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This General Employee Radiological Training (GERT) course is designed to refresh knowledge of basic radiological principles and terms, the risks of exposure to ionizing radiation, LANL radiological hazards and postings, ALARA tools, emergency response responsibilities, and how to obtain personal radiological dose records.
GERT is required by the 10 CFR 835, the Federal Regulation on Radiation Protection that we follow here at LANL. It’s the training for non Rad Workers who may enter Radiological Controlled Areas (RCAs) and Radioactive Material Areas (RMAs) in an unescorted manner. If your work assignment requires access to areas posted for higher radiological hazards like Radiological Buffer Areas (RBAs), Radiation Areas, Contamination Areas, and others you are required to have Rad Worker training. Once you complete this training you will be qualified to access RCAs and RMAs at LANL without an escort. However, you are not qualified to operate radiation producing devices, work with radioactive material, or do work where you may receive greater than 100 mrem of exposure in a year.
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The ENDF/B-VIII.1 library is the newest recommended evaluated nuclear data file by the Cross Section Evaluation Working Group (CSEWG) for use in nuclear science and technology applications, and incorporates advances made in the six years since the release of ENDF/B-VIII.0. Among key advances made are that the 239 Pu file was reevaluated by a joint international effort and that updated 16,18 O, 19 F, 28–30 Si, 50–54 Cr, 55 Mn, 54,56,57 Fe, 63,65 Cu, 139 La, 233,235,238 U, and 240,241 Pu neutron nuclear data from the IAEA coordinated INDEN collaboration were adopted. Over 60 neutron dosimetry cross sections were adopted from the IAEA's IRDFF-II library. In addition, the new library includes significant changes for 3 He, 6 Li, 9 Be, 51 V, 88 Sr, 103 Rh, 140,142 Ce, Dy, 181 Ta, Pt, 206–208 Pb, and 234,236 U neutron data, and new nuclear data for the photonuclear, charged-particle and atomic sublibraries. Numerous thermal neutron scattering kernels were reevaluated or provided for the very first time. On the covariance side, work was undertaken to introduce better uncertainty quantification standards and testing for nuclear data covariances. The significant effort to reevaluate important nuclides has reduced bias in the simulations of many integral experiments with particular progress noted for fluorine, copper, and stainless steel containing benchmarks. Data issues hindered the successful deployment of the previous ENDF/B-VIII.0 for commercial nuclear power applications in high burnup situations. These issues were addressed by improving the 238 U and 239,240,241 Pu evaluated data in the resonance region. The new library performance as a function of burnup is similar to the reference ENDF/B-VII.1 library.
Neutron-induced γ-ray spectra provide key inputs for modern active interrogation applications. A precise modeling of the nuclear reaction and subsequent emission of γ rays is challenging and often impossible due to limitations on evaluated data file formats and nuclear transport simulation codes. We present a framework that addresses these challenges by combining experimental data and reaction-model calculation outputs into an extended candidate version of the Generalized Nuclear Data Structure (GNDS) file, the successor format for the legacy Evaluated Nuclear Data File (ENDF-6). This proposed GNDS hierarchical format contains all the necessary ingredients for inline γ-ray cascade reproduction with event-by-event precision, including continuum–continuum and continuum–discrete transitions following neutron-capture and inelastic neutron scattering reactions. Cascade-event generation based on our approach demonstrates improved energy conservation on an event-by-event basis and permits the use of γ-γ coincidences in applications. This work offers, for the first time, a method to generate neutron-capture and inelastic neutron-scattering γ-ray cascades where energy conservation, correlations, and experimental primaries are fully accounted for.
Phosphate-containing molecules are ubiquitous in nature, where they play crucial roles in biochemical processes. Further, they are of technical importance, for example, in certain batteries and in fuel cells, where a unique property of phosphoric acid is exploited its exceptionally high proton conductivity. Proton transport in phosphoric acid is known to involve proton shuttling; however, the elementary steps involved are not clear. To elucidate the hydrogen bonding preferences of phosphoric acid, we investigate the dihydrogen phosphate anion as well as the deprotonated dimer of phosphoric acid (H 3 PO 4 ·H 2 PO 4 − ) in the gas phase using infrared action spectroscopy in helium nanodroplets and infrared D 2 -tagging photodissociation spectroscopy, and the experimental spectra are compared to theoretical ones. Theory finds for H 3 PO 4 ·H 2 PO 4 − two different structures that are predicted to be nearly isoenergetic. The comparison to the experimental spectra, however, allows for a clear assignment and structure identification. The resulting structure has an interesting binding motif, which might be of relevance to interactions of phosphoric acid in the condensed phase and which can serve as a benchmark for quantum chemical calculations.
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Human activity impacts the evolutionary trajectories of many species worldwide. Global trade of agricultural goods contributes to the dispersal of pathogens reshaping their genetic makeup and providing opportunities for virulence gains. Understanding how pathogens surmount control strategies and cope with new climates is crucial to predicting the future impact of crop pathogens. Here, we address this by assembling a global thousand-genome panel of Zymoseptoria tritici, a major fungal pathogen of wheat reported in all production areas worldwide. We identify the global invasion routes and ongoing genetic exchange of the pathogen among wheat-growing regions. We find that the global expansion was accompanied by increased activity of transposable elements and weakened genomic defenses. Finally, we find significant standing variation for adaptation to new climates encountered during the global spread. Our work shows how large population genomic panels enable deep insights into the evolutionary trajectory of a major crop pathogen.
CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent and hardware-dependent settings. For latest project updates, visit the project website (neurobench.ai).
We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mechanical tunneling based threshold-annealing process. The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing dynamics onto a network of integrate-and-fire neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer and the resulting spiking dynamics replicates the optimal escape mechanism and convergence of SA, particularly at low-temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved benchmark combinatorial optimization problems such as MAX-CUT and Max Independent Set. Across multiple runs, NeuroSA consistently generates distribution of solutions that are concentrated around the state-of-the-art results (within 99%) or surpass the current state-of-the-art solutions for Max Independent Set benchmarks. Furthermore, NeuroSA is able to achieve these superior distributions without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform.
Collective progress towards the Paris Agreement’s temperature targets will be assessed by the Agreement’s Global Stocktake process. Global emission pathways by Integrated Assessment Models (IAMs) are a likely benchmark against which aggregated countries’ greenhouse gas mitigation pledges will be assessed. However, this approach requires comparability, which is currently hampered by a 5 GtCO2yr-1 mismatch between the estimated global land anthropogenic flux of IAMs and countries’ GHG inventories (GHGIs) for 2005-2015. We show that this gap is mostly the result of differences in how the anthropogenic forest sink is defined. Countries define managed forest more broadly than IAMs, and on this larger area typically consider anthropogenic also fluxes due to human-induced environmental change, which IAMs do not include their pathways. Using five IAMs and a dynamic global vegetation model (DGVM), we present and apply a novel method that adjusts IAM results to the countries’ definition, closing the gap with GHGIs at global and regional scale. When expressed in a way compatible with GHGIs, adjusted 1.5°C and well-below 2°C pathways have a cumulative CO2 balance until carbon neutrality that is 110–176 GtCO2 lower than the original IAMs pathways. The use of these country-compatible emission pathways under the Global Stocktake would therefore be essential for an accurate assessment of the collective progress towards the Paris Agreement’s climate goals.
Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system. Remote-sensing estimates to quantify carbon losses from global forests are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced and satellite-derived approaches to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.
Summertime wildfire activity is increasing in boreal forest and tundra ecosystems in the Northern Hemisphere. However, the impact of long range transport and deposition of wildfire aerosols on biogeochemical cycles in the Arctic Ocean is unknown. Here, we use satellite-based ocean color data, atmospheric modeling and back trajectory analysis to investigate the transport and fate of aerosols emitted from Siberian wildfires in summer 2014 and their potential impact on phytoplankton dynamics in the Arctic Ocean. We detect large phytoplankton blooms near the North Pole (up to 82°N in the eastern Eurasian Basin). Our analysis indicates that these blooms were induced by the northward plume transport and deposition of nutrient-bearing wildfire aerosols. We estimate that these highly stratified surface waters received large amounts of wildfire-derived nitrogen, which alleviated nutrient stress in the phytoplankton community and triggered an unusually large bloom event. Our findings suggest that changes in wildfire activity may strongly influence summertime productivity in the Arctic Ocean.
High-resolution NMR spectroscopy enabled us to characterize allosteric transitions between various functional states of the dimeric Escherichia coli Lac repressor. In the absence of ligands, the dimer exists in a dynamic equilibrium between DNA-bound and inducer-bound conformations. Binding of either effector shifts this equilibrium toward either bound state. Analysis of the ternary complex between repressor, operator DNA, and inducer shows how adding the inducer results in allosteric changes that disrupt the interdomain contacts between the inducer binding and DNA binding domains and how this in turn leads to destabilization of the hinge helices and release of the Lac repressor from the operator. Based on our data, the allosteric mechanism of the induction process is in full agreement with the well-known Monod–Wyman–Changeux model.
This study introduces advancements to the numerical solutions employed in the processing of nuclear data for fusion applications. It leverages the convolution theorem and Fourier transform techniques to enhance computational efficiency and broaden applicability. Building upon a previously reported discrete Hankel transform approach for Doppler broadening, this work refines the solution of convolution integrals central to these applications. The methodology provides a general and unified framework for evaluating any convolution operation, regardless of whether the underlying problem involves temperature effects in nuclear reactions. The applicability to the nuclear data processing for fusion is demonstrated by deriving the convolution integrals for some of the fusion-related quantities. As before, the convolution operation utilizes a Gaussian-based kernel; however, the discrete Hankel transform of order $𝛼$ = $\frac{1}{2}$ is now applied to the forward Fourier transform of the nonkernel argument, rather than the inverse Fourier transform. This modification eliminates the need for the integration of the nonkernel, cross section–based function, which is a step that posed challenges for certain pointwise cross-section representations. It also removes the requirement for cross-section linearization. Optimized for graphics processing unit architectures, the approach significantly improves computational performance. These advancements are currently under evaluation as the foundation for the next-generation thermonuclear data file processing codes being developed at Lawrence Livermore National Laboratory.