Search NASA⌕ Search

SEARCH · Search NASA

Results for “Randomized methods”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-J-263: Repeatability of SUV and Texture Parameters in Serial PET Studies

Purpose: Standardized uptake values (SUV) are standard quantitative PET measures of FDG tumor uptake used,and are used as a tool to monitor response to therapy. Textural analysis is emerging as a new tool for assessing intratumoral heterogeneity which may allow better tissue characterization and improved prediction of response and survival rate.Understanding what variations may be expected in these parameters is key in order to make decisions based on how the change throughout the course of treatment. The aim of this study was to assess repeatability in SUV measures and texture parameters,and establish criteria that differentiate changes associated with treatment rather than statistical variability. Methods: Eighty patients,167 random lesions total,were scanned in a GE Discovery STE PET/CT Scanner. One field-of-view was chosen centered on the largest lesion observed in a clinical whole-body FDG PET.Immediately following,a gated 9 min scan was acquired in list mode,without changing the patient’s position between any scans. Data was replayed into 3 time bins,3 min each,in order to insure equivalent noise characteristics in each replicate.Data was reconstructed into 128×128×47 square matrices.One VOI was drawn over each lesion for each patient and used to segment all 3 replicates. The mean.max and peak SUV were calculated for each VOI and replicate. First-order textural features were also calculated (skewness and kurtosis). Repeatability was calculated as the average standard deviation over the mean for the 3 repeated measurements for each lesion. Results: The average percent error in the SUV max,peak and mean were 3.4%(0– 12.9%),1.9% (0–7.5%),2.8% (0–12.2%),respectively.For skewness and kurtosis they were 10.9% and 17.8%. Conclusion: We have shown that there is a large variation in %error in SUV measures across patients. SUVpeak is the least variable and kurtosis and skewness parameters are less reliable thatn SUVs.Higher order textures are be.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-207B-03: A Computational Methodology for Determination of CTV-To-PTV Margins with Inter Fractional Shape Variations Based On a Statistical Point Distribution Model for Prostate Cancer Radiation Therapy

Purpose: Our assumption was that interfractional shape variations of target volumes could not be negligible for determination of clinical target volume (CTV)-to-planning target volume (PTV) margins. The aim of this study was to investigate this assumption as a simulation study by developing a computational framework of CTV-to-PTV margins with taking the interfractional shape variations into account based on point distribution model (PDM) Methods: The systematic and random errors for interfractional shape variations and translations of target volumes were evaluated for four types of CTV regions (only a prostate, a prostate plus proximal 1-cm seminal vesicles, a prostate plus proximal 2-cm seminal vesicles, and a prostate plus whole seminal vesicles). The CTV regions were delineated depending on prostate cancer risk groups on planning computed tomography (CT) and cone beam CT (CBCT) images of 73 fractions of 10 patients. The random and systematic errors for shape variations of CTV regions were derived from PDMs of CTV surfaces for all fractions of each patient. Systematic errors of shape variations of CTV regions were derived by comparing PDMs between planning CTV surfaces and average CTV surfaces. Finally, anisotropic CTV-to-PTV margins with shape variations in 6 directions (anterior, posterior, superior, inferior, right, and left) were computed by using a van Herk margin formula. Results: Differences between CTV-to-PTV margins with and without shape variations ranged from 0.7 to 1.7 mm in anterior direction, 1.0 to 2.8 mm in posterior direction, 0.8 to 2.8 mm in superior direction, 0.6 to 1.6 mm in inferior direction, 1.4 to 4.4 mm in right direction, and 1.3 to 5.2 mm in left direction. Conclusion: More than 1.0 mm additional margins were needed at least in 3 directions to guarantee CTV coverage due to shape variations. Therefore, shape variations should be taken into account for the determination of CTV-to-PTV margins.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. (In homophilic graphs vertices of the same class are more likely to be connected, and vertices of different classes tend to be linked in heterophilic graphs.) While GNNs have been successfully applied to homophilic graphs, their application to heterophilic graphs remains challenging. The best-performing GNNs for heterophilic graphs do not fit the sampling paradigm, suffer high computational costs, and are not inductive. We employ samplers based on feature-similarity and feature-diversity to select subsets of neighbors for a node, and adaptively capture information from homophilic and heterophilic neighborhoods using dual channels. Currently, AGS-GNN is the only algorithm that we know of that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, which was not used in this context prior to our work. The sampling distribution is pre-computed and highly parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (<=100K nodes) and large (>100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compare to the current approaches in the literature. AGS-GNN achieves comparable test accuracy to the best-performing heterophilic GNNs, even outperforming methods using the entire graph for node classification. AGS-GNN also converges faster compared to methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.

