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DOE OSTI2026

Sampling Microbial Dynamics in the Salish Sea Estuary: Evaluating Methods to Capture Cyanobacteria and Cyanophage

Introduction: Picocyanobacteria from the genera Prochlorococcus and Synechococcus thrive across the globe in aquatic environments, have relatively small genomes, and have growth dynamics regulated by both viral interactions and abiotic conditions, making them excellent model organisms for exploring host-pathogencoevolution. Methods: We developed and refined methods to sample and sequence cyanobacteria, cyanophages, and measured features of their abiotic environment. Results: The protocol described herein can successfully discriminate large-cell eukaryotic organisms, but size fractionation of picocyanobacteria appears to be affected by the presence of free DNA, multicellular structures, and abundant tycheposons. Our preferred final protocol from this exploratory effort included a combination of in-line and single vacuum flask filtrations, which reduced filtration processing time by over threefold in some cases compared to other tested methods, such as a fully in-line sequence or in-site filtrations. We successfully extracted an average of approximately 400–1200 ng for all filter fractions, with some variations between kits. Discussion: The protocol described herein can successfully discriminate large-cell eukaryotic organisms, but size fractionation of picocyanobacteria appears to be affected by the presence of free DNA, multicellular structures, and abundant tycheposons.

Salish Sea↗

DOE OSTI2025

Ecohydrological controls on root and microbial respiration in the East River watershed of Colorado

The main objective of this project was to conduct exploratory work to quantify how snow and rain water inputs influence the CO2 coming from the soil surface (soil CO2 flux), and its plant and microbial sources, in the East River watershed, near Crested Butte, Colorado. Knowledge gained from this effort laid the groundwork for a more comprehensive (ongoing) follow-on grant that is using a combination of experiments, field observations, machine learning and simulation modeling to fully disentangle these relationships. New field measurements were made at four locations along an elevational transect on Snodgrass Mountain at Rocky Mountain Biological Laboratory. These sites were chosen to differ in total snowpack and water table depth, and included both deciduous (aspen) and evergreen (spruce/fir) forest types. We used automated measurements of soil CO2 concentrations to quantify the total soil CO2 flux, and the vertical CO2 production within the soil profile at each site. Isotope (radiocarbon, 14C) measurements determined how much of the CO2 emitted from the soil surface came from plant respiration (root metabolism) versus microbial respiration (decomposition of soil organic matter) sources. Supporting data on plant phenology and microbial dynamics provided context for the observed variation in respiration sources. This work was motivated by our overarching hypothesis that quantifying belowground plant and microbial processes separately, and how they are influenced by snow and rain inputs, is necessary for understanding and predicting how the East River watershed ecosystems will respond to future environmental change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Psychometric property study of the Affective Lability Scale-short form in Chinese patients with mood disorders

Introduction This study aimed to investigate the psychometric properties of the Affective Lability Scale-short form (ALS-SF) among Chinese patients with mood disorders, and to compare ALS-SF subscale scores between patients with major depressive disorder (MDD) and patients with bipolar disorder (BD) depression. Methods A total of 344 patients with mood disorders were included in our study. Participants were measured through a set of questionnaires including the Chinese version of ALS-SF, Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), and NEO-Five Factor Inventory (NEO-FFI). Exploratory factor analysis and confirmatory factor analysis were applied to examine the psychometric properties of ALS-SF. Besides, correlation and regression analyses were performed to explore the relationship between affective lability and depression, anxiety, and neuroticism. Independent samples t -tests were used to compare the subscale scores of ALS-SF between the MDD and BD depression groups. Results Results of factor analysis indicated that the model of ALS-SF was consistent with ALS-SF. The ALS-SF showed a solid validity and high internal consistency (Cronbach’s alpha = 0.861). In addition, each subscale of ALS-SF was significantly correlated with PHQ-9, GAD-7, and NEO-FFI neuroticism subscale, except for the anger subscale showed no significant correlation with PHQ-9. Besides, the depression/elation and anger factor scores in patients with BD depression were higher than in patients with MDD. Conclusion Our study suggests that the Chinese version of ALS-SF has good reliability and validity for measuring affective lability in Chinese patients with mood disorders. Assessing affective lability would assist clinicians to distinguish between MDD and BP depression and may decrease the risks of misdiagnosis.

