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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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Association of Prepregnancy Substance Use and Substance Use Disorders with Pregnancy Timing and Intention
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Pharmacoepidemiology, Machine Learning and COVID-19: An intent-to-treat analysis of hydroxychloroquine, with or without azithromycin, and COVID-19 outcomes amongst hospitalized US Veterans
Hydroxychloroquine (HCQ) was proposed as an early therapy for coronavirus disease 2019 (COVID-19) after in vitro studies indicated possible benefit. Previous in vivo observational studies have presented conflicting results, though recent randomized clinical trials have reported no benefit from HCQ amongst hospitalized COVID-19 patients. In this work, we examined the effects of HCQ alone, and in combination with azithromycin, in a hospitalized COVID-19 positive, United States (US) Veteran population using a propensity score adjusted survival analysis with imputation of missing data. From March 1, 2020 through April 30, 2020, 64,055 US Veterans were tested for COVID-19 based on Veteran Affairs Healthcare Administration electronic health record data. Of the 7,193 positive cases, 2,809 were hospitalized, and 657 individuals were prescribed HCQ within the first 48-hours of hospitalization for the treatment of COVID-19. There was no apparent benefit associated with HCQ receipt, alone or in combination with azithromycin, and an increased risk of intubation when used in combination with azithromycin [Hazard Ratio (95% Confidence Interval): 1.55 (1.07, 2.24)]. In conclusion, we assessed the effectiveness of HCQ with or without azithromycin in treating patients hospitalized with COVID-19 using a national sample of the US Veteran population. Using rigorous study design and analytic methods to reduce confounding and bias, we found no evidence of a survival benefit from the administration of HCQ.
Advanced FLISR With Intentional Islanding Operations in an ADMS Environment Using GridAPPS-D
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Human-in-the-Loop Robot Control for Human-Robot Collaboration: Human Intention Estimation and Safe Trajectory Tracking Control for Collaborative Tasks
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Spectral Graph Clustering for Intentional Islanding Operations in Resilient Hybrid Energy Systems
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X-ray CT scan of octet lattice structures with intentionally missing struts
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Machine learning approaches for intentional materials engineering
In this article, the development of nanoporous metals and metallic composites through dealloying processes presents significant opportunities in materials engineering. However, designing multicomponent precursor alloys and establishing corresponding processing methods that yield predictable compositions and nanostructures remain a complex challenge. This article explores how machine learning (ML)-augmented computational and experimental methodologies can tackle these challenges by predicting precursor alloy compositions, final nanoporous structures, and mechanical properties, while integrating ML-enabled autonomous experimentation for material design and quantification. We highlight recent advancements in applying ML to nanostructured materials design via dealloying and discuss how techniques from other nanomaterial designs can be adapted for improved control over morphological and compositional outcomes in nanoporous and nanocomposite materials. Furthermore, we explore the role of ML in autonomous synchrotron x-ray experimentation, enabling real-time feedback between modeling and experimental setups. ML-driven approaches to microstructure characterization and mechanical property prediction are also examined, with a focus on modeling and advanced imaging techniques such as three-dimensional nanotomography. Finally, this article outlines future directions for ML-enhanced materials science, emphasizing the exploration of high-dimensional parameter spaces and the incorporation of materials kinetics into processing and property evaluation, ultimately advancing the design of nanoporous structures and materials science.
The Policy Mullet: Good Intentions in the Front, Bad Policy in the Back
To achieve important policy goals like decarbonization, energy efficiency, and social justice, it is important to translate high-level policy into operational practice. Establishing sustainable purchasing goals has been one important part of this translation. However, not all sustainable purchasing policies are equally effective for organizations. In this study, we analyze sustainable purchasing policies collected from 30 public-sector organizations. We discuss a theoretical framework of optimal policy language and use that framework to evaluate the policies we collected. We believe that an ideal sustainable purchasing policy can increase the likelihood of implementation success by providing more product-specific guidance, giving authority to procurement staff, setting performance targets, and requiring reporting to account for progress. In our review of public sector sustainable purchasing policies, we found that policy language is less than ideal when measured against the framework. Based on our findings, we highlight the best practice language organizations can use in their sustainable purchasing policies to meet sustainability goals. While there is more work needed to inform best practices with sustainable purchasing policies, we recommend that organizations utilize existing frameworks when developing a sustainable purchasing policy to encourage better implementation.
Logical Foundations for Protecting Materials and Facilities from those with Malicious Intent?Proposed 1st Principles for Security Systems.
Abstract not provided.
