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All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Training Astronauts using Hardware-in-the-Loop Simulations and Virtual Reality

The commercial market has recently started giving significant attention to virtual and augmented reality, even though the technology has been around for many years. The Virtual Reality Training Lab (VRL) at the NASA Johnson Space Center has been using virtual reality to train astronauts for decades. This paper will focus on describing three major Hardware-in-the-Loop VR simulation systems, the Simplified Aid for EVA Rescue (SAFER) system known as the "jetpack", the Mass Handling System nicknamed Charlotte, and a simulated robotics environment for collaborative mission evaluation. Two of these systems are critical for astronaut training. Crew must certify on SAFER and go through the Charlotte Mass Handling training prior to flying to the International Space Station (ISS). Typically, they also complete at least one collaborative visualization session to review any planned Extra Vehicular Activities (EVAs), or spacewalks, before an assigned flight. Given the volatility of new technologies, the graphics and simulation environments used are maintained to be hardware agnostic to preserve a high level of fidelity. Utilizing VR for astronaut training has proved to be effective and essential for these specific systems.

Angelica D. Garcia↗

NASA NTRSDate not supplied

GL4U: Bioinformatics training for students and educators using space omics data

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. During the educator pilot, scheduled for June 2022, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative.

Amanda Marie Saravia-butler↗

NASA NTRSDate not supplied

GL4U: Bioinformatics Training for Students and Educators Using Space Omics Data

Amanda Marie Saravia-butler↗

NASA NTRSDate not supplied

Volcanic Field Sites for Artemis Testing and Training

The Artemis Program will reestablish human presence on the Moon and lead to a new era of scientific discovery and exploration. Led by the National Aeronautics and Space Administration (NASA), the Artemis Program is a collaboration of space agencies and companies around the world. An integrated effort between various disciplines of science, engineering, and mission operations is currently developing methods, facilities, and analog field locations to train astronauts and test hardware and concepts of operations. These efforts aim to best prepare for the next steps of human exploration on the lunar surface and beyond. Numerous terrestrial volcanic field sites were evaluated and selected for their unique roles in helping to prepare for the lunar surface mission phases. This effort heavily leveraged the comprehensive academic research conducted at these field sites, as well as the tremendous Apollo heritage. The currently selected volcanic field sites include the San Francisco Volcanic Field in Northern Arizona, the Potrillo Volcanic Field in southern New Mexico, the highlands of Iceland, and the Southwestern Nevada Volcanic Field. Within each of these volcanic field sites numerous specific testing and training locations are being further developed utilizing the analogous terrain and unique features in these regions. Recent Artemis testing and training events have been conducted at a number of these volcanic field sites by both a dedicated Artemis Geology Training Team and a Joint Extra Vehicular Activity (EVA) Testing Team. This presentation will highlight the selected sites as well as the objectives and accomplishments of some of the recent field-testing events and training courses. Additionally, we continually strive to pursue additional sites, locations, data sets, collaborations, and partnerships in this endeavor and welcome knowledge transfer and community input.

Trevor Graff↗

DOE OSTI2023

Systems and methods for active learning from sparse training data

A method for active learning using sparse training data can include training a machine learning model using less than ten first training data points to generate a candidate machine learning model. The method can include performing a Monte Carlo process to sample one or more first outputs of the candidate machine learning model. The method can include testing the one or more first outputs to determine if each of the one or more first outputs satisfy a respective convergence condition. The method can include, responsive to at least one first output not satisfying the respective convergence condition, training the candidate machine learning model using at least one second training data point corresponding to the at least one first output. The method can include, responsive to the one or more first outputs each satisfying the respective convergence condition, outputting the candidate machine learning model.

