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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Simulation of H{sup -} ion source extraction systems for the Spallation Neutron Source with Ion Beam Simulator

A three-dimensional ion optical code IBSimu, which is being developed at University of Jyvaeskylae, features positive and negative ion plasma extraction models and self-consistent space charge calculation. The code has been utilized for modeling the existing extraction system of the H{sup -} ion source of the Spallation Neutron Source. Simulation results are in good agreement with experimental data. A high-current extraction system with downstream electron dumping at intermediate energy has been designed. According to the simulations it provides lower emittance compared to the baseline system at H{sup -} currents exceeding 40 mA. A magnetic low energy beam transport section consisting of two solenoids has been designed to transport the beam from the alternative electrostatic extraction systems to the radio frequency quadrupole.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Topological Interpretability for Deep Learning

With the growing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly increasing. The level at which we can trust the statistical inferences made from AI-based decision systems is an increasing concern, especially in high-risk systems such as criminal justice or medical diagnosis, where incorrect inferences may have tragic consequences. Despite their successes in providing solutions to problems involving real-world data, deep learning (DL) models cannot quantify the certainty of their predictions. These models are frequently quite confident, even when their solutions are incorrect. This work presents a method to infer prominent features in two DL classification models trained on clinical and non-clinical text by employing techniques from topological and geometric data analysis. We create a graph of a model's feature space and cluster the inputs into the graph's vertices by the similarity of features and prediction statistics. We then extract subgraphs demonstrating high-predictive accuracy for a given label. These subgraphs contain a wealth of information about features that the DL model has recognized as relevant to its decisions. We infer these features for a given label using a distance metric between probability measures, and demonstrate the stability of our method compared to the LIME and SHAP interpretability methods. This work establishes that we may gain insights into the decision mechanism of a DL model. This method allows us to ascertain if the model is making its decisions based on information germane to the problem or identifies extraneous patterns within the data.

Spannaus, Adam↗
NASA NTRS1992

High-resolution gravity model of Venus

The anomalous gravity field of Venus shows high correlation with surface features revealed by radar. We extract gravity models from the Doppler tracking data from the Pioneer Venus Orbiter by means of a two-step process. In the first step, we solve the nonlinear spacecraft state estimation problem using a Kalman filter-smoother. The Kalman filter has been evaluated through simulations. This evaluation and some unusual features of the filter are discussed. In the second step, we perform a geophysical inversion using a linear Bayesian estimator. To allow an unbiased comparison between gravity and topography, we use a simulation technique to smooth and distort the radar topographic data so as to yield maps having the same characteristics as our gravity maps. The maps presented cover 2/3 of the surface of Venus and display the strong topography-gravity correlation previously reported. The topography-gravity scatter plots show two distinct trends.

Reasenberg, R. D.↗
NASA NTRS1993

Venus gravity

The anomalous gravity field of Venus shows high correlation with surface features revealed by radar. We extract gravity models from the Doppler tracking data from the Pioneer Venus Orbiter (PVO) by means of a two-step process. In the first step, we solve the nonlinear spacecraft state estimation problem using a Kalman filter-smoother. The Kalman filter was evaluated through simulations. This evaluation and some unusual features of the filter are discussed. In the second step, we perform a geophysical inversion using a linear Bayesian estimator. To allow an unbiased comparison between gravity and topography, we use a simulation technique to smooth and distort the radar topographic data so as to yield maps having the same characteristics as our gravity maps. The maps presented cover 2/3 of the surface of Venus and display the strong topography-gravity correlation previously reported. The topography-gravity scatter plots show two distinct trends.

Reasenberg, Robert D.↗
NASA NTRSDate not supplied

Application of a Dataset-Publication Knowledge Graph for Improving Earth Science Data Search

Finding a dataset at a NASA data center that is the best fit for the researcher’s application presents a challenge, not only for a novice user but for an experienced one, due to the data complexity and a multitude of choices of the existing data. Users often search for the data based on the application they are interested in, their research domain, phenomena, research topic, etc. As existing dataset metadata may not cover these search terms, the user may not obtain the most relevant results for their purpose. This problem was addressed by leveraging the content of the titles and abstracts of the research papers that utilize NASA datasets. For this, features from the paper titles and abstracts were extracted, and then a knowledge graph (KG) was used to link these features to the datasets used in that paper. The search for the datasets was tested by querying this knowledge graph through various terms extracted from Earth Science ontologies such as Semantic Web for Earth and Environment Technology (SWEET), and it was shown that this KG search outperforms the existing search that exclusively queries the dataset metadata.

