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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Comprehensive Physical Activity Assessment During U.S. Army Basic Combat Training

Abstract Alemany, JA, Pierce, JR, Bornstein, DB, Grier, TL, Jones, BH, and Glover, SH. Comprehensive physical activity assessment during U.S. Army Basic Combat Training. J Strength Cond Res 36(12): 3505–3512, 2022—Physical activity (PA) volume, intensity, and qualitative contextual information regarding activity type and loads carried are limited during U.S. Army Basic Combat Training (BCT). The purpose of this study was to characterize daily (05:00–20:00 hours) PA during BCT using a comprehensive approach. During 2 10-week BCT cycles ( n = 40 trainees per cycle), pedometers, accelerometers, and direct observation were used to estimate daily step count, PA volume, and intensity. Physical activity intensity was categorized by metabolic equivalents (METs) such as “sedentary” (1–2 METs), “light” (2–3 METs), “moderate” (3–6 METs), or “vigorous” (≥6 METs). Daily PA data were analyzed longitudinally using linear mixed models, with significance set at p ≤ 0.05. The mean daily step count was 13,459 ± 4,376 steps, and the mean daily accelerometer-assessed PA volume and intensity were as follows: sedentary: 505 ± 98 minutes, light: 190 ± 78 minutes, moderate: 168 ± 51 minutes, and vigorous: 14 ± 14 minutes, with no differences between cycles for all measures ( p > 0.50). Cumulative time on feet (∼50%) and sitting (20–25%) accounted for most daily activity types during both cycles. Trainees, on average, carried between 3 and 9 kg, and ≥9 kg, for 60% and 10% of the monitored day, respectively. Basic Combat Training's physical demands are high, where trainees achieved 1.7 to 2.7 times greater daily ambulation and 6 times the recommended weekly moderate-to-vigorous PA compared with civilian counterparts and performed weight-bearing load carriage for nearly half of the day. Basic Combat Training-associated PA may increase injury risk among trainees unaccustomed to arduous PA and exercise. Implementing national PA policies to improve physical fitness and facilitate acclimatization to BCT's high physical demands could reduce public health burdens and military nonreadiness.

Sport Sciences↗
DOE OSTI2023

Improving the Estimation of the Atmospheric Water Vapor Pressure Using Interpretable Long Short-Term Memory Networks: Dataset, Python code, and trained models

Atmospheric water vapor pressure is an essential meteorological control on land surface and hydrologic processes. It is not as frequently observed as other meteorologic conditions, but often inferred through the August–Roche–Magnus formula by simply assuming dew point and daily minimum temperatures are equivalent or by empirically correlating the two temperatures using an aridity correction. The performance of both methods varies considerably across different regions and during different time periods; obtaining consistently accurate estimates across space and time remains a great challenge. We applied an interpretable Long Short-Term Memory (iLSTM) network conditioned on static, location specific attributes to estimate daily vapor pressure for 83 FLUXNET sites in the United States and Canada. This data package includes all raw data of the 83 FLUXNET sites, input data for model training/validation/test, trained models and results, and python codes for the manuscript "Improving the Estimation of the Atmospheric Water Vapor Pressure Using an Interpretable Long Short-term Memory Network". Specifically, it consists of five parts. - First, "1_Daymet_data_83sites.zip" includes raw data downloaded from Daymet for the 83 sites used in the paper according to their longitude and latitude, in which vapor pressure is used. It also includes a pre-processed CSV data file combining all data from the 83 sites which is specifically used for the paper. - Second, "2_Fluxnet2015_data_83sites.zip" includes raw half hourly data of the 83 sites downloaded from FLUXNET2015 data portal, pre-processed daily data of the 83 sites, a CSV file including combined pre-processed daily data of the 83 sites, and a CSV file including the information (site ID, site name, latitude, longitude, data available period) of the 83 sites. - Third, "3_MODIS_LAI_data_83sites_raw.zip" includes raw leaf area index (LAI) data downloaded from the AppEEARs data portal. - Fourth, "4_Scripts.zip" includes all scripts related to model training and post-processing of a trained model, and a jupyter notebook showing an example for model post-processing. Two typo errors in files titled "run2get_args.py" and "postprocess.py" were corrected on March 27, 2024 to avoid confusions. - Finally, "Trained_models_and_results.zip" includes three folders and three files with suffix ".npy", and each folder corresponds to one file with suffix ".npy" with the same title. Each of the three folders include all trained models associated with one iLSTM model configuration (35 models for each configuration, details are described in the paper). Each file with suffix ".npy" includes the post-processed results of the corresponding 35 models under one iLSTM model configuration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Reducing Frequency Bias of Fourier Neural Operators in 3D Seismic Wavefield Simulations Through Multistage Training

