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We report here the genome sequences of three Aquimarina megaterium strains isolated from the octocoral Eunicella labiata . We reveal a coding potential for versatile carbon metabolism and biosynthesis of natural products belonging to the polyketide, nonribosomal peptide, and terpene compound classes.
The code in this repository was used to model the kinetics of photochemical nitrogen reduction. Each directory represents a manuscript. The directory titled Dahl_CRPS_2025 contains the code used to fit the kinetic models presented in the manuscript titled, "Pre-steady-state kinetics of nanocrystal:molybdenum nitrogenase biohybrids reveals hole-scavenging efficiency is critical to achieving N2 reduction" by Peter J. Dahl, Lauren M. Pellows, Zhi-Yong Yang, Lance C. Seefeldt, John W. Peters, Gordana Dukovic, David W. Mulder, and Paul W. King. Molybdenum (Mo) nitrogenase is a two-component enzyme complex that catalyzes the reduction of dinitrogen to ammonia and protons to hydrogen gas. We have shown that electrons for dinitrogen reduction can be delivered photochemically to the catalytic MoFe protein component by CdS nanocrystals. In this study, we used electron paramagnetic resonance spectroscopy to measure the transient populations of catalytic intermediates. We fit the populations with a pre-steady-state kinetic model which allowed us to distinguish between productive and non-productive reaction pathways and extract the rate constants for the reaction. Our results demonstrated that the rate of catalytic electron delivery into MoFe protein increased with the concentration of sacrificial electron donor. This enabled electron delivery to exceed the rate of hydride protonation, a relaxation pathway which competes with N2 binding. Thus, managing the balance between electron transfer and hole transfer reactions is required to achieve a kinetic regime that favors N2 reduction.
Nuclear reactors that rely upon the fission reaction have two modes of thermal energy deposition in the reactor system: neutron absorption and gamma absorption. The gamma rays are typically generated by neutron absorption reactions or during the fission process which means the primary driver of energy production is of course the neutron interaction. In conventional reactor physics methods, the gamma heating component is ignored such that the gamma absorption is forced to occur at the gamma emission site. For experimental reactor systems like EBR-II and FFTF, the placement of structural pins and assemblies internal to the core leads to problems with power heating predictions because there is no fission power source internal to the assembly to dictate a spatial distribution of the power. As part of the EBR-II support work in the 1980s, the GAMSOR code was developed to assist analysts in calculating the gamma heating. The GAMSOR code is a modified version of DIF3D and actually functions within a sequence of DIF3D calculations. The gamma flux in a conventional fission reactor system does not perturb the neutron flux and thus the gamma flux calculation can be cast as a fixed source problem given a solution to the steady state neutron flux equation. This leads to a sequence of DIF3D calculations, called the GAMSOR sequence, which involves solving the neutron flux, then the gamma flux, then combining the results to do a summary edit. In this manuscript, we go over the GAMSOR code and detail how it is put together and functions. We also discuss how to setup the GAMSOR sequence and input for each DIF3D calculation in the GAMSOR sequence. With the GAMSOR capability, users can take any valid steady state DIF3D calculation and compute the power distribution due to neutron and gamma heating. The MC2-3 code is the preferable companion code to use for generating neutron and gamma cross section data, but the GAMSOR code can accept cross section data from other sources. To further this aspect, an additional utility code was created which demonstrates how to merge the neutron and gamma cross section data together to carry out a simultaneous solve of the two systems.
The goal of this collaborative effort was to develop a data-flow graphics tool (DFG) to visualize the execution of macro-dataflow graphs. LSI incorporated DFG into their graphic user interface to support parallel code development_ and text handling for natural language applications. The CRADA applied LLNL' s expertise in high-performance parallel programming systems based on the dataflow paradigm.
Two independent numerical models have been developed to simulate the behavior of carbon impurities in molten uranium metal. Informed by experimental parameters, one model was created using the commercial software Star-CCM+ and compared with another developed using open-source codes, including OpenFOAM, Finite Element Method Magnetics (FEMM) and a First Passage Kinetic Monte Carlo (FPKMC) approach. The target experimental system features a 404g uranium metal charge containing an average carbon concentration of 139 ppm which was melted in a vacuum induction furnace at 1400° C then resolidified. The microstructures of the uranium and its impurities before and after melting have been characterized and reported separately. Prior to simulating the uranium-carbon system described, the numerical models were validated using a previously published nonradioactive experimental system to ensure agreement with expected output values. Focus has been placed on modeling velocity fields under induction stirring and impurity particle trajectories.
