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DOE OSTI2017

Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integrate them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Multi-‘omic’ analyses of the dynamics, mechanisms, and pathways for carbon turnover in grassland soil under two climate regimes

Soil microbial activity drives the carbon and nitrogen cycles and is an important determinant of atmospheric trace gas turnover, yet most soils are dominated by microorganisms with unknown metabolic capacities. This knowledge gap precludes meaningful predictions of relationships between microorganism types and their biogeochemical functions. Specifically, as climate change physically alters terrestrial ecosystems, the impact on microbial consortia and potential consequences for release of carbon and other nutrients from soils are difficult to predict. In this project, we investigated the identities, activity, and functional potential of microorganisms residing in a well characterized grassland ecosystem (the Angelo Coast Range Reserve) undergoing a controlled 14-year rainfall extension climate change experiment. The research was achieved using a combination of “omics”-based technologies, including genome-resolved metagenomics, transcriptomics, and proteomics. The approach allowed us to capture multiple facets of community ecology and activity in situ. Supported by this award, significant progress was made in addressing the grand challenge of achieving genome-resolution for very complex soil microbial communities. In addition to improvements in the methodology, the research required new approaches to define the 3-dimentional distributions of microorganisms and their metabolic traits in soils and to decipher the impact of climate. Underpinning such analyses was the need for sufficient statistical power to test for geographic, soil depth and rainfall controls on community composition and metabolic properties. This required that we scale up throughput substantially. We overcame significant technical barriers to achieve complex microbial community resolution.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys

We present a deep machine learning (ML)–based technique for accurately determining σ g and Ω m from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos suite of N -body simulations, which comprises 40 cosmological volume simulations spanning a range of cosmological parameter values, and we account for uncertainties in galaxy formation scenarios through the use of generalized halo occupation distributions (HODs). We explore a trio of ML models: a 3D convolutional neural network (CNN), a power spectrum–based fully connected network, and a hybrid approach that merges the two to combine physically motivated summary statistics with flexible CNNs. We describe best practices for training a deep model on a suite of matched-phase simulations, and we test our model on a completely independent sample that uses previously unseen initial conditions, cosmological parameters, and HOD parameters. Despite the fact that the mock observations are quite small (~0.07 h -3 Gpc 3 ) and the training data span a large parameter space (six cosmological and six HOD parameters), the CNN and hybrid CNN can constrain estimates of σ g and Ω m to ~3% and ~4%, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Statistical Characterization of Fracture in CFRP-Concrete Interfaces Due to Wet-Dry Cycling

The experimental fracture mechanics Single Contoured-Cantilever Beam (SCCB) specimen was implemented to evaluate environmentally conditioned concrete-CFRP interfaces. Fracture studies were conducted on specimens having undergone 10, 20 and 30 cycles of alternate wetting and drying. Half of the 24 conditioned specimens studied were placed in a 10% sodium sulfate solution (pH = 6.2–6.5) and half were soaked in sodium hydroxide (pH = 12) to simulate naturally occurring acidic and alkaline field exposure-conditions, respectively. At the end of each target age, SCCB fracture testing was conducted. Two approaches were used in determining the fracture toughness of the CFRP-concrete interface bond that depend on the proper identification of loads reaching a critical state, P{sub c}. In the first, the topmost, or “head”, −initiation and -arrestment loads were averaged, and in the second, the mean of the entire “set” of -initiation and -arrestment loads was taken. This raw-data was then reduced by a statistical-based method from which a range of permissible critical loads was designated. The final G{sub Ic} values cited are based on an averaging of this refined data. Considerable degradation of the CFRP-concrete interface bond resulted especially for cases of latter-stage cycling in the aggressive sodium-sulfate and -hydroxide solutions.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Estimating the CO 2 Fertilization Effect on Extratropical Forest Productivity From Flux‐Tower Observations