The recent development of neural operator (NeurOp) learning for solutions to the elastic wave equation shows promising results and provides the basis for fast large-scale simulations for different seismological applications. In this article, we use the Fourier neural operator (FNO) model to directly solve the 3D Helmholtz wave equation for fast seismic ground-motion simulations on different frequencies and show the frequency bias of the FNO model, that is, it learns the lower frequencies better comparing to the higher frequencies. To reduce the frequency bias, we adopt the multistage FNO training, that is, after training a stage 1 FNO model for estimating the ground motion, we use a second FNO model as the stage 2 to learn from the residual, which greatly reduced the errors on the higher frequencies. By adopting this multistage training, the FNO models show reduced biases on higher frequencies, which enhanced the overall results of the ground-motion simulations. Thus the multistage training FNO improves the accuracy and realism of the ground-motion simulations.

earthquakes↗
DOE OSTI2018

Training and Certification Programs on Industrial Energy Systems in the United States

The major industrial energy systems commonly found in manufacturing facilities are compressed air, ammonia refrigeration, chilled water, steam, process heating, combined heat and power, pumps, and fans. The efficiency of these systems contributes significantly to the whole-facility energy performance. On the national and even the international level, well-structured training and highly recognized certification programs help develop a highly skilled and qualified workforce to maintain and improve facilities' energy performance, particularly as technologies within these systems become more and more advanced. The purpose of this paper is to review currently available vendor-agnostic training and certification programs focusing on these systems and to identify the gap between market needs and currently available programs. Its three major conclusions are (1) most training programs focus on operations, maintenance, safety, and design although some briefly touch on energy performance aspects. (2) The Certified Refrigeration Energy Specialist certification by RETA the and Pump System Assessment Professional certification by HI were the only certifications found to be tool-agnostics and emphasize knowledge and skills for improving and maintaining systems energy performance. (3) Developing energy efficiency focused training and ANSI accredited certification programs for industrial energy systems will fill the gap between market needs and currently available programs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Royal Medical Services Training Centre Assessment

The following document represents the joint SNL/IBCTR and HDR Team's Training Centre needs assessment for the Jordan Royal Medical Service (RMS) at the King Hussein Medical Centre (KHMC) and should be used as follows: 1) To present options for future facility improvements. 2) In support of obtaining additional funding for the facility and finalization of a plan for equipment, human resource development, and technical assistance. 3) as a platform to guide future considerations to provide training centre facilities in support of the Biorisk Management (BRM) training and other training to compliment RMS capabilities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

RCT Continuing Training 1st Quarter 2022 Presentation Transcript

Welcome to RCT Continuing Training. This training will discuss the process for: collecting and evaluating radiological air samples and submitting samples to HPAL. This training consists of viewing the online presentation and completing its associated exercise guide, Utrain number 53850. It is recommended that you have the exercise guide with you while following along with the online training

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Orientation to NPHR: Scripts for Training

The IAEA is training staff to train member states to use the Nuclear Power Human Resources (NPHR) model, developed at LANL, to examine their human resource plans. Part of the NPHR training involves orienting users to the files used by NPHR, namely the data file and the model files. To assist the IAEA trainers, the scripts in this document were developed for conducting the orientation sections. The scripts are intended to be used as resources for the trainers to develop their ability to deliver the orientation and not as scripts to be read to a training workshop. The scripts are accompanied by video recordings to illustrate what the trainers will show participants during the orientation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Summary of NDC Capacity Building Workshop and Regional Seismic Travel Time (RSTT) in combination with Data Sharing and Integration Training

