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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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Document details key information about the Nuclear Criticality Safety Program (NCSP) including financial data, functions and execution of the program, and accomplishments, challenges, and initiatives, as well as projected projects/accomplishments.
The primary objective of the Accelerating Resilience of the Community through Holistic Engagement and Use of Renewables (ARCHER) initiative was to identify and incorporate the unique variations in energy burden, social vulnerability, living conditions, and access to essential services that differ across communities. By accounting for these localized factors—down to the neighborhood level—the project supports more targeted and effective investments in community resilience. The framework seeks to establish practical planning guidance, methods, and performance measures for community energy resilience, integrate community-level and electric utility system resilience planning, and assess its effectiveness through comparison with conventional and operational planning approaches. A key component of the project was its data exchange platform, which is used to evaluate and demonstrate the tools, methodologies, and planning approaches developed through ARCHER. This open-source platform enables developers and vendors of distribution and outage management systems to build upon the research by incorporating its concepts into their own tools and workflows. This capability is enabled by the transparent availability of data, functional requirements, and the underlying information model. The project yielded several important insights. First, meaningful engagement with communities is essential to achieving comprehensive resilience outcomes. Second, resilience planning is most effective when electric grid considerations and broader community needs are addressed in a coordinated manner. Third, the use of platforms that allow for real-time input from communities can enhance utility responsiveness during restoration activities. Fourth, a structured and systematic planning approach can successfully translate ARCHER concepts into practice. Fifth, the development of an integrated metric that reflects both grid performance and community impacts provides a more holistic basis for evaluating resilience. Finally, incorporating community engagement and equity considerations into grid operations is critical, particularly during severe weather events that result in extended outages.
Cytochrome P450 3A4 (CYP3A4) is a major human drug-metabolizing enzyme, notoriously known for its extreme substrate promiscuity, allosteric behavior, and implications in drug–drug interactions. Despite extensive investigations, the mechanism of ligand binding to CYP3A4 is not fully understood. We determined the crystal structure of CYP3A4 complexed with fluorol, a small fluorescent dye that can undergo hydroxylation. In the structure, fluorol associates to the substrate channel, well suited for the binding of planar polyaromatic molecules bearing polar groups, through which stabilizing H-bonds with the polar channel residues, such as Thr224 and Arg372, can be established. Mutagenesis, spectral, kinetic, and functional data confirmed the involvement but not strict requirement of Thr224 for the association of fluorol. Collectively, our data identify the substrate channel as a high-affinity ligand binding site and support the notion that hydrophobic ligands first dock to the nearby peripheral surface, before migrating to the channel and, subsequently, into the active site.
Biodegradable pectin polymers have been recommended for a variety of biomedical applications, ranging from the delivery of oral drugs to the repair of injured visceral organs. A promising approach to regulate pectin biostability is the blending of pectin films. To investigate the development of conjoined films, we examined the physical properties of high-methoxyl pectin polymer-polymer (homopolymer) interactions at the adhesive interface. Pectin polymers were tested in glass phase (10–13% w/w water content) and gel phase (38–41% w/w water content). The tensile strength of polymer-polymer adhesion was measured after variable development time and compressive force. Regardless of pretest parameters, the adhesive strength of two glass phase films was negligible. In contrast, adhesion testing of two gel phase films resulted in significant tensile adhesion strength (p < 0.01). Adhesion was also observed between glass phase and gel phase films—likely reflecting the diffusion of water from the gel phase to the glass phase films. In studies of the interaction between two gel phase films, the polymer-polymer adhesive strength increased linearly with increasing compressive force (range 10–80 N) (R 2 = 0.956). In contrast, adhesive strength increased logarithmically with time (range 10–10,000 s) (R 2 = 0.913); most of the adhesive strength was observed within minutes of contact. Fracture mechanics demonstrated that the adhesion of two gel phase films resulted in a conjoined film with distinctive physical properties including increased extensibility, decreased stiffness and a 30% increase in the work of cohesion relative to native polymers (p < 0.01). Scanning electron microscopy of the conjoined films demonstrated cross-grain adhesion at the interface between the adhesive homopolymers. These structural and functional data suggest that blended pectin films have emergent physical properties resulting from the cross-grain intermingling of interfacial pectin chains.