Kristina Stoyanova↗
DOE OSTI2018

Challenges and Promises of PET Radiomics

Radiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that can predict underlying tumor biology and behavior.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

TU-AB-BRA-10: Prognostic Value of Intra-Radiation Treatment FDG-PET and CT Imaging Features in Locally Advanced Head and Neck Cancer

Purpose: To predict response to radiation treatment using computational FDG-PET and CT images in locally advanced head and neck cancer (HNC). Methods: 68 patients with State III-IVB HNC treated with chemoradiation were included in this retrospective study. For each patient, we analyzed primary tumor and lymph nodes on PET and CT scans acquired both prior to and during radiation treatment, which led to 8 combinations of image datasets. From each image set, we extracted high-throughput, radiomic features of the following types: statistical, morphological, textural, histogram, and wavelet, resulting in a total of 437 features. We then performed unsupervised redundancy removal and stability test on these features. To avoid over-fitting, we trained a logistic regression model with simultaneous feature selection based on least absolute shrinkage and selection operator (LASSO). To objectively evaluate the prediction ability, we performed 5-fold cross validation (CV) with 50 random repeats of stratified bootstrapping. Feature selection and model training was solely conducted on the training set and independently validated on the holdout test set. Receiver operating characteristic (ROC) curve of the pooled Result and the area under the ROC curve (AUC) was calculated as figure of merit. Results: For predicting local-regional recurrence, our model built on pre-treatment PET of lymph nodes achieved the best performance (AUC=0.762) on 5-fold CV, which compared favorably with node volume and SUVmax (AUC=0.704 and 0.449, p<0.001). Wavelet coefficients turned out to be the most predictive features. Prediction of distant recurrence showed a similar trend, in which pre-treatment PET features of lymph nodes had the highest AUC of 0.705. Conclusion: The radiomics approach identified novel imaging features that are predictive to radiation treatment response. If prospectively validated in larger cohorts, they could aid in risk-adaptive treatment of HNC.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

X-Ray Imagery as the Record of All Data of Interest in Hypervelocity Impact Fragment Studies

Laboratory study of hypervelocity spacecraft fragmentation has traditionally involved the collection and analysis of fragments that were caught in deceleration material surrounding the impact. This process has typically involved the disintegration of the catchment material either through chemical dissolution, or through physical excavation to recover the fragments. Due to the scale of the three impact tests—the Satellite Orbital Debris Characterization Impact Test (SOCIT), the DebriSat satellite impact test, and the DebrisLV launch vehicle impact test—the latter two using more than 12 cubic meters of polyurethane foam to capture the fragments, hese projects have used x-ray imagery to precisely locate and thus, to more efficiently extract fragments in the soft-catch material. Three years into the DebriSat fragment extraction process, a side study was initiated to explore what additional information could be discerned from the x-rays, with significant results. This study was instrumental to a rapid replacement and retooling as the project was forced to replace the x-ray system around which the extraction process had been based. The revised process continues to map the debris for extraction. The project has, in parallel, systematically addressed the limits/tolerances of what x-rays can reveal about size, shape, density, mass, velocity, energy, and deformation/damage of the fragment during the deceleration in the catchment material while replicating the original extraction mapping function. All of these features have been optimized or have sufficient understanding to characterize the basic factors that will define a complete data set extracted solely from x-ray imagery. It is an ideal time to develop such a process, with extracted fragments providing “ground truth” against image-only data, and abundant available imagery of the same fragments under both the prior and replacement x-ray technologies, which have several fundamentally different characteristics. This paper addresses the types and quality of hypervelocity fragmentation data that can be and has been extracted from x-rays. It further addresses the question of whether and under what circumstances future hypervelocity experiments can use x-ray methods to largely—or to completely—avoid the extraction process in recording all appropriate results. Lastly, this paper addresses lessons learned and how future efforts can be further optimized.

