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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Improved subseasonal-to-seasonal precipitation prediction of climate models with nudging approach for better initialization of Tibetan Plateau-Rocky Mountain Circumglobal wave train and land surface conditions

Abstract Reliable subseasonal-to-seasonal (S2S) precipitation prediction is highly desired due to the great socioeconomical implications, yet it remains one of the most challenging topics in the weather/climate prediction research area. As part of the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) project of the Global Energy and Water Exchanges (GEWEX) program, twenty-one climate models follow the LS4P protocol to quantify the impact of the Tibetan Plateau (TP) land surface temperature/subsurface temperature (LST/SUBT) springtime anomalies on the global summertime precipitation. We find that nudging towards reanalysis winds is crucial for climate models to generate atmosphere and land surface initial conditions close to observations, which is necessary for meaningful S2S applications. Simulations with nudged initial conditions can better capture the summer precipitation responses to the imposed TP LST/SUBT spring anomalies at hotspot regions all over the world. Further analyses show that the enhanced S2S prediction skill is largely attributable to the substantially improved initialization of the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train pattern in the atmosphere. This study highlights the important role that initial condition plays in the S2S prediction and suggests that data assimilation technique (e.g., nudging) should be adopted to initialize climate models to improve their S2S prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A tensor train-based isogeometric solver for large-scale 3D poisson problems

We introduce a three-dimensional (3D), fully tensor train (TT) assembled isogeometric analysis (IGA) framework, TT-IGA, for solving partial differential equations (PDEs). Our method reformulates IGA discrete operators into TT format, enabling efficient compression and computation. Geometry evaluations use the original NURBS description at sampling points and TT approximation is applied to geometry-derived coefficient fields and discrete operators. We demonstrate the effectiveness of the proposed TT-IGA framework on the three-dimensional Poisson equation, achieving substantial reductions in memory and computational cost without compromising solution quality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Mentorship Programs in Radiation Oncology Residency Training Programs: A Critical Unmet Need

Purpose: To conduct a nationwide survey to evaluate the current status of resident mentorship in radiation oncology. Methods and Materials: An anonymous electronic questionnaire was sent to all residents and recent graduates at US Accreditation Council for Graduate Medical Education–accredited radiation oncology residency programs, identified in the member directory of the Association of Residents in Radiation Oncology. Factors predictive of having a mentor and satisfaction with the mentorship experience were identified using univariate and multivariate analyses. Results: The survey response rate was 25%, with 85% of respondents reporting that mentorship plays a critical role in residency training, whereas only 53% had a current mentor. Larger programs (≥10 faculty, P=.004; and ≥10 residents, P<.001) were more likely to offer a formal mentorship program, which makes it more likely for residents to have an active mentor (88% vs 44%). Residents in a formal mentoring program reported being more satisfied with the overall mentorship experience (univariate odds ratio 8.77, P<.001; multivariate odds ratio 5, P<.001). On multivariate analysis, women were less likely to be satisfied with the mentorship experience. Conclusions: This is the first survey focusing on the status of residency mentorship in radiation oncology. Our survey highlights the unmet need for mentorship in residency programs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu] 2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu] 2+ -catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Outage Analysis for Cooperative mmWave UAV Communications With Beam Training Overhead

In this letter, we investigate the tradeoff between reliability and beam training overhead in cooperative millimeter wave (mmWave) unmanned aerial vehicle (UAV) communications. In particular, we analyze how outage probability varies with the number of neighboring UAVs being probed as potential relays for relay selection. Unlike existing work, the limited number of UAV beams as well as UAV orientations are explicitly considered in our analysis. Here we derive the outage probability in closed form and verify our analysis via simulations. The results facilitate us to understand the fundamental limits of cooperative mmWave UAV communications.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher-dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. Additionally, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.

42 ENGINEERING↗
DOE OSTI2016

SU-F-E-18: Training Monthly QA of Medical Accelerators: Illustrated Instructions for Self-Learning

Purpose: To develop and test clear illustrated instructions for training of monthly mechanical QA of medical linear accelerators. Methods: Illustrated instructions were created for monthly mechanical QA with tolerance tabulated, and underwent several steps of review and refinement. Testers with zero QA experience were then recruited from our radiotherapy department (1 student, 2 computational scientists and 8 dosimetrists). The following parameters were progressively de-calibrated on a Varian C-series linac: Group A = gantry angle, ceiling laser position, X1 jaw position, couch longitudinal position, physical graticule position (5 testers); Group B = Group A + wall laser position, couch lateral and vertical position, collimator angle (3 testers); Group C = Group B + couch angle, wall laser angle, and optical distance indicator (3 testers). Testers were taught how to use the linac, and then used the instructions to try to identify these errors. A physicist observed each session, giving support on machine operation, as necessary. The instructions were further tested with groups of therapists, graduate students and physics residents at multiple institutions. We have also changed the language of the instructions to simulate using the instructions with non-English speakers. Results: Testers were able to follow the instructions. They determined gantry, collimator and couch angle errors within 0.4, 0.3, and 0.9degrees of the actual changed values, respectively. Laser positions were determined within 1mm, and jaw positions within 2mm. Couch position errors were determined within 2 and 3mm for lateral/longitudinal and vertical errors, respectively. Accessory positioning errors were determined within 1mm. ODI errors were determined within 2mm when comparing with distance sticks, and 6mm when using blocks, indicating that distance sticks should be the preferred approach for inexperienced staff. Conclusion: Inexperienced users were able to follow these instructions, and catch errors within the criteria suggested by AAPM TG142 for linacs used for IMRT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-FG-201-04: Cloud-Based Collaboration for Radiotherapy Clinical Trials, Research and Training

