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At least 145 records · Page 8

Nuclear Structure and Decay Data for A=76 Isobars

The experimental nuclear spectroscopic data for known nuclides of mass number 76 (Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Kr, Rb, Sr, Y) have been evaluated and presented together with Adopted properties for levels and γ rays. With the exception of structure data for 76 Ga nucleus, significant new data have been incorporated for all the other nuclides of A=76 since the previous 1995 update in ENSDF database and NDS publication 1995Si03. No data are yet available for excited states in 76 Fe, 76 Cu and 76 Y. Decay scheme characteristics for the decay of 76 Co, 76 Ni, and 76 Y are unknown while those for decays of 76 Cu and 76 Sr seem incomplete. For 76 Ni, very little structure data are available, and for 76 Ga and 76 As, only low-spin ( or so) information is available. Furthermore, this work supersedes the data presented in the previous (1995Si03) NDS evaluation of A=76.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗

International network of nuclear structure and decay data evaluators

Compilation, evaluation and dissemination of nuclear data are arduous tasks that rely on contributions from experts in both the basic and applied sciences communities whose efforts are coordinated by the International Atomic Energy Agency (IAEA). The Evaluated Nuclear Structure Data File (ENSDF) includes the most extensive and comprehensive set of nuclear structure and decay data evaluations performed by the international network of Nuclear Structure and Decay Data evaluators (NSDD) under the auspices of the IAEA. In this report we describe the recent NSDD activities supported by the IAEA and provide some future perspectives.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Synthesis of dysprosium oxychloride (DyOCl)

Dysprosium oxychloride, DyOCl, was synthesized using a simple dehydration method with DyCl3·6H2O. X-ray powder diffraction data was used to determine the crystal structure. The DyOCl compound is isostructural to the matlockite (PbFCl) crystal structure and crystallizes in the tetragonal P4/nmm (#129) space group. The crystal structure contains the alternating cationic layers of (DyO)n and anionic layers of nCl– along the c-axis. The structural data including unit cell, volume, and density of DyOCl were compared to other REOCl data from Inorganic Crystal Structure Database (ICSD) and our previous study on TbOCl. Fourier-transform infrared spectroscopy (FTIR) was performed, and the vibrations of DyOCl possibly from Dy–O and Dy–Cl were observed at 543 and 744 cm-1. Scanning electron microscopy analysis (SEM) showed irregularly shaped crystals.

Chong, Saehwa↗

Cooperative allostery and structural dynamics of streptavidin at cryogenic- and ambient-temperature

Multimeric protein assemblies are abundant in nature. Streptavidin is an attractive protein that provides a paradigm system to investigate the intra- and intermolecular interactions of multimeric protein complexes. Also, it offers a versatile tool for biotechnological applications. Here, we present two apo-streptavidin structures, the first one is an ambient temperature Serial Femtosecond X-ray crystal (Apo-SFX) structure at 1.7 Å resolution and the second one is a cryogenic crystal structure (Apo-Cryo) at 1.1 Å resolution. These structures are mostly in agreement with previous structural data. Combined with computational analysis, these structures provide invaluable information about structural dynamics of apo streptavidin. Collectively, these data further reveal a novel cooperative allostery of streptavidin which binds to substrate via water molecules that provide a polar interaction network and mimics the substrate biotin which displays one of the strongest affinities found in nature.

60 APPLIED LIFE SCIENCES↗

Complete β-decay patterns of 142 Cs, 142 Ba, and 142 La determined using total absorption spectroscopy