Sankaranarayanan, Subramanian↗

DOE OSTI2015

Current Status of Human Resource Training Program for Fostering RIBiomics Professionals

RI-Biomics is a state-of-the-art radiation fusion technology for evaluating in-vivo dynamics such as absorption, distribution, metabolism and excretion (ADME) of new drug candidates and biomaterials using radioisotope (RI), and quantitative evaluation of their efficacy via molecular imaging techniques and animal models. The RI-Biomics center is the sole comprehensive research and experiment complex in Korea that can simultaneously perform the radio-synthesis of drug candidate with radioisotope, analysis, and molecular imaging evaluation with animal model. Molecular imaging techniques, including nuclear imaging (SPECT and PET), near-infrared fluorescent (NIRF) imaging, and magnetic resonance imaging (MRI), are the cutting-edge technologies for evaluating drug candidates. Since they allow in vivo real-time imaging of the diseased site, monitoring the biodistribution of drug and determining the optimal therapeutic efficacy following treatments, we have integrated RI-ADME and molecular imaging to provide useful information for drug evaluation and to accelerate the development of new drugs and biomaterials. The RI-Biomics center was established with total investment of 18 million $ during four years from 2009 to 2012 in order to develop a comprehensive analyzing system using RI for new drug development as an axis for national growth in the next generation. The RI-Biomics center has labeling synthesis facility for the radiosynthesis of drug candidate with radioisotope such as Tc-99m, I-125, I-131, F-18, H-3 and C-14 using hot cell. It also includes RI-general analysis facilities, such as Radio-HPLC, LC/MS, GC/MS, gamma counter that can analyzing the radio-synthesized materials, and animal image analysis facilities that developed small animal imaging equipment such as SPECT/PET/CT, 7 T MRI, in-vivo optical imaging system and others. In order to achieve the system to verify safety and effectiveness of the new drugs using RI, it is necessary to establish a human resource training program for fostering RI-Biomics professionals in the following key fields; (1) Radio-pharmaceuticals synthesis and labeled compound development, (2) Development of RI-ADME in the living object and image assessment technology. Personnel training program that carries out theoretical education and practical training in the field related to RI-Biomics in parallel is being conducted. Internship training for university students has been administered twice already while educational program for the existing professionals in the RI-Biomics field will be carried out during the summer of 2014. The human resource training program for combination of RIADME and different molecular imaging techniques can offer synergistic advantages to facilitate understanding RIADME and fostering RI-ADME professionals. (authors)

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2017

Training and certification of human organizational factors designers in nuclear plant engineering - 344

Performing an increasing number of small and large-scope nuclear Human Factors Programs, AREVA NP is facing the challenging situation that (1) well-trained experts in Human Factors or Usability Engineering are currently hard to find and (2) newly hired ones need at least two years 'on the job' to become familiar with the nuclear field and the complexity of engineering Human Factors in this field. To innovatively meet this challenge we focus instead on company-internal engineers. Demonstrated nuclear engineering knowledge and skills and pronounced interest to extend both renders them optimal applicants to the AREVA NP two years Human Organizational Factors Training and Certification program. The AREVA NP training shall convey Human Factors knowledge and skills within the limited time of two years. We also stress the need to change participants' mindset from a technically-biased to a human-centered approach, committed to involve end-users throughout design. Next to psychology principles, user testing, user-centered design, plant operation principles, and the acquisition and documentation of users' requirements, design constraints, and inherent tradeoffs, participants will be trained to apply learned methods in a real environment and meet the challenges of real projects. The following sections provide insights in the training conception and preparation phase, the identification of appropriate candidates, the development of course syllabuses, and the schedule of lectures and tests. The promising experiences made thus far are reported as well. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

CFD-trained ANN Model for Approximating Near-occupant Condition in Real-time Simulations

The main drawback of Computational Fluid Dynamics (CFD) simulations has been the time and resource consuming nature which is not suitable for real-time applications. In this work, we first generated numerous CFD models of a given indoor space to obtain airspeed, temperature, and mean radiant temperature near an occupant as training data. Several artificial neural networks (ANN) models were trained using this CFD simulated data to approximate near real-time environmental conditions for a given occupant. This trained ANN model approach is a part of a real-time simulation of building operations using a combination of software and real hardware (HVAC equipment) approaches. The preliminary results suggest that the CFD- generated training data and the trained ANN model can accurately approximate such conditions in a real-time application, a method that has great potential in building simulation and building digital twin areas of research.

Zhang, Yun↗

DOE OSTI2025

A geometric framework for momentum-based optimizers for low-rank training

Low-rank pre-training and fine-tuning have recently emerged as promising techniques for reducing the computational and storage costs of large neural networks. Training low-rank parameterizations typically relies on conventional optimizers such as heavy ball momentum methods or Adam. In this work, we identify and analyze potential difficulties that these training methods encounter when used to train low-rank parameterizations of weights. In particular, we show that classical momentum methods can struggle to converge to a local optimum due to the geometry of the underlying optimization landscape. To address this, we introduce novel training strategies derived from dynamical low-rank approximation, which explicitly account for the underlying geometric structure. Our approach leverages and combines tools from dynamical low-rank approximation and momentum-based optimization to design optimizers that respect the intrinsic geometry of the parameter space. We validate our methods through numerical experiments, demonstrating faster convergence, and stronger validation metrics at given parameter budgets.

Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗

DOE OSTI2023

Nuclear Criticality Safety Training: Needs and Efforts [Slides]

This presentation on Nuclear Criticality Safety Training: Needs and Efforts covers several points. This presentation starts with a quick overview of Nuclear Criticality Safety (NCS) Training. Then it lists current and ongoing training with a focus on the training in progress. This presentation then concludes with a look at supporting efforts.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

Importance of Hands-on Training within the Nuclear Industry

Almost all work performed within the nuclear industry requires some form of training, regardless of an individual’s education. While many of the hazards that exist in nuclear operations are present in other industries (e.g., industrial safety), nuclear operations have the unique hazards of radiation and inadvertent criticality. Therefore, these topics require additional training for individuals performing hands-on work or accessing those work areas. Many methods are available to train people including lectures (either with an instructor or automated video), reading procedures/policies, hands-on demonstrations, and on-the-job training.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem

Some research topics belonging to classification problems in the nuclear industry, such as fault diagnosis and accident identification, can be solved by feature extraction and subsequent application of statistical machine learning classifiers. Recently, deep neural network-based methods with automatic feature extraction and high accuracy have gained wide attention. They usually require large-scale training data, however, plant fault or accident data are scarce or difficult to obtain. Here this paper proposes a convolutional network (CNN)-based transfer learning method to solve this problem. The network's shallow layer is derived from a pre-trained CNN based on the ImageNet database to automatically extract features, and the deep layer is customized to match the classification problem. Data in non-image formats are converted to image formats and subsequently used to train the network. Case studies of rotating machines fault diagnosis show that the proposed method requires only limited training data to achieve high accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

A mathematical approach to using the forgetting curve to evaluate experience and training factors in human reliability analysis

Traditional human reliability analysis (HRA) methods have difficulty dealing with the dynamic nature of factors such as time and rely on static and expert-judgment-based assessments of performance-shaping factors (PSFs) across limited levels. In this study, we introduce a mathematical approach for dynamically evaluating the experience and training PSF. Our proposed method integrates the psychological concept of the “forgetting curve” to evaluate how PSFs are impacted by the number of trainings and the time elapsed since training. To confirm the validity of the model, we provide experimental data fitted by identifying the quantitative relationship between training and human performance. This research enables dynamic and objective assessments, thus reducing reliance on subjective expert judgment and improving the accuracy of HRA.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2015

Education and Training Needs in Radiation Oncology in India: Opportunities for Indo–US Collaborations

Purpose: To conduct a survey of radiation oncologists in India, to better understand specific educational needs of radiation oncology in India and define areas of collaboration with US institutions. Methods and Materials: A 20-question survey was distributed to members of the Association of Indian Radiation Oncologists and the Indian Brachytherapy Society between November 2013 and May 2014. Results: We received a total of 132 responses. Over 50% of the physicians treat more than 200 patients per day, use 2-dimensional or 3-dimensional treatment planning techniques, and approximately 50% use image guided techniques. For education needs, most respondents agreed that further education in intensity modulated radiation therapy, image guided radiation therapy, stereotactic radiation therapy, biostatistics, and research methods for medical residents would be useful areas of collaboration with institutions in the United States. Other areas of collaboration include developing a structured training module for nursing, physics training, and developing a second-opinion clinic for difficult cases with faculty in the United States. Conclusion: Various areas of potential collaboration in radiation oncology education were identified through this survey. These include the following: establishing education programs focused on current technology, facilitating exchange programs for trainees in India to the United States, promoting training in research methods, establishing training modules for physicists and oncology nurses, and creating an Indo–US. Tumor Board. It would require collaboration between the Association of Indian Radiation Oncologists and the American Society for Radiation Oncology to develop these educational initiatives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Analysis of Nb$_3$Sn Accelerator Magnet Training

