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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Out of time order correlation of the Hubbard model with random local disorder

The out-of-time-order correlator (OTOC) serves as a powerful tool for investigating quantum information spreading and chaos in complex systems. We present a method employing non-equilibrium dynamical mean-field theory and coherent potential approximation combined with diagrammatic perturbation on the Schwinger–Keldysh contour to calculate the OTOC for correlated fermionic systems subjected to both random disorder and electron interaction. Furthermore, our key finding is that random disorder enhances the OTOC decay in the Hubbard model for the metallic phase in the weakly interacting limit. However, the current limitation of our perturbative solver restricts the applicability to weak interaction regimes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Online Learning for Commercial Buildings

There has been an increased interest in designing optimization based techniques for the control of building heating, ventilation, and air-conditioning (HVAC) systems for either improving the energy efficiency of buildings or providing ancillary services to the electric grid. The performance of such prediction-based control techniques heavily rely on the model of a building’s thermal dynamics. However, the development of high fidelity building thermal dynamic models is challenging given the presence of large uncertainties that affect thermal loads in buildings such as building envelope performance, thermal mass, internal heat gains as well as occupants' behavior. In this paper, we propose a method to identify both a resistive-capacitive parametric model, and non -parametric load uncertainties using measured input-output data. The parametric model is obtained using semi-parametric regression, whereas the non-parametric part is based on Random Forest, where regression trees are used to derive the dependency of non-parametric terms on both building operation parameters and ambient temperature. The effectiveness of the method is evaluated using experimental data collected from an office building at the Pacific Northwest National Laboratory (PNNL) campus. The proposed methodology was observed to provide improved accuracy over appropriate baseline strategies, when predicting indoor air temperatures.

Dong, Jin↗
DOE OSTI2014

First-order nonadiabatic coupling matrix elements between excited states: A Lagrangian formulation at the CIS, RPA, TD-HF, and TD-DFT levels

Analytic expressions for the first-order nonadiabatic coupling matrix elements between electronically excited states are first formulated exactly via both time-independent equation of motion and time-dependent response theory, and are then approximated at the configuration interaction singles, particle-hole/particle-particle random phase approximation, and time-dependent density functional theory/Hartree-Fock levels of theory. Note that, to get the Pulay terms arising from the derivatives of basis functions, the standard response theory designed for electronic perturbations has to be extended to nuclear derivatives. The results are further recast into a Lagrangian form that is similar to that for excited-state energy gradients and allows to use atomic orbital based direct algorithms for large molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗
DOE OSTI2020

Methods for the robust computation of the long-period seismic spectrum of broad-band arrays

SUMMARY We describe array methods to search for low signal-to-noise ratio (SNR) signals in long-period seismic data using Fourier analysis. This is motivated by published results that find evidence of solar free oscillations in the Earth's seismic hum. Previous work used data from only one station. In this paper, we describe methods for computing spectra from array data. Arrays reduce noise level through averaging and provide redundancy that we use to distinguish coherent signal from a random background. We describe two algorithms for calculating a robust spectrum from seismic arrays, an algorithm that automatically removes impulsive transient signals from data, a jackknife method for estimating the variance of the spectrum, and a method for assessing the significance of an entire spectral band. We show examples of their application to data recorded by the Homestake Mine 3-D array in Lead, SD and the Piñon Flats PY array. These are two of the quietest small aperture arrays ever deployed in North America. The underground Homestake data has exceptionally low noise, and the borehole sensors of the PY array also have very low noise, making these arrays well suited to finding very weak signals. We find that our methods remove transient signals effectively from the data so that even low-SNR signals in the seismic background can be found and tested. Additionally, we find that the jackknife variance estimate is comparable to the noise floor, and we present initial evidence for solar g-modes in our data through the T2 test, a multivariate generalization of Student's t-test.