Abstract The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO 2 fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO 2 fertilization effect from other long‐term confounding drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO 2 fertilization effect on photosynthetic carbon uptake using eddy covariance (EC) records across 38 extratropical forest sites. We find the median stimulation rate of GPP to be 3.2 ± 0.9 gC m −2 yr −1 ppm −1 (or 16.4 ± 4.2% per 100 ppm) under increasing atmospheric CO 2 across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process‐based land surface models. We address additional factors, including nitrogen deposition and land management, that may impact plant productivity, potentially confounding the attribution to the CO 2 fertilization effect. Assuming these site‐specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO 2 fertilization effect. However, disentanglement of these long‐term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long‐term CO 2 fertilization across multiple EC sites. Such observation‐based quantification is key to constraining the long‐standing uncertainties in the land carbon cycle under rising CO 2 concentrations.

Environmental Sciences & Ecology↗

DOE OSTI2021

Data-Driven Learning for the Mori--Zwanzig Formalism: A Generalization of the Koopman Learning Framework

A theoretical framework which unifies the conventional Mori--Zwanzig formalism and the approximate Koopman learning of deterministic dynamical systems from noiseless observation is presented. In this framework, the Mori--Zwanzig formalism, developed in statistical mechanics to tackle the hard problem of construction of reduced-order dynamics for high-dimensional dynamical systems, can be considered as a natural generalization of the Koopman description of the dynamical system. We next show that, similar to the approximate Koopman learning methods, data-driven methods can be developed for the Mori--Zwanzig formalism with Mori's linear projection operator. We have developed two algorithms to extract the key operators, the Markov and the memory kernel, using time series of a reduced set of observables in a dynamical system. We have adopted the Lorenz `96 system as a test problem and solved for the above operators. These operators exhibit complex behaviors, which are unlikely to be captured by traditional modeling approaches in Mori--Zwanzig analysis. The nontrivial generalized fluctuation-dissipation relationship, which relates the memory kernel with the two-time correlation statistics of the orthogonal dynamics, was numerically verified as a validation of the solved operators. Here we present numerical evidence that the generalized Langevin equation, a key construct in the Mori--Zwanzig formalism, is more advantageous in predicting the evolution of the reduced set of observables than the conventional approximate Koopman operators.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

An Efficient Pipeline for Abdomen Segmentation in CT Images

Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient’s diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline’s optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2025

Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning

Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05) .

Marfo, William [Univ. of Texas, El Paso, TX (Unite↗

DOE OSTI2018

Correlated prompt fission data in transport simulations

Detailed information on the fission process can be inferred from the observation, modeling and theoretical understanding of prompt fission neutron and γ-ray observables. Beyond simple average quantities, the study of distributions and correlations in prompt data, e.g., multiplicity-dependent neutron and γ-ray spectra, angular distributions of the emitted particles, n -n, n - γ, and γ - γ correlations, can place stringent constraints on fission models and parameters that would otherwise be free to be tuned separately to represent individual fission observables. The FREYA and CGMF codes have been developed to follow the sequential emissions of prompt neutrons and γ rays from the initial excited fission fragments produced right after scission. Both codes implement Monte Carlo techniques to sample initial fission fragment configurations in mass, charge and kinetic energy and sample probabilities of neutron and γ emission at each stage of the decay. This approach naturally leads to using simple but powerful statistical techniques to infer distributions and correlations among many observables and model parameters. The comparison of model calculations with experimental data provides a rich arena for testing various nuclear physics models such as those related to the nuclear structure and level densities of neutron-rich nuclei, the γ-ray strength functions of dipole and quadrupole transitions, the mechanism for dividing the excitation energy between the two nascent fragments near scission, and the mechanisms behind the production of angular momentum in the fragments, etc. Beyond the obvious interest from a fundamental physics point of view, such studies are also important for addressing data needs in various nuclear applications. The inclusion of the FREYA and CGMF codes into the MCNP6.2 and MCNPX - PoliMi transport codes, for instance, provides a new and powerful tool to simulate correlated fission events in neutron transport calculations important in nonproliferation, safeguards, nuclear energy, and defense programs. Here, this review provides an overview of the topic, starting from theoretical considerations of the fission process, with a focus on correlated signatures. It then explores the status of experimental correlated fission data and current efforts to address some of the known shortcomings. Numerical simulations employing the FREYA and CGMF codes are compared to experimental data for a wide range of correlated fission quantities. The inclusion of those codes into the MCNP6.2 and MCNPX - PoliMi transport codes is described and discussed in the context of relevant applications. The accuracy of the model predictions and their sensitivity to model assumptions and input parameters are discussed. Lastly, a series of important experimental and theoretical questions that remain unanswered are presented, suggesting a renewed effort to address these shortcomings.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