Nb 3 Sn accelerator magnet technology has made significant progress during the past decades. For the first time, it is planned to be used in a real accelerator. A relatively small number of Nb 3 Sn quadrupoles and dipoles will be installed in the LHC to increase machine luminosity. Although it will prove the possibility of using Nb 3 Sn magnets in real machines, many questions of scaling this technology up remain. One of them is related to slow training of Nb 3 Sn magnets compared to the traditional Nb-Ti accelerator magnets. Since the goal is to operate thousands of Nb 3 Sn magnets in a future post-LHC accelerator, the slow training will affect both the practical design margin and the nominal operation field. Consequently, the cost of the project to reach the design field level is also increased. Furthermore, to improve our understanding of slow magnet training the existing Fermilab data from Nb 3 Sn magnet tests were re-analyzed. A summary of coil training features and correlations with fabrication parameters observed is presented in this paper.

43 PARTICLE ACCELERATORS↗

DOE OSTI2014

MO-E-18C-01: Open Access Web-Based Peer-To-Peer Training and Education in Radiotherapy

Purpose: Current training and education delivery models have limitations which result in gaps in clinical proficiency with equipment, procedures, and techniques. Educational and training opportunities offered by vendors and professional societies are by their nature not available at point of need or for the life of clinical systems. The objective of this work is to leverage modern communications technology to provide peer-to-peer training and education for radiotherapy professionals, in the clinic and on demand, as they undertake their clinical duties. Methods: We have developed a free of charge web site ( https://i.treatsafely.org ) using the Google App Engine and datastore (NDB, GQL), Python with AJAX-RPC, and Javascript. The site is a radiotherapy-specific hosting service to which user-created videos illustrating clinical or physics processes and other relevant educational material can be uploaded. Efficient navigation to the material of interest is provided through several RT specific search tools and videos can be scored by users, thus providing comprehensive peer review of the site content. The site also supports multilingual narration\translation of videos, a quiz function for competence assessment and a library function allowing groups or institutions to define their standard operating procedures based on the video content. Results: The website went live in August 2013 and currently has over 680 registered users from 55 countries; 27.2% from the United States, 9.8% from India, 8.3% from the United Kingdom, 7.3% from Brazil, and 47.5% from other countries. The users include physicists (57.4%), Oncologists (12.5%), therapists (8.2%) and dosimetrists (4.8%). There are 75 videos to date including English, Portuguese, Mandarin, and Thai. Conclusion: Based on the initial acceptance of the site, we conclude that this open access web-based peer-to-peer tool is fulfilling an important need in radiotherapy training and education. Site functionality should expand in the future to include document sharing and continuing education credits.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2016

SU-F-E-14: Global Radiation Oncology Education and Training in Medical Physics Powered by Information and Communication Technologies

Purpose: Recent publications have highlighted the potential of Information and Communication Technologies (ICTs) to catalyze collaborations in cancer care, research and education in global radiation oncology. This work reports on the use of ICTs for global Medical Physics education and training across three countries: USA, Tanzania and Kuwait Methods: An online education platform was established by Radiation Oncology Faculty from Harvard Medical School, and the University of Pennsylvania with integrated Medical Physics Course modules accessible to trainees in Tanzania via partnership with the Muhimbili University of Health and Allied Sciences, and the Ocean Road Cancer Institute. The course modules incorporated lectures covering Radiation Therapy Physics with videos, discussion board, assessments and grade center. Faculty at Harvard Medical School and the University of Massachusetts Lowell also employed weekly Skype meetings to train/mentor three graduate students, living out-of-state and in Kuwait for up to 9 research credits per semester for over two semesters towards obtaining their graduate degrees Results: Students were able to successfully access the Medical Physics course modules and participate in learning activities, online discussion boards, and assessments. Other instructors could also access/co-teach the course modules from USA and Tanzania. Meanwhile all three graduate students with remote training via Skype and email made major progress in their graduate training with each one of them submitting their research results as abstracts to be presented at the 2016 AAPM conference. One student has also published her work already and all three are developing these abstracts for publication in peer-reviewed journals. Conclusion: Altogether, this work highlights concrete examples/model on how ICTs can be used for capacity building in Medical Physics across continents, for both education and research training needed for Masters/PhD degrees. The developed modules and model will be scaled to benefit many more trainees and other developing countries.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Radiological Worker II Training, Course 20301 (Live), Course 12909 (Test)

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↗