Background: The β decays of fission products produced in nuclear fuel are important for nuclear energy applications and fundamental science of reactor antineutrinos. In particular, nuclear reactor safety is related to the decay modes of radioactive neutron-rich nuclei, primarily via the emission of γ rays, neutrons, and electrons. Additionally nuclear reactors are the most powerful man-made source of antineutrinos emitted during the β decay of fission products. These antineutrinos are used to inspect fundamental properties of leptons as well as informing reactor operation. However, the majority of data on complex decays of fission products collected in the evaluated nuclear data repositories like Evaluated Nuclear Structure Data File (ENSDF) and Evaluated Nuclear Data Files (ENDF) are based on low-efficiency and often incomplete measurements resulting in questionable reference reactor antineutrino flux predictions, see the analysis by [Nichols, J. Nucl. Sci. Technol. 52, 17 (2015)]. Various assessments like the one done under the auspices of the [Yoshida et al., Assessment of Fission Product Decay Data for Decay Heat Calculations: A report by the Working Party on International Evaluation Co-operation of the Nuclear Energy Agency Nuclear Science Committee (Nuclear Energy Agency, Organization for Economic Co-operation and Development, Paris, France, 2007), Vol. 25], as well as by [Sonzogni, Johnson, and McCutchan, Phys. Rev. C 91, 011301(R) (2015)] and [Dwyer and Langford, Phys. Rev. Lett. 114, 012502 (2015)], list the A = 142 isobars with high cumulative fission yield among the important nuclei where data for reactor decay heat and/or antineutrino production should be verified and/or improved. Purpose: Here, our goal is to improve the quality of β -decay measurements and evaluate the impact of modified decay schemes on reactor decay heat and antineutrino energy spectra, for fission products along the A = 142 isobaric chain. This work is an in depth follow-up on [Rasco et al., Phys. Rev. Lett. 117, 092501 (2016)]. which presented briefly the impact of the corrected decay scheme of 142 Cs . Here, we extend the data to full isobaric decay chain including the daughter nuclei, 142 Ba and 142 La, and present more details on the 142 Cs results. Method: The decays of neutron-rich isobars of mass A = 142 produced by means of proton-induced fission of 238 U were measured using the Modular Total Absorption Spectrometer (MTAS) array on-line at the mass separator and Tandem accelerator at Oak Ridge National Laboratory. Results: The β -decay schemes for 142 Cs and 142 La were modified with respect to the nuclear data repositories. A small β-delayed neutron branching ratio for 142 Cs emitter was remeasured as $0.10^{+5}_{–3}% %. Improved precision on the measured half-lives is reported. Small corrections to the low-energy decay of 142 Ba are made. The β-decay patterns for 142 La and 142 Cs are presented. The decay heat release and cross section for the detection of reactor antineutrinos are deduced and compared to earlier results. Conclusions: The β-feeding pattern for 142 Cs having decay energy value $Q_β$ of over 7 MeV was substantially modified with respect to the current ENSDF entry. Smaller changes were encountered for 142 La, but since this A = 142 isobar also has a large cumulative yield in fission, the changes influence both decay heat and the antineutrino spectra. The previously known β intensities for 142 Ba decay ($Q_β$ value of 2.2 MeV) were verified and slightly modified. Overall, increased decay heat values and lower flux of antineutrinos interacting with matter are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Ramen

Many computationally unintensive models for material process-structure-property connections exist in the literature (e.g. journal articles and textbooks). However, to our knowledge there is no existing compilation of these models in one package. Many industrially and research relevant workflows involve combinations of many of these models and if the models have inconsistent interfaces, building these workflows can be challenging. Ramen provides a collection of computationally unintensive models for calculating material process-structure-property relationships from the literature, as well as related tools (e.g. for plotting results). These models use a consistent interface structure and all leverage material data files and data structures from the Mist library (also developed at ORNL). Ramen provides a structured package for combining many standard models in one place, with a standardized interface. This approach makes combining models in long/complex chains easy and straightforward.

DeWitt, Stephen [Oak Ridge National Laboratory (OR↗

Leiomodin creates a leaky cap at the pointed end of actin-thin filaments

Improper lengths of actin-thin filaments are associated with altered contractile activity and lethal myopathies. Leiomodin, a member of the tropomodulin family of proteins, is critical in thin filament assembly and maintenance; however, its role is under dispute. Using nuclear magnetic resonance data and molecular dynamics simulations, we generated the first atomic structural model of the binding interface between the tropomyosin-binding site of cardiac leiomodin and the N-terminus of striated muscle tropomyosin. Our structural data indicate that the leiomodin/tropomyosin complex only forms at the pointed end of thin filaments, where the tropomyosin N-terminus is not blocked by an adjacent tropomyosin protomer. This discovery provides evidence supporting the debated mechanism where leiomodin and tropomodulin regulate thin filament lengths by competing for thin filament binding. Data from experiments performed in cardiomyocytes provide additional support for the competition model; specifically, expression of a leiomodin mutant that is unable to interact with tropomyosin fails to displace tropomodulin at thin filament pointed ends and fails to elongate thin filaments. Together with previous structural and biochemical data, we now propose a molecular mechanism of actin polymerization at the pointed end in the presence of bound leiomodin. In the proposed model, the N-terminal actin-binding site of leiomodin can act as a “swinging gate” allowing limited actin polymerization, thus making leiomodin a leaky pointed-end cap. Results presented in this work answer long-standing questions about the role of leiomodin in thin filament length regulation and maintenance.

2D-NMR↗

Emulation automation and model checking

A method of automating emulations is provided. The method comprising collecting publicly available network data over a predefined time interval, wherein the collected network data might comprise structured and unstructured data. Any unstructured data is converted into structured data. The original and converted structured data is stored in a database and compared to known network vulnerabilities. An emulated network is created according to the collected network data and the comparison of the structured data with known vulnerabilities. Virtual machines are created to run on the emulated network. Director programs and guest actor programs are run on the virtual machines, wherein the actor programs imitate real user behavior on the emulated network. The director programs deliver task commands to the guest actor programs to imitate real user behavior. The imitated behavior is presented to a user via an interface.

