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At least 163 records · Page 9

Hierarchical Gaussian Random Field Sampling for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and the Karhunen–Loève Decomposition

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRFs) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equations (SPDEs), often reduce the dimensionality of the sample space at the cost of accuracy of inference. Other approaches, such that those based on Karhunen-Loève (KL) expansions, offer sample space dimensionality reduction but sacrifice GRF representation accuracy and ergodicity of the Markov chain Monte Carlo (MCMC) sampler and are computationally expensive for high-dimensional problems. The proposed method integrates the dimensionality reduction capabilities of KL expansions with the scalability of SPDE-based sampling, thereby providing a robust, unified framework for high-dimensional uncertainty quantification (UQ) that is scalable and accurate, preserves ergodicity, and offers dimensionality reduction of the sample space. The hierarchy in our multilevel algorithm is derived from the geometric multigrid hierarchy. By constructing a hierarchical decomposition that maintains the covariance structure across the levels in the hierarchy, the approach enables efficient coarse-to-fine sampling while ensuring that all samples are drawn from the desired distribution. The effectiveness of the proposed method is demonstrated on a benchmark subsurface flow problem, demonstrating its effectiveness in improving computational efficiency and statistical accuracy. Furthermore, our proposed technique is more efficient and accurate and displays better convergence properties than existing methods for high-dimensional Bayesian inference problems.

Gaussian random fields↗

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. (In homophilic graphs vertices of the same class are more likely to be connected, and vertices of different classes tend to be linked in heterophilic graphs.) While GNNs have been successfully applied to homophilic graphs, their application to heterophilic graphs remains challenging. The best-performing GNNs for heterophilic graphs do not fit the sampling paradigm, suffer high computational costs, and are not inductive. We employ samplers based on feature-similarity and feature-diversity to select subsets of neighbors for a node, and adaptively capture information from homophilic and heterophilic neighborhoods using dual channels. Currently, AGS-GNN is the only algorithm that we know of that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, which was not used in this context prior to our work. The sampling distribution is pre-computed and highly parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (<=100K nodes) and large (>100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compare to the current approaches in the literature. AGS-GNN achieves comparable test accuracy to the best-performing heterophilic GNNs, even outperforming methods using the entire graph for node classification. AGS-GNN also converges faster compared to methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.

artificial intelligence↗

Turbidity and suspended sediment data for Gwynns Falls, Baisman Run, and Pond Branch, Baltimore County and Baltimore City, MD, USA

This resource includes turbidity and suspended sediment data collected at two sampling stations located on Gwynns Falls in Baltimore County, MD, USA. In addition, two forested reference sites, Baisman Run and Pond Branch at Oregon Ridge, and two urban sites, Dead Run and Maiden's Choice Run (tributaries to Gwynns Falls), were sampled in Baltimore County and Baltimore City, MD, USA. Turbidity sensor data were collected at a 5-minute frequency using YSI EXO2 sondes. Suspended sediment was collected using ISCO samplers for the purpose of establishing correlations between turbidity and suspended sediment concentration. The six sites are co-located with USGS stream gages. This resource is part of the Baltimore Social-Environmental Collaborative Urban Integrated Field Laboratory supported by Department of Energy as well as the Critical Zone Collaborative Network supported by National Science Foundation. This resource includes a technical report summarizing the findings.

58 GEOSCIENCES↗

Parameter Optimization Toolbox for NS-3 network optimization, NS-3 Parameter Optimization Framework [SWR-18-60]

This simulation-based parameter optimization framework is proposed to tune parameters of different types of communication networks using ns-3 to achieve the optimal network performance. It consists of three main components: an ns-3 packet reporting module; a sampler running simulations with all possible parameter sets for the input parameter variables by using a parallel executor at each generation; and a hybrid optimization algorithm for tuning configurable parameters of hybrid designs and application parameter variables. The proposed hybrid metaheuristic optimization algorithm combines an evolutionary algorithm with a gradient descent function to quickly achieve an approximate globally optimum solution. This software is designed to be used in a multi-core processing Linux environment and run over a long duration of time. The execution time varies depending mainly upon the nature of the ns-3 configuration being simulated. This software includes a custom ns-3 QoS measurement application which must be included with the ns-3 source code during installation of the software.