This presentation provides an enlightening discussion of AMPX and ENDF/B-VIII.1. The talk begins with general ENDF issues encountered. Then narrows down into Issues particular to thermal scattering library. Such as the need for clear communication of the intended use for the file and the question of “Which graphite should I use in model?”. The presentation then looks at TSL files with many Bragg peaks are driving a change in SCALE CE library format and transport codes. This lecture concludes with a look at usage of thermal kernels with sharp peaks with the issues encountered using ENDF/B-VIII.0 data and resolved.
Machine Learning (ML) has seen an exponential growth in its applications due to its advanced data driven prediction capabilities. The study presents a data-driven approach as a preliminary attempt to predict the power at which departure from nucleate boiling (DNB) occurs in pressurized water reactors (PWRs) by constructing an advanced ML algorithm that takes outlet pressure, inlet temperature and inlet mass flux as the input features. DNB is a critical heat flux (CHF) phenomenon seen in PWRs. The experimental data from the PWR subchannel and bundle tests (PSBT) benchmark is first used to train an artificial neural network (ANN) to predict the DNB power, which produces a root mean square error (RMSE) of 6.89 kW/m when tested on a blind subset of the PSBT data. Since the PSBT dataset is relatively small to train an accurate ANN, a data augmentation methodology based on generative adversarial networks (GANs) is used to expand the training dataset. By assuming that the real data follows a certain distribution, GANs try to learn that underlying distribution to generate similar synthetic data to augment the database and to improve the predictive capabilities of the ANN. The data generated from GANs are validated using 1-nearest neighbor and kernel maximum mean discrepancy. To further ensure data from GAN is similar to PSBT, the data is tested and filtered out using the sub-channel thermal-hydraulic code CTF. The results indicate that with the addition of 120 data points from GAN the RMSE reduces to 4.84 kW/m showing promising results for future developments.
Pumpout of argon ions by ICRF waves has been observed in C-Mod deuterium L- and I-mode plasmas that had a substantial hydrogen fraction. The effect is manifested by a reduction of core argon x-ray brightness up to a factor of 90% on time scales of tens of milliseconds following injection of ICRF power. For Ar^16+, the pumpout is strongest for hydrogen minority concentrations between 0.25 and 0.4, when the ICRF waves are not expected to result in minority heating. Modeling with the TORIC code suggests that the pumpout process occurs when the H/D mode conversion layer overlaps with the 2nd harmonic impurity resonance layer. The magnitude of the argon pumpout is independent of ICRF power above an apparent threshold of ~500 kW, independent of electron density and appears to decrease as the plasma current is increased. Potential application as a heavy impurity control tool in reactors is discussed.
In the framework of the PROMETRA program, clad hardening constitutive laws (stress-strain curves) have been already implemented in the SCANAIR code. These laws were derived from uniaxial hoop tensile tests performed on narrow machined ring samples. However, they do not reproduce the conditions of strain multi-axiality acting on cladding during a RIA (Reactivity-Initiated Accident) in a PWR. As a consequence, a new specimen called Plane Strain Tensile (PST) similar to the annular sample developed by Pennsylvania State University has been developed at CEA on a wider notched ring. It aims to accurately predict the cladding failure in more prototypical conditions, close to plane strain hoop tension in the middle of the gage section. This paper details the PST tests implementation and results in the framework of the PROMETRA program on fresh and irradiated claddings. The development of this test has led also to the development of a technique to measure local deformations in the gage section of the PST specimen using 2-D Digital Image Correlation (DIC). Tests performed on fresh Zircaloy-4 and on irradiated Zircaloy-4, M5 and ZIRLO specimens are analyzed relying of post-test examinations and finite element simulations. In the case of tests performed on irradiated Zry-4 and ZIRLO at 280 and 350 Celsius degrees, the presence of a hydride rim favors the crack nucleation on the outer surface. In this case, rupture nucleation occurs preferentially in the vicinity of notches. Such tests are not representative of the rupture of a cladding subjected to plane strain loading. Nevertheless, they give a good representation of the initiation of rupture observed during RIA on irradiated cladding, resulting in a hydride damage. Zry-4 specimens tested at 480 Celsius degrees and M5TM at 280 and 350 C. degrees result in a ductile fracture nucleation in the center of the specimen. This rupture is prototypical of a ductile rupture of an irradiated specimen subjected to a plane strain loading. However, the nucleation on inner surface by shear at the mandrel contact is not prototypical of a clad rupture during the PCMI (Pellet Clad Mechanical Interaction) phase of a RIA (membrane stresses in the clad thickness in RIA). An important lesson learned from this experimental approach is that the PST test is a particularly complex test, both in its implementation and interpretation.