artificial intelligence↗
DOE OSTI2017

Comparative Analysis and Classification of Cassette Exons and Constitutive Exons

Alternative splicing (AS) is a major engine that drives proteome diversity in mammalian genomes and is a widespread cause of human hereditary diseases. More than 95% of genes in the human genome are alternatively spliced, and the most common type of AS is the cassette exon. Recent discoveries have demonstrated that the cassette exon plays an important role in genetic diseases. To discover the formation mechanism of cassette exon events, we statistically analyze cassette exons and find that cassette exon events are strongly influenced by individual exons that are smaller in size and that have a lower GC content, more codon terminations, and weaker splice sites. We propose an improved random-forest-based hybrid method of distinguishing cassette exons from constitutive exons. Our method achieves a high accuracy in classifying cassette exons and constitutive exons and is verified to outperform previous approaches. It is anticipated that this study will facilitate a better understanding of the underlying mechanisms in cassette exons.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

GANISP (GAN-assisted Importance SPlitting)

Genealogical importance splitting marches towards a rare event by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is made difficult when applied to deterministic systems as the initial conditions of the offspring realizations need to be adjusted. Typically, a random perturbation is applied to the offspring. For some cases, this cloning technique may not be adequate and prevent variance reduction in the probability estimate. A GAN-based replication process is proposed to address this limitation. The perturbations applied to the clones are physically consistent instead of being randomly chosen. The proposed method allows reducing the variance in the probability estimation.

Hassanaly, Malik↗
DOE OSTI2011

Vitreal Ocygenation in Retinal Ischemia Reperfusion

PURPOSE. To study the feasibility of anterior vitreal oxygenation for the treatment of acute retinal ischemia. METHODS. Twenty rabbits were randomized into an oxygenation group, a sham treatment group, and a no treatment group. Baseline electroretinography (ERG) and preretinal oxygen (PO2) measurements were obtained 3 to 5 days before surgery. Intraocular pressure was raised to 100 mm Hg for 90 minutes and then normalized. The oxygenation group underwent vitreal oxygenation for 30 minutes using intravitreal electrodes. The sham treatment group received inactive electrodes for 30 minutes while there was no intervention for the no treatment group. Preretinal PO2 in the posterior vitreous was measured 30 minutes after intervention or 30 minutes after reperfusion (no treatment group) and on postoperative days (d) 3, 6, 9, and 12. On d14, rabbits underwent ERG and were euthanatized.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Transcripts and genomic intervals associated with variation in metabolite abundance in maize leaves under field conditions

Abstract Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcript’s impacts on plant metabolism has employed data from controlled environments. Here, we generated and analyzed data on the variation in the abundance of 26 metabolites across 660 maize inbred lines under field conditions. We employ these data and previously published transcript and whole plant phenotype data reported for the same field experiment to identify both genomic intervals (through genome-wide association studies (GWAS)) and transcripts (using both transcriptome-wide association studies (TWAS) and an explainable artificial intelligence (AI) approach based on random forest (RF)) associated with variation in metabolite abundance. Both genome-wide association and random forest-based methods identified substantial numbers of significant associations including genes with plausible links to the metabolites they are associated with. In contrast, the transcriptome-wide association identified only six significant associations. In three cases, genetic markers associated with metabolic variation in our study colocalized with markers linked to variation in non-metabolic traits scored in the same experiment. We speculate that the poor performance of transcriptome-wide association studies in identifying transcript-metabolite associations may reflect a high prevalence of non-linear interactions between transcripts and metabolites and/or a bias towards rare transcripts playing a large role in determining intraspecific metabolic variation.