Hasandka, Adarsh↗

BIhNNs

The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural Networks (DNNs), Neural ODEs, and Symplectic Neural Networks (SympNets) used with state-of-the-art sampling schemes like Hamiltonian Monte Carlo (HMC) and the No-U-Turn-Sampler (NUTS).

Dhulipala, Som↗

LFIS

Repository of the codes developed for the paper titled "Liouville Flow Importance Sampler"

Tian, Yifeng↗

NERSC_Lightweight Distributed Metric Service (NERSC_LDMS) v4.4.2

Miscellany This LDMS Loftsman/Helm Chart horizontally scales LDMS daemons in order to achieve a 1Hz sample rate from over 5,000 nodes, collecting 38k metrics per minute on Perlmutter. This LMDS Configuration relies on already running `ldmsd` producers running on nodes, which produce metrics via sampler plugins. The Helm chart distributes the collection of metrics from producer acrross many aggregator and storage `ldmsd` daemons, ensuring no damon is overloaded and data loss is avoided.

Stile, John [Lawrence Berkeley National Laboratory↗

Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. Furthermore, the combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

54 ENVIRONMENTAL SCIENCES↗

Validation of KENO Delayed Neutron Fraction Capabilities

The delayed neutron fraction, which is necessary for the safe operation of reactors, varies by fissionable material and neutron spectrum. A prompt fission neutron spectrum has been included in the SCALE 6.2 release to account for the removal of delayed neutrons from precursor materials from the neutron balance. This paper describes how experimental measurements from criticality safety benchmarks were used to validate the SCALE calculational procedure for the delayed neutron fraction. Additionally, the effect of nuclear data uncertainty on the KENO calculation of β eff was examined with the SAMPLER sequence.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Wet and dry plastic deposition data for western US National Atmospheric Deposition Program sites (2017-2019)

Raw plastic counts and calculated plastic deposition rates for 11 National Atmospheric Deposition Program (NADP) sites in the western USA recorded from 2017-2019. Samples were collected to determine plastic deposition rates in remote locations and identify the potential sources of plastic to the atmosphere. Samples were collected using an Aerochem Metrics Wet and Dry sampler. Wet deposition refers to the material retained on a 0.45-micron filter. Dry deposition refers to the dry only deposition collected using a Dry Side Insert (DSI) (Brahney et al. 2020). Plastics are identified as either fibers or particles (includes small colored films). Wet deposition rates are recorded weekly and dry plastic deposition rates are based on monthly sampling intervals. Sites included are Grand Canyon National Park (AZ03), Joshua Tree National Park (CA67), Rocky Mountain National Park (CO98), Niwot Ridge (CO02), Ashley National Forest (UT95), East River SFA (CO10)), Craters of the Moon National Park (ID03), Great Basin National Park (NV05), Canyonlands National Park (UT09), Bryce Canyon Nationa Park (UT99), and Wind River Range (WY06). Data collection is being published in collaboration with the Department of Energy's research project, "Constraining physical understanding of aerosol loading, biogeochemistry, and snowmelt hydrology from hillslope to watershed scale in the East River Scientific Focus Areas.