Optimal Electric Grid Black Start Restoration Subject to Intentional Threats
Efficient restoration of the electric grid from significant disruptions – both natural and manmade – that lead to the grid entering a failed state is essential to maintaining resilience under a wide range of threats. Restoration follows a set of black start plans, allowing operators to select among these plans to meet the constraints imposed on the system by the disruption. Restoration objectives aim to restore power to a maximum number of customers in the shortest time. Current state-of-the-art for restoration modeling breaks the problem into multiple parts, assuming a known network state and full observability and control by grid operators. These assumptions are not guaranteed under some threats. This paper focuses on a novel integration of modeling and analysis capabilities to aid operators during restoration activities. A power flow-informed restoration framework, comprised of a restoration mixed-integer program informed by power flow models to identify restoration alternatives, interacts with a dynamic representation of the grid through a cognitive model of operator decision-making, to identify and prove an optimal restoration path. Application of this integrated approach is illustrated on exemplar systems. Validation of the restoration is performed for one of these exemplars using commercial solvers, and comparison is made between the steps and time involved in the commercial solver, and that required by the restoration optimization in and of itself, and by the operator model in acting on the restoration optimization output. Publications and proposals developed under this work, along with a path forward for additional expansion of the work, and summary of what was achieved, are also documented.
Letter of Intent: Muonium R&D/Physics Program at the MTA
With the planned turn-on of the PIP-II 800 MeV superconducting proton linac, Fermilab will potentially become the world's best laboratory at which to carry out fundamental muon measurements, sensitive searches for symmetry violation, and precision tests of theory. In preparation, we propose to develop the techniques that will be needed. An R&D and physics program is proposed at the Fermilab MeV Test Area to use the existing 400 MeV Linac to demonstrate the efficient production of a slow muonium beam using $\mu^+$ stopped in a ~100 $\mu$m-thick layer of superfluid helium, and to use that beam to measure muonium gravity.
Letter of Intent for a Drell-Yan experiment with a polarized proton target
It is well known that the proton is a spin-1/2 particle, but how the constituents (quarks and gluons) assemble to this quantized spin is still a mystery. There is a worldwide effort to map out the individual contributions to the proton spin. It is established that the quark spins contribute around 30%, while the gluon intrinsic angular momentum is still under active investigation at the Relativistic Heavy Ion Collider. Fully resolving the proton spin puzzle requires information on the orbital angular momentum (OAM) of both quarks and gluons. Recent studies have shown that the so-called transverse momentum dependent parton distribution functions (TMDs) can inform us about the OAM of the partons.
Optimal Electric Grid Black Start Restoration Subject to Intentional Threats.
Abstract not provided.
HPC Center of the Future: R&D Acquisition Intent
This document contains intended technical requirements for an anticipated future procurement for Lawrence Livermore National Laboratory (LLNL), hereafter referred to as “LLNL” or “the Laboratory”, which seeks to fund a few new and innovative proposals for developing concepts and performing studies that would lead to products available in the 2029-2030 timeframe. Our interest is to incentivize explorations and evaluations of technical capabilities in support of our vision for a High Performance Computing (HPC) center of the future at Livermore Computing (LC), the HPC center at LLNL. To realize this vision a new approach to our approach to acquisition is being considered by LC. The approach is designed to decrease risks for the vendors, while encouraging a broader set of vendors able and we hope willing to partner with LLNL and LC. The future HPC Center vision and acquisition approach was conceived to meet the future mission needs of the Advanced Simulation and Computing (ASC) Program within the National Nuclear Security Administration (NNSA).
A Technique for Thermal Overload Mitigation in a Self-Healing Intentional Island System Using Only Local Measurements
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A Systematic Approach to Light Load Calculation for DG Un-Intentional Islanding
With the influx of distributed generation (DG) penetration, power utilities have adopted active anti-islanding protection methods to prevent an unintentional islanding condition. One commonly used protection scheme is implemented through the use of a transfer trip from the upline connected substation to the DG facility and is required when the capacity of connected DGs exceed a certain threshold of local minimum load. The threshold established by standard IEEE 1547.2-2009 states that a 3:1 minimum load to generation ratio is acceptable to ensure the DG will not sustain an unintentional island. However, the term “minimum load” is not precisely defined in any standard, and in practice, a light load value is chosen by engineers through manual methods that can be inconsistent, not representative and change drastically based on various cases. To solve this problem, this paper proposes a new light load calculation method creating consistency and better accuracy; the method has been implemented in. NET C# application and is used within Dominion Energy.