On the use of Graphs for Test Sequence Selection

This report demonstrates that applying graph theory techniques provides a way to obtain sufficient statistics in finding errors when testing complex state machines. It discusses how to define the tests, then demonstrates how to automatically generate test suites that diversify test cases, subject to constraints. If included within a continuous integration approach, these constructs provide an unbiased means to systematically check for errors within the latest controller software release.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Comparison of four learning-based methods for predicting groundwater redox status

Knowing the location where groundwater denitrification occurs, or by proxy the groundwater redox status (oxic, mixed, and anoxic), is valuable information for assessing and managing potential agricultural land-use impacts on freshwater quality. We compare the efficacy of supervised (Linear Discriminant Analysis LDA; Boosted Regression Trees, BRT; and Random Forest, RF) and unsupervised (Modified Self-Organizing Map, MSOM) learning-based methods to predict groundwater redox status in the agriculturally dominated Tasman, Waikato, and Wellington regions of New Zealand. Thresholds applied to regional groundwater-quality samples provide redox status variables and learn heuristics constrained by these variables and applied to spatial factors (climate, elevation, geologic, hydrology soils, and well depth) identify optimal sets of regional predictor variables. A split-sample approach is used to train and test the learning methods ability to predict redox status using the optimal predictor variables. Overall, the supervised methods demonstrate a prediction bias toward oxic conditions and inability to perform statistically well when using independent regional data; for example, consider kappa statistics for BRT (Tasman: 0.42, Waikato: 0.38, Wellington: 0.17), RF (Tasman: 0.42, Waikato: 0.47, Wellington: 0.17 and LDA (Tasman: 0.46, Waikato: 0.32, Wellington: 0.17). By contrast, the unsupervised method performs statistically well when predicting oxic, mixed, and anoxic conditions and corresponding depths when using independent regional data; for example, consider MSOM kappa statistics for Tasman: 0.78, Waikato: 0.80, Wellington: 0.76. The unsupervised learning method provides the added benefits of being (1) able to combine predictions into 3D regional anoxic probability plots for interpreting the spatial influence of paleosols and groundwater flowpaths on redox status, and (2) readily extended to map 3D redox status across New Zealand and other countries despite data bias and sparsity.