54 ENVIRONMENTAL SCIENCES↗

Effect of fluctuating redox conditions on trace metal release from wetland soils from South Carolina and stream sediments from Tennessee

Natural aquatic systems undergo fluctuating redox conditions due to microbial activity, varying water saturation levels, and nutrients dynamics. With fluctuating oxic and anoxic conditions, trace metals can be mobilized or sequestered in response to changes in iron and sulfur speciation and the concentrations and lability of organic carbon. We conducted a systematic laboratory-based microcosm study to examine the effect of redox fluctuations on trace metal mobility in samples collected from two different natural aquatic systems: riparian wetlands and a stream. We incubated water-saturated soils under three cycles of anoxic-oxic conditions (τanoxic:τoxic = 3) spanning 24 days and monitored the change in dissolved and bioavailable metal (copper (Cu), nickel (Ni), zinc (Zn), cobalt (Co), iron (Fe), and manganese (Mn)) concentrations. The dataset includes the results of the variation in metal concentrations due to redox fluctuations in wetland soils and stream sediments. Additionally, the variation in the bioavailable concentration of metals obtained using passive samplers based on diffuse gradient in thin-films technique is included in the dataset. We have also reported the change in the pH values and dissolved oxygen concentrations in the microcosms during the entire study of 24 days. The dataset also contains the dissolved organic carbon and sulfate concentrations, and the data on fractions of metals bound to reducible and oxidizable phases. For materials from both the wetlands and the stream, anoxic conditions favored Co and Zn release, which corresponded with the reductive dissolution of iron oxides. In contrast, dissolved Cu concentrations increased under oxic conditions for both sites and correlated positively with the release of sulfate. In wetland soils, dissolution of Fe (hydr)oxides increased Ni solubility; however, in stream sediments, Ni release occurred when sulfides or organic matter were oxidized. For stream sediments, each subsequent redox cycle increased the bioavailability of trace metals. Upon redox fluctuations in wetland soils, the bioavailability of Zn and Cu increased, whereas bioavailable Ni and Co decreased. This study illustrates that different trace metals display distinct bioavailability patterns during oxic-anoxic fluctuations in natural environments. The biogeochemical cycling of carbon and nitrogen in systems with redox fluctuations may be influenced by these patterns in trace metal availability in addition to the availability of electron donors and acceptors.

54 ENVIRONMENTAL SCIENCES↗

Plant Characteristics, Porewater, Gas Flux, and Soil Biogeochemistry at Council Road Site Mile Marker 71, Seward Peninsula, Alaska, 2023

Data collected at Council, AK (64°51’35.0”N 163°41’59.1”W) during a summer campaign in 2023. Water data consists of soil porewater collected by centrifuging soil cores and also by field collection with porewater samplers (rhizons). Gas data consists of CO2, CH4 and N2O surface soil fluxes measured with a portable FTIR analyzer. Plant and root data consists of biomass, root length, diameter and mass. Soil data consists of total C and N. Air, water and soil samples span two main locations: a thermokarst wetland and an upland tussock. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).This dataset was generated to broadly address the following research question: how will climate change (i.e., thawing permafrost, landscape change) alter the ecosystem flux (sink versus source) of important greenhouse gases such as CO2, CH4 and N2O?Description of the contents of this data package: This dataset contains 5 different individual .csv files containing plant, soil, water and gas data. No software is needed to utilize them. PFTCover: Plant functional type ground cover in 1x1 meter plots. SoilCores: Solidphase and porewater phase soil biogeochemical variablesPlantData: Above and belowground plant traits. GasFlux: Surface plant-soil gas measurementsFieldPorewater: Field collected porewater biogeochemical variables

54 ENVIRONMENTAL SCIENCES↗

Greenhouse Rhizobox Experiment with Plant Characteristics, Porewater, Gas Flux, and Soil Biogeochemistry data, Seward Peninsula, Alaska, 2024

Data collected from a greenhouse rhizobox experiment (2024) using soils and plants collected at Council, AK (64°51’35.0”N 163°41’59.1”W) during a summer campaign in 2023. Water data consists of soil porewater collected by porewater samplers (rhizons). Gas data consists of CO2 and CH4 surface soil fluxes measured with an FTIR (Fourier-transformed infrared red) analyzer. Plant and root data consists of biomass, root length. This study is a part of The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).This dataset was generated to broadly address the following research question: how will climate change (i.e., thawing permafrost, landscape change) alter the ecosystem flux (sink versus source) of important greenhouse gases such as CO2 and CH4?Description of the contents of this data package: Rhizobox2024_Data.csv: This dataset contains plant, water and gas data. No software is needed to utilize them.nga535_flmd.csv: The file contains file level metadatanga535.dd.csv: This file contains the data dictionaryMethods.pdf: This file contains the data collection methods