Pumpout of argon ions by ICRF waves has been observed in C-Mod deuterium L- and I-mode plasmas that had a substantial hydrogen fraction. The effect is manifested by a reduction of core argon x-ray brightness up to a factor of 90% on time scales of tens of milliseconds following injection of ICRF power. For Ar 16+ , the pumpout is strongest for hydrogen minority concentrations between 0.25 and 0.4, when the ICRF waves are not expected to result in minority heating. Modeling with the TORIC code suggests that the pumpout process occurs when the H/D mode conversion layer overlaps with the 2nd harmonic impurity resonance layer. Here, the magnitude of the argon pumpout is independent of ICRF power above an apparent threshold of ~500 kW, independent of electron density and appears to decrease as the plasma current is increased. Potential application as a heavy impurity control tool in reactors is discussed.
The tokamak is a type of toroidal device used to confine a fusion plasma using large magnetic fields. Tokamaks and stellarators the leading devices for confining plasmas for fusion, and the capability to predict performance in these magnetically confined plasmas is essential for developing a sustainable fusion energy source. The magnetic configuration of tokamaks and stellarators does not exist in Nature, yet, the fundamental processes governing transport in fusion plasmas are universal – turbulence and instabilities, driven by inhomogeneity and asymmetry in the plasma, conspire to transport heat and particles across magnetic field lines and can play critical roles in impurity confinement and generation of intrinsic rotation. Turbulence exists in all plasmas, and in neutral fluids as well. The study of turbulence is essential to developing a fundamental understanding of the nature of the fourth state of matter, plasmas. Experimental studies of turbulence in tokamaks date back to early scattering observations from the late 1970s. Since that time, great advances in turbulence diagnostics have been made, all of which have significantly enhanced our knowledge and understanding of turbulence in tokamaks. Through comparisons with advanced gyrokinetic theory and turbulent-transport models a great deal of evidence exists to implicate turbulent-driven transport as an important mechanism determining transport in all channels: heat, particle and momentum However, prediction and control of turbulent-driven transport remains elusive. Key to development of predictive transport models for magnetically confined fusion plasmas is validation of the nonlinear gyrokinetic transport model, which describes transport due to turbulence. Validation of gyrokinetic codes must include detailed and quantitative comparisons with measured turbulence characteristics, in addition to comparisons with inferred transport levels and equilibrium profiles. For this reason, advanced plasma diagnostics for studying core turbulence are needed in order to assess the accuracy of gyrokinetic models for turbulent-driven particle, heat and momentum transport. New core turbulence diagnostics at the world-class tokamaks Alcator C-Mod at MIT and ASDEX Upgrade at the Max Planck Institute for Plasma Physics have been designed, developed, and operated over the course of this project. These new instruments are capable of measuring electron temperature fluctuations and the phase angle between density and temperature fluctuations locally and quantitatively. These new data sets from Alcator C-Mod and ASDEX Upgrade are being used to fill key gaps in our understanding of turbulent transport in tokamaks. In particular, this project has results in new results on the topics of the Transport Shortfall, the role of ETG turbulence in tokamak plasmas, profile stiffness, the LOC/SOC transition, and intrinsic rotation reversals. These data are used in a rigorous process of “Transport model validation”, and this group is a world-leader on using turbulence models to design new hardware and new experiments at tokamaks. A correlation electron cyclotron emission (CECE) diagnostic is an instrument used to measure micro-scale fluctuations (mm-scale, compared to the machine size of meters) of electron temperature in magnetically confined fusion plasmas, such as those in tokamaks and stellarators. These micro-scale fluctuations are associated with drift-wave type turbulence, which leads to enhanced cooling and mixing of particles in fusion plasmas and limits achieving the required temperatures and densities for self-sustained fusion reactions. A CECE system can also be coupled with a reflectometer system that measured micro-scale density fluctuations, and from these simultaneous measurements, one can extract the phase between the density (n) and temperature (T) fluctuations, creating an nT phase diagnostic. Measurements of the fluctuations and the phase angle between them are extremely useful for testing and validating predictive models for the transport of heat and particles in fusion plasmas due to turbulence. Once validated, the models are used to predict performance in ITER and other burning plasmas, such as the MIT ARC design. Most recently, data from the newly developed, so-called “CECE diagnostic” [Cima 1995, White 2008] and “nT phase angle measurements” [Haese 1999, White 2010] ]will be combined with data from density fluctuation diagnostics at ASDEX Upgrade to support a long-term program of physics research in turbulence and transport that will allow for more stringent testing and validation of gyrokinetic turbulent-transport codes. This work directly impacts the development of predictive transport models in the U.S. FES program, such as TGLF, developed by General Atomics, which are used to predict performance in ITER and other burning plasma devices as part of advancing the development of fusion energy sciences.