machine learning algorithms, Random Forest, booste↗

DOE OSTI2016

IMPROVED PERFORMANCES IN SUBSONIC FLOWS OF AN SPH SCHEME WITH GRADIENTS ESTIMATED USING AN INTEGRAL APPROACH

In this paper, we present results from a series of hydrodynamical tests aimed at validating the performance of a smoothed particle hydrodynamics (SPH) formulation in which gradients are derived from an integral approach. We specifically investigate the code behavior with subsonic flows, where it is well known that zeroth-order inconsistencies present in standard SPH make it particularly problematic to correctly model the fluid dynamics. In particular, we consider the Gresho–Chan vortex problem, the growth of Kelvin–Helmholtz instabilities, the statistics of driven subsonic turbulence and the cold Keplerian disk problem. We compare simulation results for the different tests with those obtained, for the same initial conditions, using standard SPH. We also compare the results with the corresponding ones obtained previously with other numerical methods, such as codes based on a moving-mesh scheme or Godunov-type Lagrangian meshless methods. We quantify code performances by introducing error norms and spectral properties of the particle distribution, in a way similar to what was done in other works. We find that the new SPH formulation exhibits strongly reduced gradient errors and outperforms standard SPH in all of the tests considered. In fact, in terms of accuracy, we find good agreement between the simulation results of the new scheme and those produced using other recently proposed numerical schemes. These findings suggest that the proposed method can be successfully applied for many astrophysical problems in which the presence of subsonic flows previously limited the use of SPH, with the new scheme now being competitive in these regimes with other numerical methods.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Optimizing a Knowledge-driven Prospectivity Model for Gold Deposits Within Peräpohja Belt, Northern Finland

This paper combines knowledge- and data-driven prospectivity mapping approaches by using the receiver operating characteristics (ROC) spatial statistical technique to optimize the process of rescaling input datasets and the process of data integration when using a fuzzy logic prospectivity mapping method. The methodology is tested in an active mineral exploration terrain within the Paleoproterozoic Peräpohja Belt (PB) in the Northern Fennoscandian Shield, Finland. The PB comprises a greenschist to amphibolite facies, complexly deformed supracrustal sequence of variable quartzites, mafic volcanic rocks and volcaniclastic rocks, carbonate rocks, black shales, mica schists and graywackes. These formations were deposited on Archean basement and 2.44 Ga layered intrusions, during the multiple rifting of the Archean basement (2.44–1.92 Ga). Younger intrusive units in the PB comprise 2.20–2.13 Ga gabbroic sills or dikes and 1.98 Ga A-type granites. Metamorphism and complex deformation of the PB took place during the Svecofennian orogeny (1.9–1.8 Ga) and were followed by intrusions of post-orogenic granitoids (1.81–1.77 Ga). The recent mineral exploration activities have indicated several gold-bearing mineral occurrences within the PB. The Rompas Au-U mineralization is hosted within deformed and metamorphosed calc-silicate veins enclosed within mafic volcanic rocks and contains uranium-bearing zones without gold and very high-grade (>10,000 g/t Au) gold pockets with uraninite and uraninite-pyrobitumen nodules. In the vicinity of the Rompas, a magnesium skarn hosted disseminated-stockwork gold mineralization was also recognized at the Palokas-Rajapalot prospect. The exploration criteria translated into a fuzzy logic prospectivity model included data derived from regional till geochemistry (Fe, Cu, Co, Ni, Au, Te, K), high-resolution airborne geophysics (magnetic field total intensity, electromagnetic, gamma radiation), ground gravity and regional bedrock map (structures). The current exploration licenses and exploration drilling sites for gold were used to validate the knowledge-driven mineral prospectivity model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

An Investigation of LES Wall Modeling for Rayleigh–Bénard Convection via Interpretable and Physics-Aware Feedforward Neural Networks with DNS

Abstract The traditional approach of using the Monin–Obukhov similarity theory (MOST) to model near-surface processes in large-eddy simulations (LESs) can lead to significant errors in natural convection. In this study, we propose an alternative approach based on feedforward neural networks (FNNs) trained on output from direct numerical simulation (DNS). To evaluate the performance, we conduct both a priori and a posteriori tests. In the a priori (offline) tests, we compare the statistics of the surface shear stress and heat flux, computed from filtered DNS input variables, to the stress and flux obtained from the filtered DNS. Additionally, we investigate the importance of various input features using the Shapley additive explanations value and the conditional average of the filter grid cells. In the a posteriori (online) tests, we implement the trained models in the System for Atmospheric Modeling (SAM) LES and compare the LES-generated surface shear stress and heat flux with those in the DNS. Our findings reveal that vertical velocity, a traditionally overlooked flow quantity, is one of the most important input features for determining the wall fluxes. Increasing the number of input features improves the a priori test results but does not always improve the model performance in the a posteriori tests because of the differences in input variables between the LES and DNS. Last, we show that physics-aware FNN models trained with logarithmic and scaled parameters can well extrapolate to more intense convection scenarios than in the training dataset, whereas those trained with primitive flow quantities cannot. Significance Statement The traditional near-surface turbulence model, based on a shear-dominated boundary layer flow, does not represent near-surface turbulence in natural convection. Using a feedforward neural network (FNN), we can construct a more accurate model that better represents the near-surface turbulence in various flows and reveals previously overlooked controlling factors and process interactions. Our study shows that the FNN-generated models outperform the traditional model and highlight the importance of the near-surface vertical velocity. Furthermore, the physics-aware FNN models exhibit the potential to extrapolate to convective flows of various intensities beyond the range of the training dataset, suggesting their broader applicability for more accurate modeling of near-surface turbulence.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