Mathivanan, Ramesh Kanna↗
DOE OSTI2021

A Comparison of Time Series Gap-Filling Methods to Impute Solar Radiation Data

Complete solar resource datasets play a critical role at every stage of solar project phases. However, measured or modeled solar resource data come with significant uncertainties and usually suffer from several issues, including but not limited to, data gaps, data quality issue, etc. In order to mitigate these issues an appropriate data imputation method should be implemented to build a complete and reliable temporal (and spatial) database. Being motivated by this, in this study we compare the performances of eight different gap filling methods extensively by creating random and artificial data gaps in (i) hourly irradiance data for one year using a few locations of the National Solar Radiation Database (NSRDB) and (ii) one-minute ground measurement dataset from Surface Radiation Budget Network (SURFRAD) stations.

clearness index↗
DOE OSTI2025

Developing ML/AI Methods for High-Throughput Characterization of Multiple-Sensor Streams of Tokamak Dynamics for High-Speed Control (Final Report)

This project evaluated and developed new mathematical and algorithmic techniques capable of handling (in real-time) the growing amounts of data generated by modern fusion research. While existing numerical linear algebra (NLA) methods provide the backbone to classical data analysis and algorithms, these methods fundamentally do not port to distributed architectures nor do they allow low-latency data reduction for control. Motivated by the needs for modern fusion reactors, this project explored and implemented new numerical methods to characterize plasma dynamics, respond in real-time to discharge evolution, and to process massive-scale data accurately and rapidly more fully. This project links expertise in multiple-sensor diagnostics of tokamak plasma dynamics from Columbia University’s Plasma Physics Laboratory with expertise in massive-scale data reduction and extreme data control algorithms at Columbia University’s Data Science Institute. This interdisciplinary project (i) applied machine learning methods, (ii) implemented a properly-trained neural-network for very fast processing of high-speed plasma videography, and (ii) developed the applied mathematical methods, based on randomized-NLA (rNLA) routines, for data analysis, reduction, and real-time control. The Columbia University High Beta Tokamak-Extended Pulse (HBT-EP) facility provided data to test new algorithms and partnership with Columbia University's Data Sciences Institute evaluated the broader use of new algorithms for many challenging control applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Targeted mutagenesis and high-throughput screening of diversified gene and promoter libraries for isolating gain-of-function mutations

Targeted mutagenesis of a promoter or gene is essential for attaining new functions in microbial and protein engineering efforts. In the burgeoning field of synthetic biology, heterologous genes are expressed in new host organisms. Similarly, natural or designed proteins are mutagenized at targeted positions and screened for gain-of-function mutations. Here, we describe methods to attain complete randomization or controlled mutations in promoters or genes. Combinatorial libraries of one hundred thousands to tens of millions of variants can be created using commercially synthesized oligonucleotides, simply by performing two rounds of polymerase chain reactions. With a suitably engineered reporter in a whole cell, these libraries can be screened rapidly by performing fluorescence-activated cell sorting (FACS). Within a few rounds of positive and negative sorting based on the response from the reporter, the library can rapidly converge to a few optimal or extremely rare variants with desired phenotypes. Library construction, transformation and sequence verification takes 6–9 days and requires only basic molecular biology lab experience. Screening the library by FACS takes 3–5 days and requires training for the specific cytometer used. Further steps after sorting, including colony picking, sequencing, verification, and characterization of individual clones may take longer, depending on number of clones and required experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The PENELOPE Physics Models and Transport Mechanics. Implementation into Geant4

A translation of the PENELOPE physics subroutines to C++, designed as an extension of the GEANT4 toolkit, is presented. The Fortran code system PENELOPE performs Monte Carlo simulation of coupled electron-photon transport in arbitrary materials for a wide energy range, nominally from 50 eV up to 1 GeV. PENELOPE implements the most reliable interaction models that are currently available, limited only by the required generality of the code. In addition, the transport of electrons and positrons is simulated by means of an elaborate class II scheme in which hard interactions (involving deflection angles or energy transfers larger than pre-defined cutoffs) are simulated from the associated restricted differential cross sections. After a brief description of the interaction models adopted for photons and electrons/positrons, we describe the details of the class-II algorithm used for tracking electrons and positrons. The C++ classes are adapted to the specific code structure of GEANT4. They provide a complete description of the interactions and transport mechanics of electrons/positrons and photons in arbitrary materials, which can be activated from the G4ProcessManager to produce simulation results equivalent to those from the original PENELOPE programs. The combined code, named PENG4, benefits from the multi-threading capabilities and advanced geometry and statistical tools of GEANT4.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1972

