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In this article , nuclear structure data for 15 known nuclides of mass number 175 (Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Re, Os, Ir, Pt, Au, Hg) have been evaluated, which supersedes the earlier work done by M. Shamsuzzoha Basunia (2004Ba89), published in Nuclear Data Sheets 102, 719 (2004).
Experimental nuclear structure data from various reactions and decays are compiled and evaluated for all known nuclides with mass number A=48 (S, Cl, Ar, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni). For each nuclide, detailed evaluated nuclear structure information is presented for each individual reaction and decay, and the best values combining all available data are recommended for spectroscopic properties, such as level energies, half-lives, γ-ray energies and intensities, decay radiations. No excited states have been identified in 48 S, 48 Cl, 48 Co and 48 Ni, among which the first three even have no measured ground-state half-lives and decay modes. For 48 Fe, a level scheme with a sequence of excited states was established for the first time from a recent one-neutron removal measurement by 2021Ya33. Data for excited states in 48 Ar, 48 K and 48 Mn remain limited; no decay scheme has been measured for 48 Ar and 48 K yet, while the decay scheme for 48 Ni is incomplete due to unobserved levels. 48 Ti is the most studied nuclide through various reactions and decays, followed by 48 V, 40 Ca, 48 Sc, and 48 Cr, among which no decay event to 48 Sc has been observed and the decay schemes for 48 V and 48 Cr are considered as incomplete. Furthermore, this work supersedes the previous full evaluations of A=48: 2006Bu08, 1993Bu04, 1985Al14, 1978Be01.
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.
Big Data in nuclear comes in the form of many structures, systems, components, data streams, data types, and more.
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.
This report summarizes the data structures developed for the demonstration version of UUDEX. They serve as an initial proposal for data structures to be proposed for standardization.
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.
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.
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.
Abstract Kokanee Oncorhynchus nerka growth is often density dependent; thus, proper management of kokanee populations necessitates an understanding of population dynamics using age structure data. To date, no calcified structures have been validated for kokanee. We compared the accuracy (i.e., the percentage of reconciled age estimates that matched the known ages of fish) and precision (i.e., the percentage of fish for which complete agreement was achieved on age estimates among all readers) of aging estimates for scales, sectioned otoliths, and sectioned pectoral fin rays from 455 known-age kokanee (ages 0–4) collected from five lentic waters in Idaho. Across all waters combined, mean weighted accuracy and precision were similar for scales (86% and 70%, respectively), fin rays (83% and 65%), and otoliths (82% and 65%), with no significant differences between structures. However, among water bodies, accuracy and precision of each calcified structure varied considerably. For example, scales were the most accurate and precise structure and otoliths were the least accurate and precise structure at one water body, while otoliths were the most accurate and precise structure and scales were the least accurate structure at two water bodies. Fin rays were the least precise structure at four of the five study waters, but were the least accurate structure for only one water body. Individual reader accuracy was most affected by fish age and water body, and older fish (age 3 and older) were consistently assigned incorrect ages regardless of the water body or the calcified structure. Taken collectively, all three structures produced satisfactory aging accuracy and precision for kokanee, but no structure was unequivocally best; at any individual water body, annual growth and local environmental conditions appeared to influence the readability of calcified structures.
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.
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.
Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.
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.
The idea of combining a relational database with concepts associated with blockchain technology is being examined. The use case outlined herein concurrently utilizes relational and blockchain data structures to facilitate the sharing of complex data among untrusted users without the participation of a trusted third party. By capturing the transactions that result in data being stored in a relational database and placing those transactions in a blockchain structure, users can be assured that the information has not been created, altered, or destroyed without the knowledge of all users of the data. Capturing relational database transactions via a blockchain structure presents several challenges. Among them are increased storage capacity requirements, transaction ordering prior to placement on the blockchain within a multiuser database, use of existing or creation of new consensus mechanisms, and timely data verification. Addressing these challenges, although not unsurmountable, will introduce trade-offs (e.g., increased system sizing, slower transaction execution) that might or might not be acceptable based upon the envisioned use. If these trade-offs are acceptable, then this concept of extending the blockchain is a powerful method to provide both applications and their users immutable, trusted, well-defined, and consistent data.
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.
Experimental nuclear structure data from various reactions and decays have been evaluated for all known nuclides of mass 39 (Mg, Al, Si, P, S, Cl, Ar, K, Ca, Sc, Ti). Detailed evaluated nuclear structure information is presented for each reaction and decay and the best values combining all available data are recommended for level energies, half-lives, γ-ray energies and intensities, decay properties (energies, intensities and placement of radiations), and other spectroscopic data. The {sup 39}K, {sup 39}Ca and {sup 39}Ar are the most extensively studied nuclides; no excited states are known in {sup 39}Mg and {sup 39}Ti. This work supersedes the earlier full evaluation of A=39 by B. Singh and J. Cameron (2006Si02).
Evaluation of likelihood functions for cosmological large scale structure data sets (including CMB, galaxy redshift surveys, etc.) naturally involves marginalization, i.e., integration, over an unknown underlying random signal field. Recently, I showed how a renormalization group method can be used to carry out this integration efficiently by first integrating out the smallest scale structure, i.e., localized structure on the scale of differences between nearby data cells, then combining adjacent cells in a coarse graining step, then repeating this process over and over until all scales have been integrated. Here I extend the formulation in several ways in order to reduce the prefactor on the method's linear scaling with data set size. The key improvement is showing how to integrate out the difference between specific adjacent cells before summing them in the coarse graining step, compared to the original formulation in which small-scale fluctuations were integrated more generally. I suggest some other improvements in details of the scheme, including showing how to perform the integration around a maximum likelihood estimate for the underlying random field. In the end, an accurate likelihood computation for a million-cell Gaussian test data set runs in two minutes on my laptop, with room for further optimization and straightforward parallelization.