Ma, Mohan↗

DOE OSTI2021

Understanding implications of cathode architecture on energy density of solid-state batteries

Next generation solid-state batteries (SSB) will need to leverage high voltage cathodes, as well as metallic anodes to achieve the realistic performance targets necessary to replace liquid electrolyte-based batteries in cutting-edge applications including electric vehicles. However, limitations arising from mass and charge transports, kinetics and chemo-mechanical degradation at the electrode | electrolyte interface limit the performance of present day SSBs. Optimizing composite cathode architecture, which is an integral part of solid-state batteries, is vital to realize the high-energy density and high-performance goals for next-generation solid-state batteries. Additionally, cathode architecture needs to be optimized for high loadings of active material, well-percolated ion and electron transport pathways and increased resilience against electrochemical stresses. This paper provides a first report of framework for geometric modeling of composite cathode architectures and evaluates the impact of cathode architecture on cell-level energy density using hierarchical models. Packing around primary and secondary active material particles are simulated for a range of active material particle size and solid electrolyte size distributions in the composite cathode. Impact of packing architecture on processing parameters of a given cathode composition and thickness, as well as on achievable energy density is evaluated for a range of commonly used solid electrolyte and cathode materials. Overall, the proposed framework offers a facile exploratory methodology for establishing initial metrics for scalable processing of practical and competent SSBs.

25 ENERGY STORAGE↗

DOE OSTI2023

Exposure to multiple ion beams, broadly representative of galactic cosmic rays, causes perivascular cardiac fibrosis in mature male rats

Long-duration space exploratory missions to the Earth’s moon and the planet Mars are actively being planned. Such missions will require humans to live for prolonged periods beyond low earth orbit where astronauts will be continuously exposed to high energy galactic cosmic rays (GCRs). A major unknown is the potential impact of GCRs on the risks of developing degenerative cardiovascular disease, which is a concern to NASA. A ground-based rat model has been used to provide a detailed characterization of the risk of long-term cardiovascular disease from components of GCRs at radiation doses relevant to future human missions beyond low earth orbit. Six month old male WAG/RijCmcr rats were irradiated at a ground-based charged particle accelerator facility with high energy ion beams broadly representative of GCRs: protons, silicon and iron. Irradiation was given either as a single ion beam or as a combination of three ion beams. For the doses used, the single ion beam studies did not show any significant changes in the known cardiac risk factors and no evidence of cardiovascular disease could be demonstrated. In the three ion beam study, the total cholesterol levels in the circulation increased modestly over the 270 day follow up period, and inflammatory cytokines were also increased, transiently, 30 days after irradiation. Perivascular cardiac collagen content, systolic blood pressure and the number of macrophages found in the kidney and in the heart were each increased 270 days after irradiation with 1.5 Gy of the three ion beam grouping. These findings provide evidence for a cardiac vascular pathology and indicate a possible threshold dose for perivascular cardiac fibrosis and increased systemic systolic blood pressure for complex radiation fields during the 9 month follow up period. The development of perivascular cardiac fibrosis and increased systemic systolic blood pressure occurred at a physical dose of the three ion beam grouping (1.5 Gy) that was much lower than that required to show similar outcomes in earlier studies with the same rat strain exposed to photons. Further studies with longer follow up periods may help determine whether humans exposed to lower, mission-relevant doses of GCRs will develop radiation-induced heart disease.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

WEST L-mode record long pulses guided by predictions using Integrated Modeling

A new record was set on the WEST Tokamak, designed to operate long duration plasmas in a tungsten (W) environment, with an injected energy of 1.15 GJ and a plasma duration 364s. Scenario development was supported by integrated modeling using the High Fidelity Plasma Simulator (HFPS), the European IMAS-coupled version of JETTO/JINTRAC, which integrates physics-driven modules into a unified framework. In particular, a reduced model for Lower-Hybrid heating and Current-Drive (LHCD) and the quasi-linear turbulent transport model TGLF are crucial for long pulses predictions up to the Last Closed Flux Surface (LCFS). Using this workflow, a 100 s reference discharge was modeled and plasma kinetic profiles and loop voltage were quantitatively well matched. In preparation for the recent long duration experiments, non-inductive current-drive actuators (I P , n e , P LHCD ) were varied to determine the operational domain going towards fully non-inductive discharges. In particular, decreasing the plasma current is shown to ease the access to such conditions, with a careful monitoring of (n e , P LHCD ) to avoid machine limitations. In addition, post-prediction experiments conducted within the investigated parameter range validated the predicted dependencies and were shown to be in quantitative agreement. Exploratory work on the use of ECCD for MHD stability purpose is also introduced.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Yes, No, Maybe So: Human Factors Considerations for Fostering Calibrated Trust in Foundation Models Under Uncertainty