John B. Bacon↗
NASA NTRSDate not supplied

The Multiplatform Precipitation Feature (MPF) Database: Synthesizing Satellite and Ground-Based Precipitation and Lightning Datasets for Convective Studies

NASA’s Lightning Imaging Sensor (LIS) and the Global Precipitation Measurement (GPM) mission have contributed a wealth of data toward global lightning and precipitation studies, respectively. Combining lightning and precipitation datasets leverages their unique insights into deep convective processes that inform about characteristics of convection and its intensity. Recent efforts to synthesize the LIS and GPM datasets prepare the opportunity for unprecedented large-scale, value-added multiplatform analyses of convection. This data synthesis proof-of-concept study elaborates on the creation of a database of reflectivity-based multiplatform precipitation features (MPFs) that capture a combination of information extracted from spatiotemporally coincident lightning and precipitation data within individual storm features. The space-based GPM Dual-frequency Precipitation Radar (DPR) provides a record of precipitation data, while the GPM Validation Network (VN) additionally incorporates ground-based polarimetric Doppler radar data to provide microphysical and kinematic context to DPR data. The LIS instrument onboard the International Space Station has contributed lightning observations since 2017. MPFs encapsulating information from these datasets are created from isolated regions of filtered, smoothed DPR reflectivity data to which ellipses are fit. Each MPF includes feature location, size, and eccentricity information as well as summary reflectivity characteristics. They also include summaries of precipitation microphysics and derived three-dimensional wind available from ground-based radar data. LIS data provides standard lightning characteristics such as flash count and density to each MPF as well as other informative metrics such as flash area and radiance. Each MPF file includes information about the original data from which the MPF and its characteristics were determined, allowing end-user reconstruction of the ellipse and deeper “level I” analysis of captured data. This database of VN-LIS MPFs enables broad statistical analysis of the relationships between the microphysical, kinematic, and electrical properties of convection. Preliminary results from a demonstration of the database will be described as well as ongoing efforts and avenues for future work.

Lightning↗
DOE OSTI2016

Method and system employing graphical electric load categorization to identify one of a plurality of different electric load types

A system for different electric loads includes sensors structured to sense voltage and current signals for each of the different electric loads; a hierarchical load feature database having a plurality of layers, with one of the layers including a plurality of different load categories; and a processor. The processor acquires voltage and current waveforms from the sensors for a corresponding one of the different electric loads; maps a voltage-current trajectory to a grid including a plurality of cells, each of which is assigned a binary value of zero or one; extracts a plurality of different features from the mapped grid of cells as a graphical signature of the corresponding one of the different electric loads; derives a category of the corresponding one of the different electric loads from the database; and identifies one of a plurality of different electric load types for the corresponding one of the different electric loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Analysis of the KROTOS KFC test by coupling X-Ray image analysis and MC3D calculations

During a hypothetical severe accident sequence in a Pressurized Water Reactor (PWR), the hot molten materials (corium) issuing from the degraded reactor core may generate a steam explosion if they come in contact with water and may damage the structures and threaten the reactor integrity. The SERENA program is an international OECD project that aims at helping the understanding of this phenomenon also called Fuel Coolant Interaction (FCI) by providing data. CEA takes part in this program by performing tests in its KROTOS facility where steam explosions using prototypic corium can be triggered. Data about the different phases in the premixing are extracted from the KROTOS X-Ray radioscopy images by using KIWI software (KROTOS Image analysis of Water-corium Interaction) currently developed by CEA. The MC3D code, developed by IRSN, is a thermal-hydraulic multiphase code mainly dedicated to FCI studies. It is composed of two applications: premixing and explosion. An overall FCI calculation with MC3D requires a premixing calculation followed by an explosion calculation. The present paper proposes an alternative approach in which all the features of the premixing are extracted from the X-Ray pictures using the KIWI software and transferred to an MC3D dataset for a direct simulation of the explosion. The main hypothesis are discussed as well as the first explosion results obtained with MC3D for the KROTOS KFC test. These results are rather encouraging and are analyzed on the basis of comparisons with the experimental data. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

The Evolution of Randomized Clinical Trial Designs to Assess Therapeutics in Alzheimer Disease