Microbial burden prediction model for unmanned planetary spacecraft

The technical development of a computer program for predicting microbial burden on unmanned planetary spacecraft is outlined. The discussion includes the derivation of the basic analytical equations, the selection of a method for handling several random variables, the macrologic of the computer programs and the validation and verification of the model. The prediction model was developed to (1) supplement the biological assays of a spacecraft by simulating the microbial accretion during periods when assays are not taken; (2) minimize the necessity for a large number of microbiological assays; and (3) predict the microbial loading on a lander immediately prior to sterilization and other non-lander equipment prior to launch. It is shown that these purposes not only were achieved but also that the prediction results compare favorably to the estimates derived from the direct assays. The computer program can be applied not only as a prediction instrument but also as a management and control tool. The basic logic of the model is shown to have possible applicability to other sequential flow processes, such as food processing.

Hoffman, A. R.↗
NASA NTRS1973

Development and application of vibroacoustic structural data banks in predicting vibration design and test criteria for rocket vehicle structures

A method of predicting broadband random vibration criteria for components on space vehicles is presented. Large amounts of vibration and acoustic data obtained from flights and static firing tests of space vehicle were formulated into vibroacoustic data banks for structural categories of ring frame, skin stringer, and honeycomb. The vibration spectra with their associated acoustic spectra are normalized to a reference acoustic spectrum. The individual normalized spectra are grouped according to definite structural characteristics and statistically analyzed to form the vibroacoustic data banks described in this report. These data banks represent the reference vibration criteria available for determining the new vehicle vibration criteria.

Bandgren, H. J.↗
NASA NTRS1973

New approach to cosmic-ray diffusion theory.

We have investigated a new approach to deriving a diffusion equation for charged particles in a static, random magnetic field. Our method incorporates essential effects of the magnetic fluctuations in the lowest order particle orbits. Significant corrections to the usual quasilinear diffusion coefficient for cosmic rays with pitch angles near 90 deg are a consequence. Monte Carlo results bear out the validity of our theory.

Jones, F. C.↗
NASA NTRS1977

Measurement of bit-error rate

Error rate of RF digital communication link is measured at low signal-to-noise ratios using this technique. In this method, identical pseudo-random sequences of pulses are generated at transmitter and receiver of link. However, comparison of two sequences at receiver involves integration that makes bit-tracking threshold depend on length of pseudo-random sequence, rather than on actual rate of transmission of individual bits.

Fowler, J.↗
NASA NTRS1977

Dual frequency correlation radar measurements of the height statistics of ocean waves

A radar technique has been developed for measuring the statistical height properties of a random rough surface. This method is being applied to the problem of measuring the significant wave height and probability density function of ocean waves from an aircraft or spacecraft. Earlier theoretical and laboratory results have been extended to define the requirements and performance limitations of flight systems. Some details of the current airborne radar system are discussed and results obtained on several experimental missions are presented and interpreted.

Weissman, D. E.↗
NASA NTRS1986

Scattering from nonspherical Chebyshev particles. I - Cross sections, single-scattering albedo, asymmetry factor, and backscattered fraction

Using the extended boundary condition method, scattering calculations from randomly oriented rotationally symmetric nonspherical particles were made, the size parameters (1-25) and the refractive indicies (1.5 0.02i) of which were chosen to relate to the transfer of solar and IR radiation in an aerosol atmosphere. Comparison of computations for 23 Chebyshev shapes to corresponding size-averaged spherical results revealed that nonsphericity increased absorption for size parameters larger than around 10 and decreased the asymmetry factor, and correspondingly increased the backscattered fraction, in the size ranges 8-15. The scattering efficiency was somewhat larger for nonspherical particles, while the single-scattering albedo tended to be smaller, and concavity was shown to almost always enhance spherical-nonspherical differences.

Mugnai, A.↗