramount importance to perform accurate calculations of gamma-ray spectra expected from sub-GeV dark matter annihilation and decay. We present hazma, a python package for reliably computing these spectra, determining the resulting constraints from existing gamma-ray data, and prospects for upcoming telescopes. For high-level analyses, hazma comes with several built-in dark matter models where the interactions between dark matter and hadrons have been determined in detail using chiral perturbation theory. Additionally, hazma provides tools for computing spectra from individual final states with arbitrary numbers of light leptons and mesons, and for analyzing custom dark matter models. hazma can also produce electron and positron spectra from dark matter annihilation, enabling precise derivation of constraints from the cosmic microwave background

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

LArSoft: Toolkit for Simulation, Reconstruction and Analysis of Liquid Argon TPC Neutrino Detectors

LArSoft is a set of detector-independent software tools for the simulation, reconstruction and analysis of data from liquid argon (LAr) neutrino experiments The common features of LAr time projection chambers (TPCs) enable sharing of algorithm code across detectors of very dierent size and conguration. LArSoft is currently used in production simulation and reconstruction by the ArgoNeuT, DUNE, LArIAT, MicroBooNE, and SBND experiments. The software suite oers a wide selection of algorithms and utilities, including those for associated photo-detectors and the handling of auxiliary detectors outside the TPCs. Available algorithms cover the full range of simulation and reconstruction, from raw waveforms to high- level reconstructed objects, event topologies and classication. The common code within LArSoft is contributed by adopting experiments, which also provide detector-specic geometry descriptions, and code for the treatment of electronic signals. LArSoft is also a collaboration of experiments, Fermilab and associated software projects which cooperate in setting requirements, priorities, and schedules. In this talk, we outline the general architecture of the software and the interaction with external libraries and detector-specic code. We also describe the dynamics of LArSoft software development between the contributing experiments, the projects supporting the software infrastructure LArSoft relies on, and the core LArSoft support project.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Ontology Development Kit: a toolkit for building, maintaining and standardizing biomedical ontologies

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management. To manage these processes, a diverse set of tools is required, from command-line utilities to powerful ontology-engineering environmentsr. Particularly in the biomedical domain, which has developed a set of highly diverse yet inter-dependent ontologies, standardizing release practices and metadata and establishing shared quality standards are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardized, customizable and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice and describe how we envision it driving standardization efforts in our community.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

The LaueUtil toolkit for Laue photocrystallography. I. Rapid orientation matrix determination for intermediate-size-unit-cell Laue data

A new method for determination of the orientation matrix of Laue X-ray data is presented. The method is based on matching of the experimental patterns of central reciprocal lattice rows projected on a unit sphere centered on the origin of the reciprocal lattice with the corresponding pattern of a monochromatic data set on the same material. This technique is applied to the complete data set and thus eliminates problems often encountered when single frames with a limited number of peaks are to be used for orientation matrix determination. Application of the method to a series of Laue data sets on organometallic crystals is described. The corresponding program is available under a Mozilla Public License-like open-source license.

Kalinowski, Jaroslaw A.↗
DOE OSTI2015

The LaueUtil toolkit for Laue photocrystallography. II. Spot finding and integration

A spot-integration method is described which does not require prior indexing of the reflections. It is based on statistical analysis of the values from each of the pixels on successive frames, followed for each frame by morphological analysis to identify clusters of high value pixels which form an appropriate mask corresponding to a reflection peak. The method does not require prior assumptions such as fitting of a profile or definition of an integration box. The results are compared with those of the seed-skewness method which is based on minimizing the skewness of the intensity distribution within a peak's integration box. Applications in Laue photocrystallography are presented.

Kalinowski, Jaroslaw A.↗
DOE OSTI2020

GIWAXS-SIIRkit: scattering intensity, indexing and refraction calculation toolkit for grazing-incidence wide-angle X-ray scattering of organic materials

Grazing-incidence wide-angle X-ray scattering (GIWAXS) has become an increasingly popular technique for quantitative structural characterization and comparison of thin films. For this purpose, accurate intensity normalization and peak position determination are crucial. At present, few tools exist to estimate the uncertainties of these measurements. Here, a simulation package is introduced called GIWAXS-SIIRkit , where SIIR stands for scattering intensity, indexing and refraction. Furthermore, the package contains several tools that are freely available for download and can be executed in MATLAB. The package includes three functionalities: estimation of the relative scattering intensity and the corresponding uncertainty based on experimental setup and sample dimensions; extraction and indexing of peak positions to approximate the crystal structure of organic materials starting from calibrated GIWAXS patterns; and analysis of the effects of refraction on peak positions. Additionally, each tool is based on a graphical user interface and designed to have a short learning curve. A user guide is provided with detailed usage instruction, tips for adding functionality and customization, and exemplary files.

36 MATERIALS SCIENCE↗
DOE OSTI2024

PyFaults: A Python Toolkit for Stacking Fault Screening

PyFaults is an open-source Python library designed to model stacking fault disorder in crystalline materials and qualitatively assess the characteristic selective broadening effects in powder X-ray diffraction (PXRD). Here, the main capabilities of PyFaults are presented, including unit cell and supercell model construction, PXRD pattern calculation, assessment against experimental PXRD, and methods for rapid screening of candidate models within a set of possible stacking vectors and fault occurrence probabilities. This program aims to serve as a computationally inexpensive tool for identifying and screening potential stacking fault models in materials with planar disorder. Three diverse case studies, involving GaN, Li2MnO3 and Li3YCl6, are presented to illustrate the program functionality across a range of structure types and stacking fault modalities.