High-stakes analytical environments require analysts to evaluate evidence and generate conclusions to inform critical decisions often under conditions of uncertainty. Probabilistic decision-making based on incomplete or inaccurate information can reduce productivity, compromise national interests, and endanger public safety. Researchers are developing expert systems built on foundation models (FMs) to support analysts’ decision-making processes by enabling human-artificial intelligence (AI) teaming, in part through the quantification and expression of uncertainty information. As FMs continue to mature, it is imperative to correspondingly consider analysts’ needs for appropriately interpreting and using uncertainty information. However, prior research indicates that it remains unclear how analysts engage with FM-generated uncertainty information and the extent to which these interactions influence trust in, and reliance on, expert systems. We plan to review the state of the science and conduct an exploratory, qualitative study to (a) understand how properly communicated uncertainty can foster calibrated trust and appropriate reliance and (b) identify approaches for effectively conveying FM-generated uncertainty information during analytical workflows. We will administer semi-structured interviews with analysts from a specific high-stakes analytical environment to collect their current experiences with job-related uncertainty and their impressions when viewing FM-generated uncertainty information. During the interview protocol, participants will be presented with several different FM outputs and invited to discuss their thoughts and beliefs about the uncertainty information displayed. Participants may provide insights into how trust and reliance may be influenced by uncertainty. The results of this study will help us to better understand how analysts currently interpret and use uncertainty information. Our findings may inform human factors recommendations for effectively conveying uncertainty information to foster calibrated trust in, and appropriate reliance on, expert systems. Interaction designers and FM developers can use this knowledge to enhance human-AI teaming and ensure the responsible deployment of FM-based expert systems in analytical workflows.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Accelerating Scientific Applications With SambaNova Reconfigurable Dataflow Architecture

Here, our exploratory work finds that the SambaNova Reconfigurable Dataflow Architecture (RDA) along with the SambaFlow software stack provides for an attractive system and solution to accelerate AI for science workloads. We have observed the efficacy of using the system with a diverse set of science applications and reasoned their suitability for performance gains over traditional hardware. As the Data-Scale system provides for a very large memory capacity, the system can be used to train models that typically do not fit in a GPU. The architecture also provides for deeper integration with upcoming supercomputers at the Argonne Leadership Computing Facility (ALCF), a US Department of Energy Office of Science user facility, to help advance science insights.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Unique Forbidden Beta Decays at Zero Momentum Transfer

We report an exploratory study of the 𝒪⁡(𝛼) structure-dependent electromagnetic radiative corrections to unique first-forbidden nuclear beta decays. We show that the insertion of angular momentum into the nuclear matrix element by the virtual or real photon exchange opens up the decay at vanishing nuclear recoil momentum which was forbidden at tree level, leading to a dramatic change in the decay spectrum not anticipated in existing studies. We discuss its implications for precision tests on the standard model and searches for new physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗

DOE OSTI2022

Deep reaction network exploration at a heterogeneous catalytic interface

Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga 3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Monitoring of dielectric permittivity in accelerated alkali-silica reaction concrete with microwave backscattering

Deterioration of concrete due to the alkali–silica reaction (ASR) involves a reaction between alkaline ions in the cement pore solution and non-crystalline silica found in many aggregates. The product of reaction is a porous hydrophilic ASR gel, which causes expansion and cracking of concrete structure. Currently, there is no reliable method for non-destructive evaluation of ASR. We have investigated the feasibility of using relative dielectric permittivity obtained from X-band microwave backscattering of concrete. Advantages of this method of concrete evaluation are that measurements are non-contact and one-sided, and the approach is scalable to arbitrary-size concrete structures. The exploratory study was performed using an unrestrained set of accelerated ASR concrete prism specimens developed according to the ASTM1293 standard. One set of specimens contained no entrained air, while another set was contained entrained air. The specimens were removed from the environmental chamber at different times during a year-long study to create a set of different ASR maturity specimens. Strain measurements were performed on all specimens. Strain data both air entrained and non-air entrained sets were shown to be in close agreement with Larive model of isothermal unrestrained ASR expansion with similar fitting parameters. Furthermore, relative dielectric permittivity of concrete specimens was correlated with strain data. Both sets show correlation between dielectric permittivity and strain, with stronger linear correlation observed for the air-entrained specimens. Development of such correlation would enable estimating expansion of actual concrete structures from microwave backscattering measurements, which could be used for risk stratification to guide and minimize coring.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Nucleon tomography and total angular momentum of valence quarks from synergy between lattice QCD and elastic scattering data

We present an exploratory work on integrating lattice-QCD results with experimental data for elastic scattering. Within the framework of generalized parton distributions, this approach allows for the extraction of detailed information about nucleon tomography and the total angular momentum carried by valence quarks. To accomplish this reliably, we introduce a new quantity, the “double ratio,” which significantly reduces the systematic uncertainties inherent in lattice QCD computations. Moreover, we introduce a “shadow” term in the extraction procedure, which is sensitive only to lattice-QCD results. This term allows us to investigate the model dependence of the extraction, providing a more flexible description of the nucleon that goes beyond the previously considered bell-shaped distribution.

