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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Lessons Learned from NASA Goddard Space Flight Center’s Product Development Lead Training Schedule and Cost Development Workshop: Continuous Improvement

This presentation provides a status of the Goddard Space Flight Center (GSFC) effort to increase foundational knowledge of Product Development Leads (PDLs) in schedule and cost management including earned value management (EVM). In 2012, GSFC’s Engineering and Technology Directorate (ETD) implemented an in-house training program to prepare PDLs for managing the technical, cost, schedule, and risk aspects of spaceflight systems to meet their subsystem commitments. Developed in-house, the PDL training program provides an integrated approach to requirements development, risk, schedule and cost management, EVM, performance tracking, and other areas. The program has been held twice yearly since its inception with 531 participating and 451 completing the curriculum. In 2017, the program won the Robert H. Goddard award for Quality and Process Improvement. Program development and evolution were presented in the 2018 NASA Schedule and Cost Symposium. The presentation was so well received that this year we focus on one workshop within the program: Schedule and Cost Development, including EVM. We examine the on-going logic modeling process and how participant and stakeholder data influence workshop content and design, and how the disciplines of schedule and cost contribute to mission success. In this presentation we refresh you on how the approach integrates lecture, small group discussion, estimating, case study exercises, and problem solving. We update you on the data collected from participants and stakeholders, and we discuss how we use these data to measure training effectiveness. Specific topics include: • How the logic model is used as the backbone for continuous program improvement, • How feedback influences implementation and curriculum updates, • How data collection and analysis inform workshop content and development, including participant discoveries of EVM data, • How including the resource analyst and planner in the product development team supports project success.

Lessons Learned↗
NASA NTRSDate not supplied

Humans to Mars, but How Many? Using Training Requirements Modeling to Inform Crew Size

Missions to Mars will differ from previous human spaceflight missions in that the onboard crew of astronauts will be required to operate in an Earth-independent manner due to the long communication delays. Without a systematic, repeatable process to determine the number and composition of crew necessary to successfully accomplish these missions, NASA increases the risk that crew sizes may be too small to meet primary mission objectives under nominal conditions and, more consequentially, that crewmembers may not have the expertise needed to successfully respond to unforeseen failures without the real-time expertise of the Mission Control Central (MCC) team NASA currently relies upon. The NASA Engineering and Safety Center (NESC) is developing a methodology for assessing the trade space of factors that affect the number of crew for future missions. This methodology includes the consideration of results from three human performance models developed using the Improved Performance Research and Integration Tool (IMPRINT) modeling platform as well as a custom-built model on expertise trained within the crew. The IMPRINT results will be presented in the modeling and simulation sub-tag. Here we present results of a model based on NASA’s crew qualification and responsibility matrix (CQRM), a tool used to identify the crew qualifications for each area of responsibility (operation, system, and payload) for a mission. The model outputs an optimized allocation of training assignments along with a flight-assigned CQRM that can be used to consider the expertise that can be trained within a crew of a given size. We discuss the CQRM model result implications on the trade space for Mars mission crew size.

Mars↗
DOE OSTI2018

Cybersecurity Framework Tool and Training Game Help Utilities ID Cyber Attack Vulnerabilities

PNNL cybersecurity experts developed two novel cyber tools to help utilities and organizations that serve them combat malicious cyber attacks. The first is the Cybersecurity Framework Tool, or CFT. This patented tool provides a way for utilities and their associated vendors to identify, protect, detect, respond to, and recover from emerging cyber threats or vulnerabilities to critical infrastructure. CFT provides an easy-to-follow set of best practices, policies, and procedures to improve the cybersecurity postures of critical infrastructure. The second tool is the Cybersecurity Training Game Simulator, a dynamic “train the trainer” tool. The game provides interactive training scenarios based on major cyberattacks that happened in the U.S. during the past decade. Both innovations are available for commercialization. Contact Commercialization Manager Peter Christensen at peter.christensen@pnnl.gov to learn more.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2017

A perfect amalgam - the combination of company in-house training with an academic online certificate program