Caton, Ross C.↗
DOE OSTI2015

Sublinear scaling for time-dependent stochastic density functional theory

A stochastic approach to time-dependent density functional theory is developed for computing the absorption cross section and the random phase approximation (RPA) correlation energy. The core idea of the approach involves time-propagation of a small set of stochastic orbitals which are first projected on the occupied space and then propagated in time according to the time-dependent Kohn-Sham equations. The evolving electron density is exactly represented when the number of random orbitals is infinite, but even a small number (≈16) of such orbitals is enough to obtain meaningful results for absorption spectrum and the RPA correlation energy per electron. We implement the approach for silicon nanocrystals using real-space grids and find that the overall scaling of the algorithm is sublinear with computational time and memory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A finite difference informed random walker (FDiRW) solver for strongly inhomogeneous diffusion problems

In nature, many complex multi-physics coupling problems exhibit strong diffusivity inhomogeneity. For instance, in the context of radionuclide absorption by porous wasteform materials within a flowing waste stream, the difference of species’ diffusivity in solid and liquid phases spans by 3~8 orders of magnitude. To solve the diffusion equations with strongly inhomogeneous diffusivity, traditional discretization-based methods, such as the Finite Difference Method (FDM), require infinitesimally small time steps (<10 -10 ) as high spatial resolutions are employed in most microstructure evolution processes, leading to prohibitively high computational costs. Here, this work developed an integrated numerical approach (FDiRW: Finite Difference informed Random Walk) to tackle this challenge. The idea is that utilizing the Random Walk concept, the fast diffusion is modeled as a superposition of point source’s solution for a concentration distribution while FDM is used to obtain the point source’s solution at each node. A mesh-coarsening algorithm is developed to generate an exclusive coarse mesh for FDiRW approach to maximize its efficiency. The effectiveness of the coarse mesh-based FDiRW approach is validated by benchmarking Finite Difference solutions. Numerical results demonstrated that FDiRW achieves a remarkable 1000x computational efficiency improvement over FDM while preserving desired accuracy for a medium-sized model of 192 × 192 × 192 grids. Finally, as models scale up, a floating-point operations (PLOPs) analysis of the FDiRW algorithm reveals that its computational complexity grows quadratically in terms of the number of nodes employed in computation.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Numerical Methods of Computational Electromagnetics for Complex Inhomogeneous Systems

Understanding electromagnetic phenomena is the key in many scientific investigation and engineering designs such as solar cell designs, studying biological ion channels for diseases, and creating clean fusion energies, among other things. The objectives of the project are to develop high order numerical methods to simulate evanescent electromagnetic waves occurring in plasmon solar cells and biological ion-channels, where local field enhancement within random media in the former and long range electrostatic interactions in the latter are of major challenges for accurate and efficient numerical computations. We have accomplished these objectives by developing high order numerical methods for solving Maxwell equations such as high order finite element basis for discontinuous Galerkin methods, well-conditioned Nedelec edge element method, divergence free finite element basis for MHD, and fast integral equation methods for layered media. These methods can be used to model the complex local field enhancement in plasmon solar cells. On the other hand, to treat long range electrostatic interaction in ion channels, we have developed image charge based method for a hybrid model in combining atomistic electrostatics and continuum Poisson-Boltzmann electrostatics. Such a hybrid model will speed up the molecular dynamics simulation of transport in biological ion-channels.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Role of defects in the process of graphene growth on hexagonal boron nitride from atomic carbon

Hexagonal boron nitride (h-BN) is an attractive substrate for graphene, as the interaction between these materials is weak enough for high carrier mobility to be retained in graphene but strong enough to allow for some epitaxial relationship. We deposited graphene on exfoliated h-BN by molecular beam epitaxy (MBE), we analyzed the atomistic details of the process by ab initio density functional theory (DFT), and we linked the DFT and MBE results by random walk theory. Graphene appears to nucleate around defects in virgin h-BN. The DFT analysis reveals that sticking of carbon to perfect h-BN is strongly reduced by desorption, so that pre-existing seeds are needed for the nucleation. The dominant nucleation seeds are C{sub N}C{sub B} and O{sub N}C{sub N} pairs and B{sub 2}O{sub 3} inclusions in the virgin substrate.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Long-Term Results of a Randomized Trial Comparing Iridium Implant Plus External Beam Radiation Therapy With External Beam Radiation Therapy Alone in Node-Negative Locally Advanced Cancer of the Prostate