Many low- and middle-income countries lack the resources and services to manage cancer, from screening and diagnosis to radiation therapy planning, treatment and quality assurance. The challenges in upgrading or introducing the needed services are enormous, and include severe shortages in equipment and trained staff. In this symposium, we will describe examples of technology and scientific research that have the potential to impact all these areas. These include: (1) the development of high-quality/low-cost colposcopes for cervical cancer screening, (2) the application of automated radiotherapy treatment planning to reduce staffing shortages, (3) the development of a novel radiotherapy treatment unit, and (4) utilizing a cloud-based infrastructure to facilitate collaboration and QA. Learning Objectives: Understand some of the issues in cancer care in low- resource environments, including shortages in staff and equipment, and inadequate physical infrastructure for advanced radiotherapy. Understand the challenges in developing and deploying diagnostic and treatment devices and services for low-resource environments. Understand some of the emerging technological solutions for cancer management in LMICs. NCI; L. Court, NIH, Varian, Elekta; I. Feain, Ilana Feain is founder and CTO of Nano-X Pty Ltd.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Microbiome dynamics in the congregate environment of U.S. Army Infantry training

Within military training and operational environments, individuals from diverse backgrounds share common spaces, follow structured routines and diets, and engage in physically demanding tasks. While there has been interest in leveraging microbiome features to predict and improve military health and performance, the longitudinal convergence of microbiomes in such constrained environments has not been established. To assess the degree of microbiome convergence, we performed shotgun metagenomic sequencing on swab samples from a military trainee cohort. Samples were taken across four different body sites, three timepoints, and two spatially distinct platoons. We observed evidence of convergence in one platoon, whereby similarity in microbiome composition increased over time, with numerous differentially abundant species. We found no indication of strain transfer between individuals, suggesting that convergence was influenced by external environmental factors, diet, and lifestyle. Microbial shifts observed in the convergence process included a decrease in fungal species, such as Malassezia restricta in nasal cavities, and a decrease in Prevotella species at inguinal regions across time. Shifts in multiple Corynebacterium species were also observed with varying magnitudes depending on the body site. Overall, we provide preliminary evidence of convergence of host microbial communities in military-associated environments that were distinguishable using shotgun metagenomic sequencing approaches. The data presented here on microbiome convergence, dynamics, and stability may inform risk-based mitigation in congregate military settings facilitating development of targeted microbial, dietary, or other interventions to optimize health and performance of military populations.

Biological and medical sciences↗
DOE OSTI2023

Parallel Algorithms for Computing the Tensor-Train Decomposition

The tensor-train (TT) decomposition expresses a tensor in a data-sparse format used in molecular simulations, high-order correlation functions, and optimization. In this paper, we propose four parallelizable algorithms that compute the TT format from various tensor inputs: (1) Parallel-TTSVD for traditional format, (2) PSTT and its variants for streaming data, (3) Tucker2TT for Tucker format, and (4) TT-fADI for solutions of Sylvester tensor equations. We provide theoretical guarantees of accuracy, parallelization methods, scaling analysis, and numerical results. For example, for a d-dimension tensor in $\mathbb{R}$ $n\times∙∙∙$$\times$$n$ a two-sided sketching algorithm PSTT2 is shown to have a memory complexity of $O(n^{[d/2]})$, improving upon $O(n^{d—1})$ from previous algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Integrated research training program of excellence in radiochemistry

The overall goal of this “Integrated Research Training Program of Excellence in Radiochemistry” is to provide a rich and deep research experience in state-of-the-art radiochemistry and in the fundamentals of radioisotopic labeling and tracer methodology to develop researchers who are capable of meeting the challenges of designing and preparing radiotracers of broad applicability for monitoring and imaging diverse biological systems and environmental processes. This program was based in the Departments of Radiology and Radiation Oncology at Washington University Medical School and the Department of Chemistry at the University of Illinois at Urbana Champaign, and it was initially directed by Professor Michael J. Welch as Principal Investigator. After his passing in 2012, the program was led by Professor Suzanne E. Lapi. Programmatic content and participant progress was overseen by an Internal Advisory Committee of senior investigators consisting of the PIs, Professor Mach from the Department of Radiology at Washington University and Professor John A. Katzenellenbogen of the Department of Chemistry at the University of Illinois. A small External Advisory Committee to give overall program guidance was also constituted of experts in radiolabeled compounds and in their applications in environmental and plant science.