Importance The success of recent randomized clinical trials (RCTs) for Alzheimer disease (AD), particularly those focusing on anti-amyloid therapies, has been discussed at length. However, the evolution of RCT design features for AD that preceded this success remain underexplored. Objective To describe temporal changes in the features of RCT design for interventions in AD. Evidence Review PubMed, Scopus, and Web of Science databases were searched in January 2025 for phase 2 and 3 AD RCTs published between January 1992 and December 2024. RCTs that investigated an intervention for AD, with a placebo or standard-of-care control group, were included. Four assessors independently reviewed full-text articles to capture study characteristics. Main Outcomes and Measures The number of participants and the duration of RCTs as well as the target population, outcomes, and funding were extracted from published reports. These features were analyzed with respect to time using linear regression and χ 2 analyses. Results The study included 203 RCTs with 79 589 participants testing interventions in AD. From 1992 to 2024, the mean sample size increased by 464% for phase 2 RCTs (from 42 to 237), and 50% for phase 3 RCTs (from 632 to 951), while the mean trial duration increased by 188% (from 16 to 46 weeks) for phase 2, and 256% (from 20 to 71 weeks) for phase 3 RCTs. This longer duration of RCTs may be partially attributed by a greater share of disease-modifying rather than symptomatic treatments. Similarly, more recent trials required AD biomarker evidence for enrollment (from 1 of 36 [2.7%] before 2006 to 40 of 76 [52.6%] since 2019). A substantial difference in the type of therapeutics researched was observed, with anti-amyloid and anti-tau RCTs being more likely to be funded by the pharmaceutical industry compared with neurotransmitter or other RCTs (anti-amyloid or anti-tau, 68 of 71 [95.8%]; neurotransmitter, 52 of 69 [77.6%]; other, 33 of 52 [63.5%]). RCT transparency improved, with more frequent data accessibility statements, registered reports, and better reporting on race and ethnicity. Conclusions and Relevance This methodology research of AD RCTs highlights substantial changes in key features of AD clinical trials from 1992 to 2024. AD RCTs have become larger and longer, such that they are powered to detect smaller clinical differences. The increased sample sizes and duration should enable the detection of smaller and more slowly occurring outcomes, which may lead to successful RCTs of therapies with slower and more subtle efficacy.

General & Internal Medicine↗
DOE OSTI2021

Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments

Remote sensing (RS) scene classification has wide applications in the environmental monitoring and geological survey. In the real-world applications, the RS scene images taken by the satellite might have two scenarios: clear and cloudy environments. However, most of existing methods did not consider these two environments simultaneously. In this paper, we assume that the global and local features are iscriminative in either clear or cloudy environments. Many existing Convolution Neural Networks (CNN) based models have made excellent achievements in the image classification, however they somewhat ignored the global and local features in their network structure. In this paper, we propose a new CNN based network (named GLNet) with the Global Encoder and Local Encoder to extract the discriminative global and local features for the RS scene classification, where the constraints for inter-class dispersion and intra-class compactness are embedded in the GLNet training. The experimental results on two publicized RS scene classification datasets show that the proposed GLNet could achieve better performance based on many existing CNN backbones under both clear and cloudy environments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Scene segmentation through region growing

A computer algorithm to segment Landsat Thematic Mapper (TM) images into areas representing surface features is described. The algorithm is based on a region growing approach and uses edge elements and edge element orientation to define the limits of the surface features. Adjacent regions which are not separated by edges are linked to form larger regions. Some of the advantages of scene segmentation over conventional TM image extraction algorithms are discussed, including surface feature analysis on a pixel-by-pixel basis, and faster identification of the pixels in each region. A detailed flow diagram of region growing algorithm is provided.

Latty, R. S.↗
NASA NTRS2012

Hyperspectral Feature Detection Onboard the Earth Observing One Spacecraft using Superpixel Segmentation and Endmember Extraction

We present a demonstration of onboard hyperspectral image processing with the potential to reduce mission downlink requirements. The system detects spectral endmembers and then uses them to map units of surface material. This summarizes the content of the scene, reveals spectral anomalies warranting fast response, and reduces data volume by two orders of magnitude. We have integrated this system into the Autonomous Science craft Experiment for operational use onboard the Earth Observing One (EO-1) Spacecraft. The system does not require prior knowledge about spectra of interest. We report on a series of trial overflights in which identical spacecraft commands are effective for autonomous spectral discovery and mapping for varied target features, scenes and imaging conditions.