MATHEMATICS AND COMPUTING↗
DOE OSTI2016

VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures

Execution on massively threaded processors is one of the most critical challenges for high-performance computing (HPC) scientific visualization. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Moreover, our current production scientific visualization software is not designed for these new types of architectures. In order to address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Microservices Architecture Toolkit for Interconnected Science Ecosystems

Microservices architecture is a promising approach for developing reusable scientific workflow capabilities for inte- grating diverse resources, such as experimental and observational instruments and advanced computational and data management systems, across many distributed organizations and facilities. In this paper, we describe how the INTERSECT Open Architec- ture leverages federated systems of microservices to construct interconnected science ecosystems, review how the INTERSECT software development kit eases microservice capability develop- ment, and demonstrate the use of such capabilities for deploying an example multi-facility INTERSECT ecosystem.

Brim, Michael↗
DOE OSTI2018

Quantitative ROS bioreporters: A robust toolkit for studying biological roles of ROS in response to abiotic and biotic stresses

While the accumulation of reactive oxygen species (ROS) through spontaneous generation or as the by-products of aerobic metabolism can be toxic to plants, recent findings demonstrate that ROS act as signaling molecules that play a critical role in adapting to various stress conditions. Tight regulation of ROS homeostasis is required to adapt to stress and survive, yet in vivo spatiotemporal information of ROS dynamics are still largely undefined. In order to understand the dynamics of ROS changes and their biological function in adapting to stresses, two quantitative ROS transcription-based bioreporters were developed. These reporters use ROS-responsive promoters from RBOHD or ZAT12 to drive green fluorescent protein (GFP) expression. The resulting GFP expression is compared to a constitutively expressed mCherry that is contained on the same cassette with the ROS-responsive promoter: This allows for the generation of ratiometric images comparing ROS changes (GFP) to the constitutively expressed mCherry. Both reporters were used to assess ROS levels to oxidative stress, salt stress, and the pathogen defense elicitor flg22. These bioreporters showed increases in the ratio values of GFP to mCherry signals between 10 and 30 min poststress application. Such stress-associated ROS signals correlated with the induction of abiotic/biotic stress responsive markers such as RbohD, ZAT12, SOS2 and PR5 suggesting these ROS bioreporters provide a robust indicator of increased ROS related to stress responses. Based upon the spatiotemporal response patterns of signal increase, ZAT12 promoter-dependent ROS (Zat12p-ROS) bioreporter appears to be suitable for cellular mapping of ROS changes in response to abiotic and biotic stresses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

SU-E-T-154: Calculation of Tissue Dose Point Kernels Using GATE Monte Carlo Simulation Toolkit to Compare with Water Dose Point Kernel

Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine. the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-466: Implementation of An Extension Module for Dose Response Models in the TOPAS Monte Carlo Toolkit

Purpose: To develop and verify an extension to TOPAS for calculation of dose response models (TCP/NTCP). TOPAS wraps and extends Geant4. Methods: The TOPAS DICOM interface was extended to include structure contours, for subsequent calculation of DVH’s and TCP/NTCP. The following dose response models were implemented: Lyman-Kutcher-Burman (LKB), critical element (CE), population based critical volume (CV), parallel-serials, a sigmoid-based model of Niemierko for NTCP and TCP, and a Poisson-based model for TCP. For verification, results for the parallel-serial and Poisson models, with 6 MV x-ray dose distributions calculated with TOPAS and Pinnacle v9.2, were compared to data from the benchmark configuration of the AAPM Task Group 166 (TG166). We provide a benchmark configuration suitable for proton therapy along with results for the implementation of the Niemierko, CV and CE models. Results: The maximum difference in DVH calculated with Pinnacle and TOPAS was 2%. Differences between TG166 data and Monte Carlo calculations of up to 4.2%±6.1% were found for the parallel-serial model and up to 1.0%±0.7% for the Poisson model (including the uncertainty due to lack of knowledge of the point spacing in TG166). For CE, CV and Niemierko models, the discrepancies between the Pinnacle and TOPAS results are 74.5%, 34.8% and 52.1% when using 29.7 cGy point spacing, the differences being highly sensitive to dose spacing. On the other hand, with our proposed benchmark configuration, the largest differences were 12.05%±0.38%, 3.74%±1.6%, 1.57%±4.9% and 1.97%±4.6% for the CE, CV, Niemierko and LKB models, respectively. Conclusion: Several dose response models were successfully implemented with the extension module. Reference data was calculated for future benchmarking. Dose response calculated for the different models varied much more widely for the TG166 benchmark than for the proposed benchmark, which had much lower sensitivity to the choice of DVH dose points. This work was supported by National Cancer Institute Grant R01CA140735.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

GridPACK Toolkit for Developing Power Grid Simulations on High Performance Computing Platforms

This paper describes the GridPACK™ framework, which is designed to help power grid engineers develop modeling software capable of running on todays high performance computers. The framework contains modules for setting up distributed power grid networks, assigning buses and branches with arbitrary behaviors to the network, creating distributed matrices and vectors, using parallel linear and non-linear solvers to solve algebraic equations, and mapping functionality to create matrices and vectors based on properties of the network. In addition, the framework contains additional functionality to support IO and to manage errors.

Palmer, Bruce J.↗
DOE OSTI2014

Machine Learning Toolkit for Extreme Scale

Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination of samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are considered in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets

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