The SolarSMART 2020 Georgia Survey dataset provides results from a survey of 1,544 residents of the State of Georgia above the age of 18 on their perceptions of photovoltaic solar energy. The survey includes the following: demographic information for each respondent, their location in the state (by ZIP Code); the type of house they reside within; whether they have or have not adopted solar energy (and if so, how did they adopt solar energy); their personal views and perceptions of solar energy both for themselves and within their community; and their perceptions of who within Georgia and across the United States would or would not be inclined to adopt solar. Respondents were asked to provide their responses to questions addressing the following issues as they pertained to both themselves and their perceptions of others (Georgians and Americans writ large): (a) perception of how home type shape solar adoption; (b) perceptions of personal values (interpersonal, political, environmental); (c) the design of solar and the marketing of it; (d) financial dimensions of adopting solar energy; (d) and favorability towards new technologies. This data was collected via an online survey through a third-party distributor between June and September of 2020. Respondents were from 151 of the 159 counties of the State of Georgia, with representation of both urban and rural residents. In total, the dataset includes 171 variables, with one being an open-ended question coded by the research team. Variables include demographic characteristics (personal, housing, location), multiple-choice single answer, ranked-choice responses to different statements about solar, and a series of two digital map exercises where respondents highlighted geographic regions (states or parts of Georgia) based on whether the believe residents there do, do not, or they do not know if the respondents do or do not adopt solar.
The objectives of the vadose modeling for the updated Hanford Site composite analysis (CA) are to simulate the flow and transport of water and radionuclide releases from the surface to the water table and to provide radionuclide transfer rates for the plateau to river (P2R) model, version 8.3 (CP-57037, Model Package Report: Plateau to River Groundwater Model, Version 8.3). Water additions include natural recharge and water discharged to the ground as a result of industrial processes associated with Hanford Site operations. Contaminant sources include radionuclides in water discharged to the ground during operations and radionuclides disposed “dry” in solid waste burial grounds or other means. The following 16 radionuclides were selected for this modeling effort: carbon-14 (C-14), chlorine-36 (Cl-36), tritium (H-3), iodine-129 (I-129), neptunium-237 (Np-237), rhenium-187 (Re-187), strontium-90 (Sr-90), technetium-99 (Tc-99), uranium-232 (U-232), uranium-233 (U-233), uranium-234 (U-234), uranium-235 (U-235), uranium-236 (U-236), uranium-238 (U-238), radium-226 (Ra-226), and thorium-230 (Th-230). The simulation time starts in 1943 and ends at 12070, which is 10,000 years after assumed Hanford Site closure in 2070. The parallel version of the Subsurface Transport Over Multiple Phases (STOMP1) simulator, officially named the exascale Subsurface Transport Over Multiple Phases (eSTOMP), is used to simulate flow and transport for the vadose models. The documentation for the STOMP code is comprehensive. The theoretical and numerical approaches applied in the STOMP code are documented in a published theory guide (PNNL-12030, STOMP Subsurface Transport Over Multiple Phases Version 2.0 Theory Guide). The code has undergone a rigorous verification procedure against analytical solutions, laboratory-scale experiments, and field-scale demonstrations. The application guide (PNNL-11216, STOMP Subsurface Transport Over Multiple Phases Application Guide) provides instructive examples in the application of the code to classical groundwater problems. The user’s guide (PNNL-15782, STOMP: Subsurface Transport Over Multiple Phases Version 4.0: User’s Guide) describes the general use, input file formatting, compilation, and execution of the code. The primary output of the vadose zone modeling is radionuclide transfer rates to the groundwater for input into the P2R model. The rates will be summed over the 100 by 100 m P2R grid cells that fall within the vadose zone model source domain.