Superpixel Endmember Detection↗
DOE OSTI2016

SU-F-R-38: Impact of Smoothing and Noise On Robustness of CBCT Textural Features for Prediction of Response to Radiotherapy Treatment of Head and Neck Cancers

Purpose: To examine the impact of image smoothing and noise on the robustness of textural information extracted from CBCT images for prediction of radiotherapy response for patients with head/neck (H/N) cancers. Methods: CBCT image datasets for 14 patients with H/N cancer treated with radiation (70 Gy in 35 fractions) were investigated. A deformable registration algorithm was used to fuse planning CT’s to CBCT’s. Tumor volume was automatically segmented on each CBCT image dataset. Local control at 1-year was used to classify 8 patients as responders (R), and 6 as non-responders (NR). A smoothing filter [2D Adaptive Weiner (2DAW) with 3 different windows (ψ=3, 5, and 7)], and two noise models (Poisson and Gaussian, SNR=25) were implemented, and independently applied to CBCT images. Twenty-two textural features, describing the spatial arrangement of voxel intensities calculated from gray-level co-occurrence matrices, were extracted for all tumor volumes. Results: Relative to CBCT images without smoothing, none of 22 textural features extracted showed any significant differences when smoothing was applied (using the 2DAW with filtering parameters of ψ=3 and 5), in the responder and non-responder groups. When smoothing, 2DAW with ψ=7 was applied, one textural feature, Information Measure of Correlation, was significantly different relative to no smoothing. Only 4 features (Energy, Entropy, Homogeneity, and Maximum-Probability) were found to be statistically different between the R and NR groups (Table 1). These features remained statistically significant discriminators for R and NR groups in presence of noise and smoothing. Conclusion: This preliminary work suggests that textural classifiers for response prediction, extracted from H&N CBCT images, are robust to low-power noise and low-pass filtering. While other types of filters will alter the spatial frequencies differently, these results are promising. The current study is subject to Type II errors. A much larger cohort of patients is needed to confirm these results. This work was supported in part by a grant from Varian Medical Systems (Palo Alto, CA)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Measurement of $t\overline{t}$ production in association with additional b -jets in the eμ final state in proton–proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

This paper presents measurements of top-antitop quark pair ($t\overline{t}$) production in association with additional b-jets. The analysis utilises 140 fb -1 of proton–proton collision data collected with the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy of 13 TeV. Fiducial cross-sections are extracted in a final state featuring one electron and one muon, with at least three or four b-jets. Results are presented at the particle level for both integrated cross-sections and normalised differential cross-sections, as functions of global event properties, jet kinematics, and b-jet pair properties. Observable quantities characterising b-jets originating from the top quark decay and additional b-jets are also measured at the particle level, after correcting for detector effects. The measured integrated fiducial cross sections are consistent with $t\overline{t}$$b\overline{b}$ predictions from various next-to-leading-order matrix element calculations matched to a parton shower within the uncertainties of the predictions. State of-the-art theoretical predictions are compared with the differential measurements; none of them simultaneously describes all observables. Differences between any two predictions are smaller than the measurement uncertainties for most observables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Automatic Determination of the Weak-Beam Condition in Dark Field X-ray Microscopy

Mechanical properties in crystals are strongly correlated to the arrangement of 1D line defects, termed dislocations. Recently, Dark field X-ray Microscopy (DFXM) has emerged as a new tool to image and interpret dislocations within crystals using multidimensional scans. However, the methods required to reconstruct meaningful dislocation information from high-dimensional DFXM scans are still nascent and require significant manual oversight (i.e. supervision). In this work, we present a new relatively unsupervised method that extracts dislocation-specific information (features) from a 3D dataset (x, y, Φ) using Gram-Schmidt orthogonalization to represent the large dataset as an array of 3-component feature vectors for each position, corresponding to the weak-beam conditions and the strong-beam condition. Finally, this method offers key opportunities to significantly reduce dataset size while preserving only the crystallographic information that is important for data reconstruction.

36 MATERIALS SCIENCE↗