54 ENVIRONMENTAL SCIENCES↗

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗

DES Y3 + KiDS-1000: Consistent cosmology combining cosmic shear surveys

We present a joint cosmic shear analysis of the Dark Energy Survey (DES Y3) and the Kilo-Degree Survey (KiDS-1000) in a collaborative effort between the two survey teams. We find consistent cosmological parameter constraints between DES Y3 and KiDS-1000 which, when combined in a joint-survey analysis, constrain the parameter $S_8 = \sigma_8 \sqrt{\Omega_{\rm m}/0.3}$ with a mean value of $0.790^{+0.018}_{-0.014}$. The mean marginal is lower than the maximum a posteriori estimate, $S_8=0.801$, owing to skewness in the marginal distribution and projection effects in the multi-dimensional parameter space. Our results are consistent with $S_8$ constraints from observations of the cosmic microwave background by Planck, with agreement at the $1.7\sigma$ level. We use a Hybrid analysis pipeline, defined from a mock survey study quantifying the impact of the different analysis choices originally adopted by each survey team. We review intrinsic alignment models, baryon feedback mitigation strategies, priors, samplers and models of the non-linear matter power spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗

Nitrate Leaching from Continuous Corn, Perennial Grasses, and Poplar in the US Midwest

Leaching from annual corn ( Zea mays L.) crops is a primary source of nitrate (NO 3 - ) pollution of ground and surface waters. Here, we compare NO 3 - losses from no-till corn with losses from various alternative perennial cropping systems (switchgrass [ Panicum virgatum L.], miscanthus [ Miscanthus ×giganteus J.M. Greef & Deuter ex Hodkinson & Renvoiz], a native grass mixture, and restored prairie), as well as hybrid poplar ( Populus nigra L. × P. maximowiczii A. Henry ‘NM6’), all grown on a well-drained soil in Michigan. Soil water was sampled from below the root zone using suction cup samplers during nonfrozen periods (March–November) between 2009 and 2016. Leaching was estimated from NO 3 - concentrations in soil water and modeled drainage (percolation) rates. Drainage rates were not significantly different among crops, constituting ~30% of total annual precipitation. Aboveground net primary production (Mg ha -1 yr -1 ) averaged across the 7 yr was highest in poplar (30.8 ± 1.9 [SE]) followed by miscanthus (23.9 ± 2.4) and corn (20.4 ± 0.9). Volume-weighted mean NO 3 - concentrations (mg N L -1 ) and NO 3 - leaching (kg ha -1 yr -1 ) averaged across the 7 yr were 9.2 and 34.1, 2.3 and 5.9, and 3.0 and 7.2, respectively, for corn, perennial grasses and poplar. Approximately 10 to 32% of applied N was lost as NO 3 - from these crops, with the highest percent losses from poplar (32%) followed by corn (20%). Perennial cropping systems leached considerably more NO 3 - in first few years after planting, but over 7 yr they lost much less NO 3 - than corn. Perennial crops may therefore help ameliorate NO 3 - pollution in agricultural landscapes even if they receive modest N fertilization.

54 ENVIRONMENTAL SCIENCES↗

IAEA Coordinated Research Project on HTGR Physics, Thermal Hydraulics, and Depletion Uncertainty Analysis: PHISICS/RELAP5-3D Results for the Phase III Coupled Core Exercises