Purpose: To determine the impact on long-term survival from the addition of brachytherapy to external beam radiation therapy (EBRT) in patients with prostate cancer. Methods and Materials: Between 1992 and 1997, 104 men with cT2-3, surgically staged node-negative prostate cancer were randomized to receive either EBRT (40 Gy/20 fractions) with iridium implant (35 Gy/48 hours) or EBRT alone (66 Gy/33 fractions) to the prostate. According to T stage, Gleason score, and prostate-specific antigen level, 60% of patients had high-risk disease. Substantial improvements in biochemical control at 8 years have previously been reported. Additional follow-up was collected on deaths and metastases. Results: Median follow-up was 14 years. Five patients were lost to follow-up. All other patients have been followed a minimum of 13 years. There have been 75 deaths, including 21 from prostate cancer and 25 from second cancers. No patients developing a second cancer have died from prostate cancer. There was no difference in overall survival between the 2 treatment groups: 34 deaths (67%) in the implant arm and 41 (77%) in the EBRT arm (hazard ratio [HR] 1.00, 95% confidence interval [CI] 0.63-1.59). Similarly, there was no difference in prostate cancer–specific deaths: 9 (18%) patients in the implant arm compared with 12 (23%) in the EBRT arm (HR 0.79, 95% CI 0.34-1.87). There was no statistically significant difference in the number of patients developing metastatic disease: 10 (20%) in the implant arm and 15 (28%) in the EBRT arm (HR 0.70, 95% CI 0.32-1.57). Improvements in biochemical control were maintained (HR 0.53, 95% CI 0.31-0.88). Conclusions: Despite a dramatic reduction of biochemical recurrence rates, the addition of iridium implant to EBRT did not translate into improved overall survival or prostate cancer–specific survival.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization

Here, we consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from derivative-free simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients using finite differences of stochastic function evaluations within a common random number framework. We develop modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations and provide global convergence results to the neighborhood of a locally optimal solution. We present numerical experiments on simulation optimization problems to illustrate the performance of the proposed algorithm. When compared with classical zeroth-order stochastic gradient methods, we observe that our strategies of adapting the sample sizes significantly improve performance in terms of the number of stochastic function evaluations required.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Augmenting subspace optimization methods with linear bandits

In this work, we consider the framework of methods for unconstrained minimization that are, in each iteration, restricted to a model that is only a valid approximation to the objective function on some affine subspace containing an incumbent point. These methods are of practical interest in computational settings where derivative information is either expensive or impossible to obtain. Recent attention has been paid in the literature to employing randomized matrix sketching for generating the affine subspaces within this framework. We consider a relatively straightforward, deterministic augmentation of such a generic subspace optimization method. In particular, we consider a sequential optimization framework where actions consist of one-dimensional linear subspaces and rewards consist of (approximations to) the magnitudes of directional derivatives computed in the direction of the action subspace. Reward maximization in this context is consistent with maximizing lower bounds on descent guaranteed by first-order Taylor models. This sequential optimization problem can be analysed through the lens of dynamic regret. We modify an existing linear upper confidence bound (UCB) bandit method and prove sublinear dynamic regret in the subspace optimization setting. We demonstrate the efficacy of employing this linear UCB method in a setting where forward-mode algorithmic differentiation can provide directional derivatives in arbitrary directions and in a derivative-free setting. For the derivative-free setting, we propose SS-POUNDers, an extension of the derivative-free optimization method POUNDers that employs the linear UCB mechanism to identify promising subspaces. Our numerical experiments suggest a preference, in either computational setting, for employing a linear UCB mechanism within a subspace optimization method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