A well-trained and qualified workforce is an essential element in the modern day work place of the nuclear industry. For generations, from the guilds of the Middle Age through to the early Industrial Age, people learned their crafts through hands-on training provided at the hands of masters. Today, though there is an ever-pressing need for mentoring in the workplace, the fast-paced environment and lack of time to develop specific in-house training programs can create hindrances to both formal and informal learning in the work place. One way to overcome this gap is through private company and academic collaboration. In 2012, the University of Nevada, Las Vegas (UNLV) developed a Nuclear Regulatory Commission sponsored Nuclear Criticality Safety Certificate Program. In collaboration with URENCO USA, an online version of the program was also developed to assist the URENCO criticality workforce in the nuclear criticality safety (NCS) qualification process to further their education. This paper looks into the differences between formal and informal learning and how factors, which shape informal learning, are used to compliment formal learning in the work environment. It further discusses how a collaborative relationship between private and academic partners can assist a company in maintaining a high level of expertise, while assisting with a company's goals of fostering, sustaining and strengthening the nuclear safety culture. (authors)

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Training for Decommissioning and Waste Management - Competence in the Field of Nuclear Engineering - 20137

Due to the phase-out of nuclear power and the reorganization of the participating organizations in Germany, the demand for qualified personnel has increased. But the number of nuclear study programs has rapidly decreased in Germany, although qualified employees are needed for the upcoming tasks in nuclear decommissioning. The solution for those challenging tasks can only be achieved through a strategic approach, involving all stakeholders. AiNT supports this process by offering a tailor-made Education and Training (E and T) program in cooperation with a wide range of partners and initiating the project BEAST (Building competence - Expert knowledge - Applied techniques - Safe decommissioning - Train fellows) as part of the OECD NEA framework Nuclear Education, Skills and Technology (NEST) program. This paper describes the current situation in Germany, provides insight into the education and training program of AiNT, and presents the project BEAST. We are currently looking for international partner institutions that would like to cooperate with us within the framework of the BEAST project and expand the range of topics. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

The Use of Ljubljana TRIGA Reactor in Training of NPP Operators and Public Information

The TRIGA Mark II reactor at Jozef Stefan Institute in Ljubljana, Slovenia, marked 50. anniversary of its operation this year. The acronym TRIGA comes from Training, Research, Isotopes and General Atomics (manufacturer). In addition to research and production of isotopes, the Ljubljana TRIGA reactor was extensively used also for training and education. This paper reviews the contribution of this research reactor to training and public information in the last five decades. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

3D Virtual Simulation for Radiation Safety and Hazards Identification Training

This presentation will be presented at CAES codebreaker session on May 4th. The slides include two projects that demonstrate how virtual training can benefit from visualization technology. One is a collaboration project between the Applied Visualization Laboratory and College of Eastern Idaho for radiation safety and survey training. The other one is Idaho National Laboratory's hazards identification online training that utilized LiDAR technology.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Iterative sampling of expensive simulations for faster deep surrogate training

Deep neural network (DNN) surrogates of expensive physics simulations are enabling a rapid change in the way that common experimental design and analysis tasks are approached. Surrogate models allow simulations to be performed in parallel and separately from downstream tasks, thereby enabling analyses that would be impossible with the simulation in-the-loop; surrogates based on DNNs can effectively emulate diverse non-scalar data of the types collected in fusion and laboratory-astrophysics experiments. The challenge is in training the surrogate model, for which large ensembles of physics simulations must be run, preferably without wasting computational effort on uninteresting simulations. Here, in this paper, we present an iterative sampling scheme that can preferentially propose simulations in interesting regions of parameter space without neglecting unexplored regions, allowing high-quality and wide-ranging surrogate models to be trained using 2–3 times fewer simulations compare to space-filling designs. Our approach uses an explicit importance function defined on the simulation output space, balanced against a measure of simulation density which serves as a proxy for surrogate accuracy. It is easy to implement and can be tuned to find interesting simulations early in the study, allowing surrogates to be trained quickly and refined as new simulations become available; this represents an important step towards the routine generation of deep surrogate models quickly enough to be truly relevant to experimental work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Multi-task Parallelism for Robust Pre-training of Graph Foundation Models on Multi-source, Multi-fidelity Atomistic Modeling Data

Graph foundation models using graph neural networks promise sustainable, efficient atomistic modeling. To tackle challenges of processing multi-source, multi-fidelity data during pre-training, recent studies employ multi-task learning, in which shared message passing layers initially process input atomistic structures regardless of source, then route them to multiple decoding heads that predict data-specific outputs. This approach stabilizes pre-training and enhances a model’s transferability to unexplored chemical regions. Preliminary results on approximately four million structures are encouraging, yet questions remain about generalizability to larger, more diverse datasets and scalability on supercomputers. We propose a multi-task parallelism method that distributes each head across computing resources with GPU acceleration. Implemented in the open-source HydraGNN architecture, our method was trained on over 24 million structures from five datasets and tested on the Perlmutter, Aurora, and Frontier supercomputers, demonstrating efficient scaling on all three highly heterogeneous super-computing architectures.