M3SF-25LL010302052 - Radionuclide Interaction with Hydrothermally Altered Repository Materials

This progress report (Level 3 Milestone Number M3SF-25LL010302052) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Crystalline Host Rock Properties & Processes - LLNL Number SF-25LL01030205. Observed changes in radionuclide sorption after bentonite/clay heating have implications for radionuclide diffusive transport through engineered barriers and must be considered when designing waste disposal repositories. Recent research performed at Los Alamos National Laboratory (LANL) has provided key insights regarding the hydrothermal alteration behavior of bentonite backfill in the presence of repository materials (steel, concrete, etc.). We are examining how this mineral alteration affects retardation behavior of a suite of radionuclides of interest to repository performance assessment. Sorption experiments and data analysis for 233 U were initiated in FY24 following earlier experiments performed on 243 Am, 90 Sr, 137 Cs. In FY25, we completed the 233 U study and initiated and completed a study of 237 Np sorption. Below, we summarize the results and potential impacts of hydrothermal alteration on radionuclide retardation and assess the importance of this process to radionuclide migration from a GHRDC. We also use statistical tools (i.e. PCA) to help us determine the major drivers in affecting changes in measured Kd values induced by hydrothermal alteration. These experiments also allow us to test the predictive ability of our component additivity approach to surface complexation and ion exchange. Our guiding hypothesis is that a robust surface complexation/ion exchange model and associated database can effectively predict changes in radionuclide sorption behavior resulting from the hydrothermal alteration of mineralogy in a repository near field. In November 2024 the paper “Selenium interaction with iron minerals: Quantitative comparison of sorption and coprecipitation impacts on mobility” was published in Applied Geochemistry. A short update of results to date is presented below. In FY25, we also actively supported the DITUSC project, which is part of the EURADII initiative, as associate partners. We are executing the Migration2025 conference and support associated the NEA-TDB and Thermochimie workshops that will provide critical international engagements and develop consensus and synergy in thermodynamics as it relates to supporting the US nuclear waste repository program.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Measuring phylogenetic signal between categorical traits and phylogenies

Abstract Motivation Determining whether a trait and phylogeny share some degree of phylogenetic signal is a flagship goal in evolutionary biology. Signatures of phylogenetic signal can assist the resolution of a broad range of evolutionary questions regarding the tempo and mode of phenotypic evolution. However, despite the considerable number of strategies to measure it, few and limited approaches exist for categorical traits. Here, we used the concept of Shannon entropy and propose the δ statistic for evaluating the degree of phylogenetic signal between a phylogeny and categorical traits. Results We validated δ as a measure of phylogenetic signal: the higher the δ-value the higher the degree of phylogenetic signal between a given tree and a trait. Based on simulated data we proposed a threshold-based classification test to pinpoint cases of phylogenetic signal. The assessment of the test’s specificity and sensitivity suggested that the δ approach should only be applied to 20 or more species. We have further tested the performance of δ in scenarios of branch length and topology uncertainty, unbiased and biased trait evolution and trait saturation. Our results showed that δ may be applied in a wide range of phylogenetic contexts. Finally, we investigated our method in 14 360 mammalian gene trees and found that olfactory receptor genes are significantly associated with the mammalian activity patterns, a result that is congruent with expectations and experiments from the literature. Our application shows that δ can successfully detect molecular signatures of phenotypic evolution. We conclude that δ represents a useful measure of phylogenetic signal since many phenotypes can only be measured in categories. Availability and implementation https://github.com/mrborges23/delta_statistic. Supplementary information Supplementary data are available at Bioinformatics online.