Invited Talk at AGU Fall Meeting 2023 Modeling how human institutions and infrastructure interact with the water cycle is essential to better understand the vulnerability, and resilience of water resources systems from the local to the global scale. This is especially true when investigating multi-sector, multi-actor responses to the effects of long-term changes and short-term shocks. It is also well recognized that uncertainty present throughout the modeling cycle (e.g., in data, functional relations, and model coupling approaches) limits our ability to trace the interactive dynamics of human-water systems, as well as to quantify their implications for management and planning. Systems with large numbers of diverse stakeholders further compound this challenge, as uncertain drivers and complex dynamics might have very disparate effects on water users. The work presented is conducted through the Integrated Multisector Multiscale Modeling (IM3) Science Focus Area, which explores how human and natural systems co-evolve in response to change. Through this multi-year effort, we have developed exploratory modeling methods to better characterize how the uncertain human and natural drivers of water scarcity yield consequential vulnerabilities in institutionally complex, multi-sectoral systems. This talk will specifically present on a series of uncertainty characterization experiments performed in the Upper Colorado River Basin, a sub-basin of the Colorado with thousands of water users. These complementary and systematic experiments are aimed at understanding: How are various uncertain stressors (e.g., climate change, demand growth) affecting the diverse water users of this basin in terms of water shortage? What are the key drivers of this shortage for each user? Methods and results from this work are used to address additional questions on the ability of adaptation to modulate the effects of these uncertain drivers, and on their compounding effects across spatial scales and through sectoral interactions.
Tripartite ATP-independent periplasmic (TRAP) transporters are secondary-active transporters that receive their substrates via a soluble-binding protein to move bioorganic acids across bacterial or archaeal cell membranes. Recent cryo-electron microscopy (cryo-EM) structures of TRAP transporters provide a broad framework to understand how they work, but the mechanistic details of transport are not yet defined. Here we report the cryo-EM structure of the Haemophilus influenzae N-acetylneuraminate TRAP transporter (HiSiaQM) at 2.99 Å resolution (extending to 2.2 Å at the core), revealing new features. The improved resolution (the previous HiSiaQM structure is 4.7 Å resolution) permits accurate assignment of two Na + sites and the architecture of the substrate-binding site, consistent with mutagenic and functional data. Moreover, rather than a monomer, the HiSiaQM structure is a homodimer. We observe lipids at the dimer interface, as well as a lipid trapped within the fusion that links the SiaQ and SiaM subunits. We show that the affinity (K D ) for the complex between the soluble HiSiaP protein and HiSiaQM is in the micromolar range and that a related SiaP can bind HiSiaQM. This work provides key data that enhances our understanding of the ‘elevator-with-an-operator’ mechanism of TRAP transporters.
Deep learning has exhibited remarkable results across diverse areas. To understand its success, substantial research has been directed towards its theoretical foundations. Nev- ertheless, the majority of these studies examine how well deep neural networks can model functions with uniform regularities. In this paper, we explore a different angle: how deep neural networks can adapt to varying degrees of smoothness in functions and nonuni- form data distributions across different locations and scales. More precisely, we focus on a broad class of functions defined by nonlinear tree-based approximation methods. This class encompasses a range of function types, such as functions with uniform regularities and discontinuous functions. We develop nonparametric approximation and estimation theories for this class using deep ReLU networks. Our results show that deep neural networks are adaptive to the nonuniform smoothness of functions and nonuniform data distributions at different locations and scales. We apply our results to several function classes, and derive the corresponding approximation and generalization errors. The validity of our results is demonstrated through numerical experiments.
The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.
The reweighting procedure that using Bayesian statistics incorporates the information contained in a new data set, without the need of re-fitting, is applied to the quark Sivers function extracted from Semi-Inclusive Deep Inelastic Scattering (SIDIS) data. We exploit the recently published single spin asymmetry data for the inclusive jet production in polarized pp collisions from the STAR Collaboration at RHIC, which cover a much wider x region compared to SIDIS measurements. The reweighting method is extended to the case of asymmetric errors and the results show a remarkable improvement of the knowledge of the quark Sivers function.
This data package contains spatial data layers and processing scripts used in Wainwright, H.M. et al. 2021, “Watershed zonation approach for tractably quantifying above-and- belowground watershed heterogeneity and functions”. The purpose of the data and paper is to develop a watershed zonation approach for characterizing watershed organization and function in a tractable manner by applying clustering methods to multiple spatial data layers. The data package contains the geotiff files of spatial data layers, and the processed data values corresponding to the figures in the paper. The Data_description file describe each file in details. The spatial data sets (geotiff) are included in the zip files.