This report details the Parallel and Highly Innovative Simulation for INL Code System (PHISICS)/Reactor Excursions and Leak Analysis Program (RELAP5)-3D results obtained for the neutronics stand-alone core exercises defined for Phase II of the International Atomic Energy Agency (IAEA) Coordinated Research Project (CRP) on high-temperature gas cooled reactor (HTGR) uncertainty analysis in modeling (UAM). The Phase II models and results are linked to the earlier Standardized Computer Analyses for Licensing Evaluation (SCALE)/Sampler/New ESC-based Weighting Transport (NEWT) data generated for the lattice physics (lattice) stage Phase I of the CRP. The focus of this report is the propagation of cross-section uncertainties from the lattice to the core calculation (core) phase of the prismatic 350 megawatt (MW) General Atomics (GA) Modular HTGR (MHTGR). The Phase II neutronics exercises consist of two sub-cases (II-2a and II-2b) specified for the fresh and mixed core loading of the MHTGR-350 prismatic high-temperature reactor design. Exercises II-2a and II-2b are performed without temperature feedback using isothermal temperature profiles. For the nominal lattice physics (i.e., best-estimate) model, a series of fresh and depleted single block and super cell SCALE/KENO and SCALE/NEWT models were developed at Idaho National Laboratory (INL) to assess the impact of spectral effects on the few-group cross sections. A combination of these lattice cells were used in a super cell calculation to construct 8-group cross-section libraries for use in the INL-developed PHISICS/RELAP5-3D code suite. The block-level homogenized libraries were assigned to the 220 fuel zones utilized in the MHTGR-350 PHISICS core model, according to the fresh and mixed fresh/depleted core loading pattern. In order to evaluate the effect of uncertainty propagation, several sets consisting of 1,000 perturbed NEWT lattice and PHISICS/RELAP5-3D core calculations were performed using the SCALE 6.2 ENDF/B-VII.1 cross-section co-variance data and the stochastic sampling module Sampler in SCALE 6.2.0. The Figures of Merit (FOM) investigated include the mean and standard deviation uncertainty values obtained for the eigenvalues, control rod worth values, block-averaged power profiles, power axial offsets, and isothermal temperature feedback coefficients. A sensitivity study on the main nuclide reaction contributors to the FOM uncertainties has not yet been performed since the SCALE/PHISICS/RAVEN coupling with ENDF/B-VII co-variance data is still being developed. It was found that uncertainties in the cross sections lead to uncertainties in the local power values of up to 1.5%. The variances between the fresh and mixed/depleted core models are not as large as the axial variations between the top and bottom within each core model. The core eigenvalue uncertainty is approximately 0.5%, which is significant in comparison to typical eigenvalue difference targets (less than ~0.1% for LWRs). The power peaking and axial offset uncertainties are smaller (0.10% – 0.15%) and probably below the level of significance in typical core design metrics. The use of an 8-group energy structure slightly under-estimates some of the uncertainties, but in absolute terms, the uncertainty results are reasonably close to the 26-group results. The 26- and 8-group mean and nominal results differ by a larger degree, and if it can be afforded in terms of calculation resources, the 26-group structure should be used for HTGR calculations. The 8-group structure can, however, still be useful for reasonably accurate uncertainty assessments of transient cases where computational time is important. It was furthermore found that the use of a super cell during the cross-section generation process for better representation of the spectral environment at the core periphery did not lead to significant changes in the core eigenvalues, axial offset, and power peaking uncertainties. The impact of the super cell cross sections on the nominal and mean eigenvalues is, however, important enough (0.6% – 1.1% lower) to justify the use of these super cells for HTGR core simulation to represent the softer spectral environment in the core peripheral regions for nominal best-estimate calculations. The use of super cells for HTGR uncertainty studies is, therefore, not strictly necessary, but the improvements in nominal results could justify their use.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Moore ME Feb 18 2019 aerosol class for Northern NM College [Slides)

This presentation was prepared for the request from Northern New Mexico College. "For your talk I’m thinking a bit more detail about the specific sampler you’re bringing, and the types in use at LANL, then setup and ops check, loading and removing a filter, cutting out the right size circle, reading the sample (we have a couple Eberline SAC-4s and Ludlum 2929s here), and radon/daughters corrections."

61 RADIATION PROTECTION AND DOSIMETRY↗