18F↗
DOE OSTI2016

Research Projects for Interrogations of Biological Systems: Training for the Development of Novel Radiotracers

This grant was situated at the University of Missouri to train Ph.D. scientists in radiochemistry and synthetic chemistry in conjunction with Faculty from the Interdisciplinary Plant Group, Division of Biological Sciences, the MU Research Reactor Center, Molecular Biology and the Radiopharmaceutical Sciences Institute. This project was collaborative with Brookhaven National Laboratory (Richard Ferrieri, PI). Projects for the Ph.D. candidates included novel probe development for peptides, nucleosides, small molecules or radiometals, the direct use of radiometals as probes, or nuclear techniques for analysis. The projects for the postdoctoral fellow involved synthetic chemistry for the preparation of precursors for novel tracers that will be radiolabeled with 18 F or other appropriate radionuclides. The skill sets of our team members allowed us to prepare probes with positron or single photon emitters, as well as ones that are dual-labeled (fluorescent and radiolabeled). We focused our technical advances to those that will be broadly applicable to any research field.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2018

Filling Critical Gaps Through Innovative Cradle-to-Grave Training Final Report

This project seeks to enhance and provide training on alternative fuels and alternative fuel vehicles to reach mechanics/technicians, first responders, public safety officials, and other critical service providers across the multi-state region of Arkansas, Louisiana, Oklahoma, and Texas

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Conduct of Training P781-1

This document establishes Los Alamos National Laboratory (LANL or the Laboratory) training program management requirements that meet regulatory drivers and contractual requirements. The implementation of the processes defined in this document assures workers have the knowledge, skills, and abilities to perform their assigned duties efficiently, safely, securely, and compliantly.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Dose Modeling for Harborview Research and Training Facility

The Harborview Research and Training (R&T) Facility at the University of Washington (UW) was accidently contaminated from a breached sealed source of Cesium-137. The release occurred during removal of the sealed source from its holder within a portable hot cell in the loading dock area near room 220. The loading dock and northeast corner of the building above the ground floor were impacted by the release, whereas the remaining portions of the R&T Facility were minimally affected. Detailed descriptions of the R&T Facility and the affected area/rooms are included in Section 2. The RESRAD Team in the Environmental Science Division of Argonne National Laboratory visited the site on August 12, 2019, and interviewed the R&T Facility building manager and staff working in the building at that time. Site- and building-specific data were collected including building floor plan CAD drawings, building occupants’ working hours, contaminated rooms photos and their dimensions, building air exchange rates, building materials and “curtain wall” details, and so forth. These data are being used in the dose modeling for potential occupants of the building after it is remediated. The dose modeling was performed using the RESRAD-BUILD computer code, which is designed for the evaluation of radiologically contaminated buildings (Yu et al. 2003). Two hypothetical building occupants of the Harborview R&T Facility were evaluated: a researcher occupying room 726 (a research laboratory on the seventh floor) and a custodian occupying room 220 (loading dock and trash compactor room with a rollup door). A maintenance worker that will be spending less time in impacted areas was not modeled because it will be bounded by other two hypothetical building occupants modeled. A summary description of the RESRAD-BUILD is provided in Appendix A, and the modeling of potential doses to a researcher and a custodian is described in detail in Section 3. A summary and a discussion of the dose modeling results are included in Section 4. The parameters used in the RESRAD-BUILD modeling are included in Appendix B. This report documents the dose modeling results; it does not describe measurements of the actual radioactivity levels at the Harborview R&T Facility, nor does it set any expectation for the actual final radioactivity levels. The radiation doses presented are modeling results based on the generic screening values of Cs-137 and several conservative assumptions and are meant only to assist in decision making regarding decontamination of the Harborview R&T Facility.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

General Employee Radiological Refresher Training (Self-Study 47968)

This General Employee Radiological Training (GERT) course is designed to refresh knowledge of basic radiological principles and terms, the risks of exposure to ionizing radiation, LANL radiological hazards and postings, ALARA tools, emergency response responsibilities, and how to obtain personal radiological dose records.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

MFVI Energy Efficiency Audit Training - Module 1.1: Introduction to Efficient Lighting [Slides]

This guide is designed to help trained energy efficiency professionals conduct an energy efficiency audit for commercial and industrial buildings, particularly for micro-, small-, and medium-sized businesses in Mexico. This guide is focused on auditing lighting systems. As such, it will assist its user in determining the optimal lighting for a given space and then identifying the most efficient means of delivering that lighting.

21CPP↗