Urias, Vincent↗

Determination of β -decay feeding patterns of 88 Rb and 88 Kr using the Modular Total Absorption Spectrometer at ORNL HRIBF

We report precise determination of ground-state feeding in the β decay of fission products is an important but challenging component in modeling reactor antineutrino flux and reactor decay heat. The Modular Total Absorption Spectrometer (MTAS) is a versatile NaI(Tl) detector array that determines the true β-decay pattern free from the pandemonium effect, including precise ground-state feeding intensities. In this paper, we report MTAS results of the β feeding intensities of 88 Rb and 88 Kr, fission products with large cumulative yields in nuclear reactors. By comparing MTAS results with previous measurements, 88 Rb provides a validation of MTAS's ability to determine ground-state feedings in β decays, while the precision of 88 Kr ground-state feeding is improved when compared with the Evaluated Nuclear Structure Data File (ENSDF). The investigation of sources that contribute to β feeding branching uncertainties in MTAS experiments is discussed in detail. Lastly, the deconvolution of 88 Rb decay spectra suggests that MTAS can distinguish an allowed β spectral shape from a first forbidden unique β spectral shape.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗

Axom

Axom is an open-source library of "building block" software components that provide core infrastructure capabilities for HPC applications. Such capabilities include: input file parsing and verification for various file formats, format specification for material shape input to multi-material simulations and tools for placing those shapes on meshes, parallel distributed coordination of diagnostic messages, geometric primitives, spatial queries and spatial search acceleration data structures, in memory key-value data store for managing simulation data and parallel file I/O. Axom components are used widely across LLNL Advanced Simulation and Computing program applications. It is also part of the LLNL institutionally-supported RADIUSS project, which promotes and funds its adoption by projects across LLNL.

Zagaris, George↗

Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems

Modern industrial systems are now fitted with several sensors for condition monitoring. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault detection, diagnosis, and prognostics. However, the information valuable for performing these tasks is often clouded in noise and must be mined from high-dimensional data structures. Therefore, this dissertation presents a data analytics framework for performing these condition monitoring tasks using high-dimensional data. Demonstrations of this framework are detailed for challenges related to power generation systems in automobiles, power plants, and aircraft engines. These implementations leverage data collected from state-of-the-art, industry class test-rigs. Results indicate the ability of this framework to develop effective methodologies for condition monitoring of complex systems.

Peters, Benjamin↗

Unified architecture for data-driven metadata tagging of building automation systems

This article presents a Unified Architecture (UA) for automated point tagging of Building Automation System (BAS) data, based on a combination of data-driven approaches. Advanced energy analytics applications—including fault detection and diagnostics and supervisory control—have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical BAS implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time-series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: (i) commercial retail buildings and (ii) office buildings from the National Renewable Energy Laboratory (NREL) campus. We report the proposed methodology correctly applied 85–90% and 70–75% of the tags in each of these test scenarios, respectively for two significantly different building types used for testing UA's fully-functional prototype. The proposed UA, therefore, offers promising approach for automatically tagging BAS data as it reaches close to 90% accuracy. Further building upon this framework to algorithmically identify the equipment type and their relationships is an apt future research direction to pursue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Calculating the grain boundary inclination of voxelated grain structures using a smoothing algorithm

We have developed a flexible method for calculating the grain boundary (GB) inclinations of voxelated grain structure data using smoothing algorithms. We compared the performance of four algorithms: the linear interpolation, Allen–Cahn, level-set, and vertex algorithms. We assessed their accuracy using 2D and 3D cases with known inclinations. The vertex algorithm provided the best balance between accuracy and efficiency for 2D structures while the linear interpolation algorithm provided the best balance for 3D structures. We compared the GB inclinations calculated using our smoothing method on a 3D high energy X-ray diffraction microscopy (HEDM) dataset to those determined by meshing the GBs. The two approaches determined similar GB plane distributions, though they varied significantly at triple junctions. In conclusion, the smoothing method was demonstrated for two sources of 3D voxelated grain structures: HEDM data and results from Monte Carlo Potts grain growth simulations.

36 MATERIALS SCIENCE↗

Structure analysis of the telomere resolvase from the Lyme disease spirochete Borrelia garinii reveals functional divergence of its C-terminal domain

Borrelia spirochetes are the causative agents of Lyme disease and relapsing fever, two of the most common tick-borne illnesses. A characteristic feature of these spirochetes is their highly segmented genomes which consists of a linear chromosome and a mixture of up to approximately 24 linear and circular extrachromosomal plasmids. The complexity of this genomic arrangement requires multiple strategies for efficient replication and partitioning during cell division, including the generation of hairpin ends found on linear replicons mediated by the essential enzyme ResT, a telomere resolvase. Using an integrative structural biology approach employing advanced modelling, circular dichroism, X-ray crystallography and small-angle X-ray scattering, we have generated high resolution structural data on ResT from B. garinii. Our data provides the first high-resolution structures of ResT from Borrelia spirochetes and revealed active site positioning in the catalytic domain. We also demonstrate that the C-terminal domain of ResT is required for both transesterification steps of telomere resolution, and is a requirement for DNA binding, distinguishing ResT from other telomere resolvases from phage and bacteria. These results advance our understanding of the molecular function of this essential enzyme involved in genome maintenance in Borrelia pathogens.

59 BASIC BIOLOGICAL SCIENCES↗