Lupo Pasini, Massimiliano [ORNL] (ORCID:0000000249↗
DOE OSTI2013

Ginsenoside Rg3 improves cardiac mitochondrial population quality: Mimetic exercise training

Highlights: •Rg3 is an ergogenic aid. •Rg3 improves mitochondrial antioxidant capacity. •Rg3 regulates mitochondria dynamic remodeling. •Rg3 alone matches some the benefits of aerobic exercise. -- Abstract: Emerging evidence indicates exercise training could mediate mitochondrial quality control through the improvement of mitochondrial dynamics. Ginsenoside Rg3 (Rg3), one of the active ingredients in Panax ginseng, is well known in herbal medicine as a tonic and restorative agent. However, the molecular mechanism underlying the beneficial effects of Rg3 has been elusive. In the present study, we compared the effects of Rg3 administration with aerobic exercise on mitochondrial adaptation in cardiac muscle tissue of Sprague–Dawley (SD) rats. Three groups of SD rats were studied: (1) sedentary control, (2) Rg3-treated and (3) aerobic exercise trained. Both aerobic exercise training and Rg3 supplementation enhanced peroxisome proliferator-activated receptor coactivator 1 alpha (PGC-1α) and nuclear factor-E2-related factor 2 (Nrf2) protein levels in cardiac muscle. The activation of PGC-1α led to increased mRNA levels of mitochondrial transcription factor A (Tfam) and nuclear related factor 1(Nrf1), these changes were accompanied by increases in mitochondrial DNA copy number and complex protein levels, while activation of Nrf2 increased levels of phase II detoxifying enzymes, including nicotinamide adenine dinucleotide phosphate:quinone oxidoreductase 1(NQO1), superoxide dismutase (MnSOD) and catalase. Aerobic exercise also enhanced mitochondrial autophagy pathway activity, including increased conversion of LC3-I to LC3-II and greater expression of beclin1 and autophagy-related protein 7 (ATG7), these effects of aerobic exercise are comparable to that of Rg3. These results demonstrate that Rg3 mimics improved cardiac adaptations to exercise by regulating mitochondria dynamic remodeling and enhancing the quantity and quality of mitochondria.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Aerobic exercise training decreases cereblon and increases AMPK signaling in the skeletal muscle of STZ-induced diabetic rats

Highlights: • STZ-induced diabetes increased levels of CRBN protein in the skeletal muscle. • Aerobic exercise training attenuates level of CRBN protein and increases AMPK signaling in skeletal muscle. • CRBN is a regulator of exercise-mediated anti-diabetic effect. Cereblon (CRBN) has been reported as a negative regulator of adenosine monophosphate-activated protein kinase (AMPK). Aerobic exercise training has been shown to increase AMPK, which resulted in glucose regulation in skeletal muscle. However, the expression level of CRBN and its association with the physiological modulation of glucose are still unclear. Male Sprague-Dawley rats (5-week-old, n = 18) were assigned to control, streptozotocin (STZ, 65 mg/kg)-induced diabetic group, and STZ + exercise (STZ + EXE) group with six rats in each group. Rats in the STZ + EXE group exercised by treadmill running (20 m/min, 60 min, 4 times/week) for 8 weeks. Compared with the STZ group, blood glucose was significantly decreased in the STZ + EXE group. The skeletal muscle of rats in the STZ + EXE group showed a significant decrease in CRBN levels and an increase in AMPK, protein kinase B, peroxisome proliferator-activated receptor gamma coactivator 1-alpha, fibronectin type III domain-containing protein 5, glucose transporter type 4, superoxide dismutase 1, and uncoupling protein 3 levels. These results suggest that CRBN is a potential regulator of glucose homeostasis in the skeletal muscle. Moreover, our results suggest that aerobic exercise training may provide an important physiological treatment for type 1 diabetes by decreasing CRBN and increasing AMPK signaling in skeletal muscle.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications

In this work we introduce a novel two-level overlapping additive Schwarz preconditioner for accelerating the training of scientific machine learning applications. The design of the proposed preconditioner is motivated by the nonlinear two-level overlapping additive Schwarz preconditioner. The neural network parameters are decomposed into groups (subdomains) with overlapping regions. In addition, the network’s feed-forward structure is indirectly imposed through a novel subdomain-wise synchronization strategy and a coarse-level training step. Through a series of numerical experiments, which consider physicsinformed neural networks and operator learning approaches, we demonstrate that the proposed two-level preconditioner significantly speeds up the convergence of the standard (LBFGS) optimizer while also yielding more accurate machine learning models. Moreover, the devised preconditioner is designed to take advantage of model-parallel computations, which can further reduce the training time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Convergence of variational Monte Carlo simulation and scale-invariant pre-training

We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem. Here, we study both the energy minimization phase and the supervised pre-training phase that is commonly used prior to energy minimization. For the energy minimization phase, the standard algorithm is scale-invariant by design, and we provide a proof of convergence for this algorithm without modifications. The pre-training stage typically does not feature such scale-invariance. We propose using a scale-invariant loss for the pretraining phase and demonstrate empirically that it leads to faster pre-training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Divergent effects of sex and calcium/vitamin D supplementation on serum magnesium and markers of bone structure and function during initial military training

Maintaining Mg status may be important for military recruits, a population that experiences high rates of stress fracture during initial military training (IMT). The objectives of this secondary analysis were to (1) compare dietary Mg intake and serum Mg in female and male recruits pre- and post-IMT, (2) determine whether serum Mg was related to parameters of bone health pre-IMT, and (3) whether Ca and vitamin D supplementation (Ca/vitamin D) during IMT modified serum Mg. Females (n 62) and males (n 51) consumed 2000 mg of Ca and 25 μg of vitamin D/d or placebo during IMT (12 weeks). Dietary Mg intakes were estimated using FFQ, serum Mg was assessed and peripheral quantitative computed tomography was performed on the tibia. Dietary Mg intakes for females and males pre-IMT were below the estimated average requirement and did not change with training. Serum Mg increased during IMT in females (0·06 ± 0·08 mmol/l) compared with males (–0·02 ± 0·10 mmol/l; P < 0·001) and in those consuming Ca/vitamin D (0·05 ± 0·09 mmol/l) compared with placebo (0·001 ± 0·11 mmol/l; P = 0·015). In females, serum Mg was associated with total bone mineral content (BMC, β = 0·367, P = 0·004) and robustness (β = 0·393, P = 0·006) at the distal 4 % site, stress–strain index of the polaris axis (β = 0·334, P = 0·009) and robustness (β = 0·420, P = 0·004) at the 14 % diaphyseal site, and BMC (β = 0·309, P = 0·009) and stress–strain index of the polaris axis (β = 0·314, P = 0·006) at the 66 % diaphyseal site pre-IMT. No significant relationships between serum Mg and bone measures were observed in males. Findings suggest that serum Mg may be modulated by Ca/vitamin D intake and may impact tibial bone health during training in female military recruits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Tensor-Train Split-Operator Fourier Transform (TT-SOFT) Method: Multidimensional Nonadiabatic Quantum Dynamics

We introduce the “tensor-train split-operator Fourier transform” (TT-SOFT) method for simulations of multidimensional nonadiabatic quantum dynamics. TT-SOFT is essentially the grid-based SOFT method implemented in dynamically adaptive tensor-train representations. In the same spirit of all matrix product states, the tensor-train format enables the representation, propagation, and computation of observables of multidimensional wave functions in terms of the grid-based wavepacket tensor components, bypassing the need of actually computing the wave function in its full-rank tensor product grid space. We demonstrate the accuracy and efficiency of the TT-SOFT method as applied to propagation of 24-dimensional wave packets, describing the S 1 / S 2 interconversion dynamics of pyrazine after UV photoexcitation to the S 2 state. Furthermore, our results show that the TT-SOFT method is a powerful computational approach for simulations of quantum dynamics of polyatomic systems since it avoids the exponential scaling problem of full-rank grid-based representations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