The NNSA Seismic Cooperation Program (SCP) sponsored Stephen Myers (LLNL), Michael Begnaud (LANL), Brian Young (SNL) and Istvan Bondar (Research Center for Astronomy and Earth Sciences, Hungary) to serve as a presenters/trainers at the “NDC Capacity Building Workshop and Regional Seismic Travel Time (RSTT) in combination with Data Sharing and Integration Training” September 4-8 2022 in Muscat, Oman (See Appendix A for the agenda). The workshop and training (workshop from here forward) was organized by the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) Provisional Technical Secretariat (PTS). The first half of the week was devoted to NDC workshop activities, and the second half was devoted to RSTT training. Fifty-five participants from 27 countries and the CTBTO-PTS attended the 5-day workshop (See Appendix B for list of participants and countries of origin). Presentations from the PTS described the International Monitoring System (IMS), International Data Centre (IDC) products, and metrics of regional data utilization. Contributed presentations from each country’s scientists included descriptions of regional and national networks, methods of data analysis, and needs for material and technical assistance. Training included an overview of the RSTT method and instruction on how to locate seismic events with the iLoc program, which utilizes RSTT travel times to reduce bias in event location estimates. Methods of seismic tomography and the need for a high-quality tomographic set, including seismological “ground truth”, were emphasized. Seismological “ground truth” or “GT” is a term that has come to mean both events with known location and events with well-characterized locations that are estimated using seismological data, typically with epicenter accuracy of 5 km or better. Notably, the instructional platform has migrated from UNIX shell scripts to Jupyter Notebooks. Jupyter Notebooks have the advantage being more visually intuitive, including display of graphics within the notebook. Each notebook includes every processing step that participants need to reproduce the entire exercise.

58 GEOSCIENCES↗
DOE OSTI2023

Virtual Community Trains

When access to transit solutions is restricted to parts of a community, a division occurs what leads to disadvantaged socioeconomic conditions that only grows over time. This restriction causes difficulties in accessing needed resources such as medical facilities, employment, healthy food, education, healthy food, and more. To solve this restricted access, Labyrinth Smart Mobility and partners are developing a virtual community train that encourages scalability, energy efficiency, and equitability to all regardless of individual circumstances and limitation. The implementation of virtual community trains allows access to resources previously out of reach. The virtual community train enables one operator to drive a lead vehicle. This lead vehicle would then provide direction to a follow vehicle through vehicle-to-vehicle communication. Labyrinth Smart Mobility in conjunction with partners used various methods and resources to prove market feasibility, technical feasibility, economic need and viability, and more as well as design system requirements and architecture emphasis efficiency and safety. In doing so, Labyrinth Smart Mobility and partners will continue these advancements to further develop the virtual community train.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Effect of CLIQ on training of HL-LHC quadrupole magnets

The high-luminosity LHC upgrade requires stronger than LHC low-beta magnets to reach the luminosity goals of the project. The project is well advanced and HL-LHC quadrupole magnets are currently being commissioned in US Labs (MQXFA magnets) and CERN (MQXFB magnets). Those are the first Nb3Sn magnets to be used in any large particle accelerator. At development stages, many Nb3Sn accelerator sub-scale models showed relatively slow training and MQXFA magnets were projected to have low tens of quenches before reaching operational field. Recently it was shown that dedicated capacitor-based devices can affect Nb3Sn magnet training, and it was suggested that CLIQ, a capacitor-based device intended for quench protection, can do too. The present paper investigates effects on training induced by CLIQ, using the base fact that only half the coils in a quadrupole experience upward current modulation because of the capacitor discharge. The study encompasses all MQXFA production magnets trained at BNL to date. No other high-statistics data from identical magnets (series) with CLIQ protection exists so far. Implications and opportunities stemming from data analysis are discussed and conclusions drawn.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Radiological Worker II Training: Course #20301 [Slides]

Radiological worker training is the basic building block for any additional radiological training you may receive. Upon completing radiological worker training, you will have the basic knowledge needed to work safely, using proper radiological practices, in areas where radiological hazards exist. You will also have a better understanding of the hazards and responsibilities associated with radiological work, to help prevent the carelessness that can occur when working continually with or around radioactive material. This course does not qualify you for any specific radiological work. You may be required to take additional training at individual facilities to address facility and job specific hazards and procedures.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Building Monitoring and Energy Efficiency Training for Small and Mid-Sized Commercial Buildings in Non-Urbanized Areas of Alaska

The goal of this project was to create an effective training that increases understanding, competency and capacity in monitoring critical building data. Mentorship, verification and follow-up ensured participants now have necessary skills to install and maintain building monitoring systems. This training sought to empower building managers and maintenance staff to properly and efficiently operate their buildings for energy savings and increased lifespan of equipment. Through the project, participants’ knowledge of the building was increased through hands-on training using a familiar building environment and eliminating common barriers to effective training through peer-to peer delivery. In successful cases the host organization will save money and resources due to more efficient operation of building equipment already in place.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains

Natural language processing (NLP) tasks (text classification, named entity recognition, etc.) have seen amazing improvements over the last few years. This is due to models such as BERT that achieve deep knowledge transfer by using a large pre-trained model, then fine-tuning the model on specific tasks. The BERT architecture has shown even better performance on domain-specific tasks when the model is pre-trained using domain-relevant texts. Here, inspired by these recent advancements, we have developed NukeLM, a nuclear-domain BERT model pre-trained on 1.5 million abstracts from the DOE Office of Scientific and Technical Information (OSTI) database. This NukeLM model is then fine-tuned for the classification of research articles into either binary classes (related to the nuclear fuel cycle (NFC) or not) or multiple categories related to the subject of the article. We show that continued pre-training of a BERT-style architecture prior to fine-tuning results in greater performance in both article classification tasks. This information is critical for properly triaging manuscripts, a necessary task for better understanding citation networks that publish in the nuclear space and uncovering new areas of research in the nuclear (or nuclear relevant) domain.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Improving the Accuracy of Nearest-Neighbor Classification Using Principled Construction and Stochastic Sampling of Training-Set Centroids

A conceptually simple way to classify images is to directly compare test-set data and training-set data. The accuracy of this approach is limited by the method of comparison used, and by the extent to which the training-set data cover configuration space. Here we show that this coverage can be substantially increased using coarse-graining (replacing groups of images by their centroids) and stochastic sampling (using distinct sets of centroids in combination). We use the MNIST and Fashion-MNIST data sets to show that a principled coarse-graining algorithm can convert training images into fewer image centroids without loss of accuracy of classification of test-set images by nearest-neighbor classification. Distinct batches of centroids can be used in combination as a means of stochastically sampling configuration space, and can classify test-set data more accurately than can the unaltered training set. On the MNIST and Fashion-MNIST data sets this approach converts nearest-neighbor classification from a mid-ranking- to an upper-ranking member of the set of classical machine-learning techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The Evaluation of Machine Learning Techniques for Isotope Identification Contextualized by Training and Testing Spectral Similarity

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset and evaluated on twelve other datasets with varying standoff distances, shielding, and background configurations. A tailored statistical approach was introduced to quantify the similarity between the training and testing configurations, which was then related to the predictive performance. Wilcoxon signed-rank tests revealed that the OVR-wrapped XGB significantly outperformed the other algorithms, with confidence levels of 99.0% or above for the 133Ba, 60Co, 137Cs, and 152Eu sources. The findings from this work are significant as they outline techniques to promote the development of robust ML-based approaches for isotope identification.

domain adaptation↗
DOE OSTI2017

Dispersive Evolution of Nonlinear Fast Magnetoacoustic Wave Trains

Quasi-periodic rapidly propagating wave trains are frequently observed in extreme ultraviolet observations of the solar corona, or are inferred by the quasi-periodic modulation of radio emission. The dispersive nature of fast magnetohydrodynamic waves in coronal structures provides a robust mechanism to explain the detected quasi-periodic patterns. We perform 2D numerical simulations of impulsively generated wave trains in coronal plasma slabs and investigate how the behavior of the trapped and leaky components depend on the properties of the initial perturbation. For large amplitude compressive perturbations, the geometrical dispersion associated with the waveguide suppresses the nonlinear steepening for the trapped wave train. The wave train formed by the leaky components does not experience dispersion once it leaves the waveguide and so can steepen and form shocks. The mechanism we consider can lead to the formation of multiple shock fronts by a single, large amplitude, impulsive event and so can account for quasi-periodic features observed in radio spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1972

Training and certification program of the operating staff for a 90-day test of a regenerative life support system

Prior to beginning a 90-day test of a regenerative life support system, a need was identified for a training and certification program to qualify an operating staff for conducting the test. The staff was responsible for operating and maintaining the test facility, monitoring and ensuring crew safety, and implementing procedures to ensure effective mission performance with good data collection and analysis. The training program was designed to ensure that each operating staff member was capable of performing his assigned function and was sufficiently cross-trained to serve at certain other positions on a contingency basis. Complicating the training program were budget and schedule limitations, and the high level of sophistication of test systems.

Source record↗