A search for correlation of ultra-high energy cosmic rays with IRAS-PSCz and 2MASS-6dF galaxies

We study the arrival directions of 69 ultra-high energy cosmic rays (UHECRs) observed at the Pierre Auger Observatory (PAO) with energies exceeding 55 EeV. We investigate whether the UHECRs exhibit the anisotropy signal expected if the primary particles are protons that originate in galaxies in the local universe, or in sources correlated with these galaxies. We cross-correlate the UHECR arrival directions with the positions of IRAS-PSCz and 2MASS-6dF galaxies taking into account particle energy losses during propagation. This is the first time that the 6dF survey is used in a search for the sources of UHECRs and the first time that the PSCz survey is used with the full 69 PAO events. The observed cross-correlation signal is larger for the PAO UHECRs than for 94% (98%) of realisations from an isotropic distribution when cross-correlated with the PSCz (6dF). On the other hand the observed cross-correlation signal is lower than that expected from ∼>85% of realisations, had the UHECRs originated in galaxies in either survey. The observed cross-correlation signal does exceed that expected by 50% of the realisations if the UHECRs are randomly deflected by intervening magnetic fields by 5° or more. We propose a new method of analysing the expected anisotropy signal, by dividing the predicted UHECR source distribution into equal predicted flux radial shells, which can help localise and constrain the properties of UHECR sources. We find that the 69 PAO events are consistent with isotropy in the nearest of three shells we define, whereas there is weak evidence for correlation with the predicted source distribution in the two more distant shells in which the galaxy distribution is less anisotropic.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies

Purpose: To develop and evaluate a fast and simple tool called dPETSTEP (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images. Methods: The tool was developed in MATLAB using both new and previously reported modules of PETSTEP (PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS). Results: dPETSTEP was 8000 times faster than MC. Dynamic images from dPETSTEP had a root mean square error that was within 4% on average of that of MC images, whereas the GAUSS images were within 11%. The average bias in dPETSTEP and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dPETSTEP images conformed well to MC images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dPETSTEP images and noise properties agreed better with MC. Conclusions: The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC images, in a fraction of the time. They believe dPETSTEP to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dPETSTEP can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Evaluating the Interplay between Trajectory Segmentation and Mode Inference Error

Travel behavior changes are essential to transportation decarbonization. Travel diaries, consisting of sequences of trips between places, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of Global Positioning System tracking surveys. These algorithms have typically been validated using prompted recall of presegmented trips, thus disregarding the continuity of mode inference. Phone operating systems have adopted battery-conserving techniques, but the resulting data collection errors have not been studied extensively. We introduce a framework to evaluate the accuracy of trip length computations and mode inference by analyzing continuous mode-segmented trajectories for groups of trips. We then use the framework to identify the input data quality and the impact of postprocessing. Our primary inputs to this evaluation are MobilityNet, a public dataset containing information from three artificial timelines covering 15 different travel modes, and sample open-source travel diary creation algorithms from the OpenPATH project. Our framework concretely shows that the variance of the distance error drops from (0.217, 0.0848) to (o.011, 0.0407) (Android, iOS) after postprocessing. Similarly, the weighted F-scores for mode inference increase from (0.25, 0.29) to (0.60, 0.74) (iOS, Android) between random forest and geographic information system-based models. We hope that this standardized method will be adapted to evaluate other, potentially proprietary, travel diary algorithms. Finally, the results can be used to understand and improve the state of the art in the travel diary creation field.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

PET/CT in brachytherapy early response evaluation of pancreatic ductal adenocarcinoma xenografts: comparison with apparent diffusion coefficient from diffusion-weighted MR imaging