Purpose: To analyze all recalls involving radiation oncology devices (RODs) from the US Food and Drug Administration (FDA)'s recall database, comparing these with non–radiation oncology device recalls to identify discipline-specific trends that may inform improvements in device safety. Methods and Materials: Recall data on RODs from 2002 to 2015 were sorted into 4 product categories (external beam, brachytherapy, planning systems, and simulation systems). Outcomes included determined cause of recall, recall class (severity), quantity in commerce, time until recall termination (date FDA determines recall is complete), and time since 510(k) approval. Descriptive statistics were performed with linear regression of time-series data. Results for RODs were compared with those for other devices by Pearson χ{sup 2} test for categorical data and 2-sample Kolmogorov-Smirnov test for distributions. Results: There were 502 ROD recalls and 9534 other class II device recalls during 2002 to 2015. Most recalls were for external beam devices (66.7%) and planning systems (22.9%), and recall events peaked in 2011. Radiation oncology devices differed significantly from other devices in all recall outcomes (P≤.04). Recall cause was commonly software related (49% vs 10% for other devices). Recall severity was more often moderate among RODs (97.6% vs 87.2%) instead of severe (0.2% vs 4.4%; P<.001). Time from 510(k) market approval to recall was shorter among RODs (P<.001) and progressively shortened over time. Radiation oncology devices had fewer recalled devices in commerce than other devices (P<.001). Conclusions: Compared with other class II devices, RODs experience recalls sooner after market approval and are trending sooner still. Most of these recalls were moderate in severity, and software issues are prevalent. Comprehensive analysis of recall data can identify areas for device improvement, such as better system design among RODs.
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Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional reconstruction-based techniques either use simplifying assumptions or require specialized diagnostics to infer high-dimensional (> $2D$) beam properties. In this Letter, we introduce a general-purpose algorithm that combines neural networks with differentiable particle tracking to efficiently reconstruct high-dimensional phase space distributions without using specialized beam diagnostics or beam manipulations. Furthermore, we demonstrate that our algorithm accurately reconstructs detailed 4D phase space distributions with corresponding confidence intervals in both simulation and experiment using a single focusing quadrupole and diagnostic screen. This technique allows for the measurement of multiple correlated phase spaces simultaneously, which will enable simplified 6D phase space distribution reconstructions in the future.
Purpose: The goal of this study was to evaluate the repeatability of radiomics features (intensity, shape and heterogeneity) in both PET and low-dose CT components of test-retest FDG-PET/CT images in a prospective multicenter cohort of 74 NSCLC patients from ACRIN 6678 and a similar Merck trial. Methods: Seventy-four patients with stage III-IV NCSLC were prospectively included. The primary tumor and up to 3 additional lesions per patient were analyzed. The Fuzzy Locally Adaptive Bayesian algorithm was used to automatically delineate metabolically active volume (MAV) in PET. The 3D SlicerTM software was exploited to delineate anatomical volumes (AV) in CT. Ten intensity first-order features, as well as 26 textural features and four 3D shape descriptors were calculated from tumour volumes in both modalities. The repeatability of each metric was assessed by Bland-Altman analysis. Results: One hundred and five lesions (primary tumors and nodal or distant metastases) were delineated and characterized. The MAV and AV determination had a repeatability of −1.4±11.0% and −1.2±18.7% respectively. Several shape and heterogeneity features were found to be highly or moderately repeatable (e.g., sphericity, co-occurrence entropy or intensity size-zone matrix zone percentage), whereas others were confirmed as unreliable with much higher variability (more than twice that of the corresponding volume determination). Conclusion: Our results in this large multicenter cohort with more than 100 measurements confirm the PET findings in previous studies (with <30 lesions). In addition, our study is the first to explore the repeatability of radiomics features in the low-dose CT component of PET/CT acquisitions (previous studies considered dosimetry CT, CE-CT or CBCT). Several features were identified as reliable in both PET and CT components and could be used to build prognostic models. This work has received a French government support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX-07-01, and support from the city of Brest.