Borges, Rui↗

DOE OSTI2018

Advanced Models for Nondestructive Evaluation of Aging Nuclear Power Plant Cables

Background: Nuclear power plant (NPP) cables must function correctly for proper power supply and control of the nuclear reactor. This is especially important for public and environmental safety at critical times such as during reactor shut-down following loss of coolant. Over extended periods of service, the cable insulation and jacket polymer materials suffer from degradation due to exposure to environmental stressors such as heat and ionizing radiation. The focus of this project has been the development of detailed descriptions of the way in which insulation polymers degrade, from a materials science perspective, and the development of new electrical techniques to monitor cable polymer condition nondestructively. Objectives: The objectives of this project have been to i) perform detailed characterization experiments upon cable insulation polymers that have experienced thermal and radiation exposure mimicking that received during NPP operation, including the exploration of novel cable nondestructive evaluation methods; capacitive, THz and infrared methods; ii) identify the most sensitive indicators of aging in these materials, and iii) develop advanced, validated models describing microstructural and chemical changes that are observed in cable insulation polymers due to thermal and radiation exposure. Methods: Accelerated aging was performed on the two most common United States NPP cable insulation polymers (cross-linked polyethylene (XLPE) and ethylene propylene rubber (EPR)) under various conditions of elevated temperature and gamma radiation dose. Aging mechanisms were elucidated through extensive thermal, mechanical and microscopic characterization of the aged samples, and materials modeling. Dielectric and IR spectra were measured on pristine and aged samples. Data was analyzed statistically, via multiple linear regression, partial least squares and probabilistic neural network approaches. Impact: Extant cable monitoring techniques do not deal successfully with all polymer types. This research has laid a foundation upon which new electrical nondestructive test methods for cable polymer condition monitoring may be developed, hence launching the development of new test methods for cable insulation monitoring based on measuring electrical rather than mechanical parameters. Collaboration: This project has benefitted from collaboration between experts in materials science and engineering, materials modeling, electrical engineering, and nondestructive evaluation. The majority of the characterization measurements were carried out at Iowa State University. Materials modeling was carried out primarily at Washington State University. Irradiation of samples took place at Pacific Northwest National Laboratory (PNNL), where a student intern from Iowa State was hosted for one summer, setting up the aging experiments. A research student from the University of Bologna, Italy, joined the project for their thesis research at Iowa State University with samples provided by PNNL. The project benefitted from regular involvement of senior engineers in US-based industry partners. Project investigators presented findings at various national and international conferences and workshops.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

Probing neutrino emission at GeV energies from compact binary mergers with the IceCube Neutrino Observatory

The advent of multimessenger astronomy has allowed for new types of source searches by neutrino detectors. Here, we present the results of the search for 0.5–100 GeV astrophysical neutrinos detected with IceCube and emitted from compact binary mergers detected by the LIGO, Virgo, and KAGRA interferometers from their first run of observation (O1) to the end of the first part of the fourth (O4a). An innovative approach is used to lower the energy threshold to 0.5 GeV and to search for an excess of GeV neutrinos in time coincidence with astrophysical transient events. Furthermore, we use a statistical combination of all observations, a binomial test, to search for a subpopulation of neutrino emitters. No significant excess was found from the studied mergers, with a best post-trial 𝑝-value of 40%, and there is currently no hint of a population of GeV neutrino emitters found in the IceCube data (post-trial 𝑝−value=81%).

Abbasi, R. [Loyola University Chicago]↗