Objective: To evaluate the feasibility of using PET/CT and diffusion-weighted magnetic resonance imaging (DW-MRI) to monitor the early response of pancreatic ductal adenocarcinoma (PDAC) xenografts to brachytherapy, and to determine whether maximum standardized uptake value (SUVmax) correlate with apparent diffusion coefficient (ADC). Materials and Methods: SW1990 human PDAC were subcutaneously implanted in 20 nude mice. They were randomly divided into 125-Iodine ({sup 125}I) seeds and blank seeds group. PET/CT and DW-MRI were performed at pretreatment and 5 days after therapy. SUVmax and ADC values were calculated, respectively. The correlation between SUVmax and ADC values was analyzed by the Pearson correlation test. Results: The SUVmax were significantly decreased between pretreatment and 5 days after {sup 125}I seeds treatment (p < 0.001) and between two groups (p < 0.001). And the ADC values were significantly increased between pretreatment and 5 days after {sup 125}I seeds treatment (p < 0.001) and between two groups (p < 0.001). While in the bank seeds group, there were no significantly difference between pretreatment and after treatment in SUVmax and ADC values (p = 0.057; p = 0.397). SUVmax and ADC correlated significantly and negatively before treatment in both groups (r = − 0.964, R{sup 2} = 0.929, p < 0.001; r = − 0.917, R{sup 2} = 0.841, p < 0.001) and after treatment in the blank seeds group (r = − 0.944, R{sup 2} = 0.891, p < 0.001). But after {sup 125}I seeds treatment there was no significant correlation between SUVmax and ADC (r = − 0.388, R{sup 2} = 0.151, p = 0.268). Conclusion: The PET/CT and DW-MRI are capable of monitoring the early response of PDAC xenografts to brachytherapy. The significantly inverse correlation between pretreatment SUVmax and ADC suggests that PET/CT and DW-MRI might play complementary roles for therapy assessment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Interventional Radiology of Male Varicocele: Current Status

Varicocele is a fairly common condition in male individuals. Although a minor disease, it may cause infertility and testicular pain. Consequently, it has high health and social impact. Here we review the current status of interventional radiology of male varicocele. We describe the radiological anatomy of gonadal veins and the clinical aspects of male varicocele, particularly the physical examination, which includes a new clinical and ultrasound Doppler maneuver. The surgical and radiological treatment options are also described with the focus on retrograde and antegrade sclerotherapy, together with our long experience with these procedures. Last, we compare the outcomes, recurrence and persistence rates, complications, procedure time and cost-effectiveness of each method. It clearly emerges from this analysis that there is a need for randomized multicentre trials designed to compare the various surgical and percutaneous techniques, all of which are aimed at occlusion of the anterior pampiniform plexus.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Antagonistic effect of disulfide-rich peptide aptamers selected by cDNA display on interleukin-6-dependent cell proliferation

Highlights: Black-Right-Pointing-Pointer Disulfide-rich peptide aptamer inhibits IL-6-dependent cell proliferation. Black-Right-Pointing-Pointer Disulfide bond of peptide aptamer is essential for its affinity to IL-6R. Black-Right-Pointing-Pointer Inhibitory effect of peptide depends on number and pattern of its disulfide bonds. -- Abstract: Several engineered protein scaffolds have been developed recently to circumvent particular disadvantages of antibodies such as their large size and complex composition, low stability, and high production costs. We previously identified peptide aptamers containing one or two disulfide-bonds as an alternative ligand to the interleukin-6 receptor (IL-6R). Peptide aptamers (32 amino acids in length) were screened from a random peptide library by in vitro peptide selection using the evolutionary molecular engineering method 'cDNA display'. In this report, the antagonistic activity of the peptide aptamers were examined by an in vitro competition enzyme-linked immunosorbent assay (ELISA) and an IL-6-dependent cell proliferation assay. The results revealed that a disulfide-rich peptide aptamer inhibited IL-6-dependent cell proliferation with similar efficacy to an anti-IL-6R monoclonal antibody.

60 APPLIED LIFE SCIENCES↗