Here, a new variable Eddington factor (VEF) model is presented for nonlinear problems of thermal radiative transfer (TRT). The VEF model is data-driven and acts on known (a-priori) radiation-diffusion solutions for material temperatures in the TRT problem. A linear auxiliary problem is constructed for the radiative transfer equation (RTE) whose emission source and opacities are evaluated at these known material temperatures. The solution to this RTE approximates the specific intensity distribution in phase-space and time. It is applied as a shape function to define the Eddington tensor for the presented VEF model. The shape function computed via the auxiliary RTE problem will capture some degree of transport effects within the TRT problem. The VEF moment equations closed with this approximate Eddington tensor will thus carry with them these captured transport effects. In this study, the temperature data comes from multigroup P 1 , P 1/3 , and flux-limited diffusion radiative transfer models. The proposed VEF model can be interpreted as a transport-corrected diffusion reduced-order model. Numerical results are presented on the Fleck-Cummings test problem which models a supersonic wavefront of radiation. The VEF model is shown to improve accuracy by 1–2 orders of magnitude compared to the considered radiation-diffusion model solutions to the TRT problem.
Structural modelling of operando pair distribution function (PDF) data of complex functional materials can be highly challenging. To aid the understanding of complex operando PDF data, this article demonstrates a toolbox for PDF analysis. The tools include denoising using principal component analysis together with the structureMining , similarityMapping and nmfMapping apps available through the online service `PDF in the cloud' ( PDFitc , https://pdfitc.org/). The toolbox is used for both ex situ and operando PDF data for 3 nm TiO 2 -bronze nanocrystals, which function as the active electrode material in a Li-ion battery. The tools enable structural modelling of the ex situ and operando PDF data, revealing two pristine TiO 2 phases (bronze and anatase) and two lithiated Li x TiO 2 phases (lithiated versions of bronze and anatase), and the phase evolution during galvanostatic cycling is characterized.
We propose a general machine learning-based framework for building an accurate and widely applicable energy functional within the framework of generalized Kohn–Sham density functional theory. To this end, we develop a way of training self-consistent models that are capable of taking large datasets from different systems and different kinds of labels. Here, we demonstrate that the functional that results from this training procedure gives chemically accurate predictions on energy, force, dipole, and electron density for a large class of molecules. It can be continuously improved when more and more data are available.
In this work, we have developed a new joint inversion method that incorporates body wave arrival times, surface wave dispersion and receiver functions to simultaneously update earthquake locations and constrain three-dimensional P-wave (Vp) and S-wave velocity (Vs) models. Due to complementary sensitivities of the three types of data, the proposed joint inversion algorithm can reduce the intrinsic non-uniqueness of inversions using fewer types of data and better determine smooth velocity variations and velocity discontinuities. Synthetic tests demonstrate the advantages of this new joint inversion algorithm in resolving velocity structures, especially in constraining velocity gradients across the Moho interface. We have applied the proposed joint inversion algorithm to image the lithosphere velocity structure of south China. The inverted Vp and Vs models fit body wave arrival times, surface wave dispersion and receiver functions well. We further analyzed the distribution of Vs gradients across the Moho interface in detail, which helps us better understand tectonics in south China.
We introduce the emPDF (empirical distribution function), a novel dynamical modelling method that infers the gravitational potential from kinematic tracers with optimal statistical efficiency under the minimal assumption of steady state. emPDF determines the best-fitting potential by maximizing the similarity between instantaneous kinematics and the time-averaged phase-space distribution function (DF), which is empirically constructed from observation upon the theoretical foundation of oPDF (Han et al. 2016). This approach eliminates the need for presumed functional forms of DFs or orbit libraries required by conventional DF- or orbit-based methods. emPDF stands out for its flexibility, efficiency, and capability in handling observational effects, making it preferable to the popular Jeans equation or other minimal assumption methods, especially for the Milky Way (MW) outer halo where tracers often have limited sample size and poor data quality. We apply emPDF to infer the MW mass profile using Gaia DR3 data of satellite galaxies and globular clusters, obtaining enclosed masses of M (,r) = 26±8, 46±8, 90±13, and 149±40 x 10 10 M ⊙ at r = 30, 50, 100, and 200 kpc, respectively. These are consistent with the updated constraints from simulation-informed DF fitting (Li et al. 2020). While the simulation-informed DF offers superior precision owing to the additional information extracted from simulations, emPDF is independent of such supplementary knowledge and applicable to general tracer populations. emPDF is currently implemented for tracers with complete 6D kinematics within spherical potentials, but it can potentially be extended to address more general problems.
An overarching goal of the UNiversal Interoperability for grid-Forming Inverters (UNIFI) Consortium is to develop vendor agnostic specifications and guidelines that ensure interoperability of gridforming (GFM) inverter-based resources (IBRs) without requiring vendors or system operators to reveal proprietary information. These UNIFI principles and specifications are envisioned to apply to a wide range of technologies and systems. The initial work on the UNIFI principles and specifications has focused on performance requirements and high-level aspirational principles outlined in [1]. Going forward, a key question is how to translate such high-level requirements and principles into rigorous specifications that can be enforced and validated for a wide range of IBRs.
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