Lattice QCD↗

DOE OSTI2020

Exploratory Investigation of Disengagements and Crashes in Autonomous Vehicles Under Mixed Traffic: An Endogenous Switching Regime Framework

Autonomous Vehicles (AVs) have a large potential to improve traffic safety but also pose some critical challenges. While AVs may help reduce crashes caused by human error, they still may experience failures of technologies and sensing, as well as decision-making errors in a mixed traffic environment. A disengagement refers to an AV transitioning control from autonomous systems to the trained test driver. The safety critical nature of disengagements makes it imperative to analyze disengagements and crashes together. In this study, we analyze both crashes and disengagements from real-world AV driving in California to fill the knowledge gap regarding the relationship between disengagements and crashes in a mixed traffic environment. A nested logit model was calibrated using three different outcomes: (1) disengagement with a crash, (2) disengagement with no crash, and (3) no disengagement with a crash. Furthermore, endogenous switching regime models were also calibrated to draw distinctions between the relation of disengagements and crashes while accounting for endogeneity effects. The results show that factors related to AV systems (such as software failures) and other roadway participants increase the propensity of a disengagement without a crash. Furthermore, AVs were observed to disengage less often as the technology matured over time. Marginal effects revealed an 8% decrease. The results thus suggest that disengagements are a part of AVs' safe performance and disengagement alerts may need to be triggered in order to avoid certain failures with current technology.

42 ENGINEERING↗

DOE OSTI2023

An Interpretable Index of Social Vulnerability to Environmental Hazards

Index-based measures of social vulnerability to environmental hazards are commonly modeled from composites of population-level risk factors. These models overlook individual context in communities' experiences of environmental hazards, producing metrics that may hinder spatial decision support for mitigating and responding to hazards. This paper introduces an interpretable, high-resolution model for generating an individual-oriented social vulnerability index (IOSVI) for the United States built on synthetic populations that couples individual and social determinants of vulnerability. The IOSVI combines an individual vulnerability index (IVI) that ranks individuals in an area’s synthetic population based on intersecting risk factors, with a social vulnerability index (SVI) based on the population’s cumulative distribution of IVI scores. Interpretability of the IOSVI procedure is demonstrated through examples of national, metropolitan, and neighborhood (census tract) level spatial variation in index scores and IVI themes, as well as an exploratory analysis examining risk factors affecting a specific sub-population (military veterans) in areas of high social and environmental vulnerability.

Tuccillo, Joe↗

DOE OSTI2023

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.

chemical space exploration↗

DOE OSTI2020

Hawaii Play Fairway Analysis: Lanai Resistivity and Density 3D Inversion Models

To prepare for its third phase, the Hawaii Play Fairway project conducted groundwater sampling and analyses in ten locations in the Hawaiian islands, magnetotelluric (MT) and gravity surveys, as well as calculations of 3D subsurface stress due to the weight of the rock underlying the topography of the volcano. The subsurface stresses were used to evaluate the potential for fracture-induced permeability. Inversions of the MT and gravity data produce 3D models of resistivity and density, respectively, on Lanai, across Haleakala's SW rift (Maui), and surrounding Mauna Kea (Hawaii Island). The project developed and applied a new method for incorporating depth information about resistivity, density, and potential for fracture-induced permeability into the statistical method for computing resource probability in these three focus areas. The project then incorporated the new groundwater results with the new geophysical results and the calculations of potential for fracture-induced permeability to produce updated maps of resource probability and confidence. These results were used to identify target sites for exploratory drilling. Spreadsheet information: Each sheet contains data for a particular depth in kilometers. Positive depths are above sea level, and negative below. For more information, go to the Hawaii Groundwater and Geothermal Resources Center website linked in the resources.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Hospice Landscape Report

In July 2018, CMS requested assistance from Oak Ridge National Laboratory (ORNL) to provide expert data science support aimed at developing algorithms for data mining of medical data for operational and payment purposes. The project is intended to be exploratory: work is aimed at alleviating challenges associated with improper payments, specifically audit methodologies and targeting and changes to risk scores. Project goals include developing new, sophisticated methods for audit targeting and improved profile– payment error correlations, specifically focused on Medicare Part C, the program under which MAOs provide health care services to beneficiaries. ORNL conducted RADV analyses against RAPS and EDS data as well as a hospice landscape analysis per a January 2015 dataset that included Medicare beneficiaries who were in hospice in 2017 and 2018. Ongoing work under this project also involves development of predictive models for RADV investigations and hospice landscape.

97 MATHEMATICS AND COMPUTING↗