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DOE OSTI2024

A Comparative Metagenomic Analysis of Specified Microorganisms in Groundwater for Non-Sterilized Pharmaceutical Products

In pharmaceutical manufacturing, ensuring product safety involves the detection and identification of microorganisms with human pathogenic potential, including Burkholderia cepacia complex (BCC), Escherichia coli, Pseudomonas aeruginosa, Salmonella enterica, Staphylococcus aureus, Clostridium sporogenes, Candida albicans, and Mycoplasma spp., some of which may be missed or not identified by traditional culture-dependent methods. In this study, we employed a metagenomic approach to detect these taxa, avoiding the limitations of conventional cultivation methods. We assessed the groundwater microbiome’s taxonomic and functional features from samples collected at two locations in the spring and summer. All datasets comprised 436–557 genera with Proteobacteria, Bacteroidota, Firmicutes, Actinobacteria, and Cyanobacteria accounting for > 95% of microbial DNA sequences. The aforementioned species constituted less than 18.3% of relative abundance. Escherichia and Salmonella were mainly detected in Hot Springs, relative to Jefferson, while Clostridium and Pseudomonas were mainly found in Jefferson relative to Hot Springs. Multidrug resistance efflux pumps and BlaR1 family regulatory sensor-transducer disambiguation dominated in Hot Springs and in Jefferson. These initial results provide insight into the detection of specified microorganisms and could constitute a framework for the establishment of comprehensive metagenomic analysis for the microbiological evaluation of pharmaceutical-grade water and other non-sterile pharmaceutical products, ensuring public safety.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Multivariate environmental and trait-based controls of transpiration in the Central Amazon Rainforest

Tropical forest tree mortality is increasing due to more severe droughts, yet our understanding of how tree traits and life strategies are linked to drought stress has been limited by measurement scarcity. The BIONTE (BIOmass and NuTrient Experiment) near Manaus, Brazil hosts one of the world’s largest sap flow installations, with sensors in 90 canopy trees across a wood density gradient monitored since June 2022. The 2023 El Niño drought provided a unique opportunity to evaluate how water availability impacts tree transpiration. An interpretable machine learning framework was used to study the complex interactions between transpiration and multiple environmental variables such as soil water availability and vapor pressure deficit (VPD), and how these interactions vary with wood density and individual trees. We found varying responses of transpiration from different trees during the El Niño drought. Transpiration generally increased with temperature, with stronger effects in wetter areas and in trees with low to medium wood density. However, this response was modulated by stomatal sensitivity to VPD, which constrained transpiration under high atmospheric demand, particularly in intermediate-moisture area. The inflection in transpiration rate at high temperatures (>32°C) underscores the role of stomatal and hydraulic regulation in limiting water loss and protecting trees from excessive evaporative demand. Analysis of soil water contribution to transpiration revealed unimodal patterns in wetter area, with peak contributions near 0.45 cm 3 cm -3 of surface soil water and declining or flat responses beyond that threshold, suggesting a shift from water- to energy-limited transpiration. In contrast, drier areas exhibited limited transpiration sensitivity to soil water conditions and minimal trait-based variation in VPD responses, indicating supply-limited conditions. Despite higher wood density trees being generally more resilient, this study shows diverse tree drought resilience, prompting further investigation into the specific traits and dynamics between environmental variables in regulating transpiration and other physiological processes in trees.

Drought↗

DOE OSTI2014

Two charges on plane in a magnetic field I. “Quasi-equal” charges and neutral quantum system at rest cases

Low-lying bound states for the problem of two Coulomb charges of finite masses on a plane subject to a constant magnetic field B perpendicular to the plane are considered. Major emphasis is given to two systems: two charges with the equal charge-to-mass ratio (quasi-equal charges) and neutral systems with concrete results for the hydrogen atom and two electrons (quantum dot). It is shown that for these two cases, when a neutral system is at rest (the center-of-mass momentum is zero), some outstanding properties occur: in double polar coordinates in CMS (R,ϕ) and relative (ρ,φ) coordinate systems (i) the eigenfunctions are factorizable, all factors except for ρ-dependent are found analytically, they have definite relative angular momentum, (ii) dynamics in ρ-direction is the same for both systems being described by a funnel-type potential; (iii) at some discrete values of dimensionless magnetic fields b≤1 the system becomes quasi-exactly-solvable and a finite number of eigenfunctions in ρ are polynomials. The variational method is employed. Trial functions are based on combining for the phase of a wavefunction (a) the WKB expansion at large distances, (b) the perturbation theory at small distances (c) with a form of the known analytically (quasi-exactly-solvable) eigenfunctions. Such a form of trial function appears as a compact uniform approximation for lowest eigenfunctions. For the lowest states with relative magnetic quantum numbers s=0,1,2 this approximation gives not less than 7 s.d., 8 s.d., 9 s.d., respectively, for the total energy E(B) for magnetic fields 0.049a.u.<B<2000a.u. (hydrogen atom) and 0.025a.u.⋜B⋜1000a.u. (two electrons). The evolution of nodes of excited states with the magnetic field change is indicated. In the framework of convergent perturbation theory the corrections to proposed approximations are evaluated. -- Highlights: •Approximate solution for the low-lying eigenstates for two Coulomb charges given. •Factorization of eigenfunctions in double polar center-of-mass coordinates uncovered. •A magnetic field range varies from weak to ultra-strong, 1000–2000 a.u.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Nonperturbative heavy-flavor transport approach for hot QCD matter

The heavy charm and bottom quarks are unique probes of the transport properties of the quark-gluon plasma (QGP) and its hadronization in high-energy nuclear collisions. A key challenge in this context is to embed the interactions of the heavy quarks in the expanding medium compatible with the strong-coupling nature of the QGP, and thus to unravel the underlying microscopic mechanisms. In the present work we progress toward this goal by combining recent T -matrix interactions for elastic scattering with an effective transport implementation of gluon radiation, and apply these in a Langevin framework in a viscous hydrodynamic evolution. Hadronization of heavy quarks is evaluated using a modern recombination model with 4-momentum conservation, supplemented with fragmentation constrained by data in proton-proton collisions. Deploying this approach to charm-hadron observables in Pb-Pb collisions at the LHC yields fair agreement with experiment while also identifying areas of further systematic improvement of the simulations and its current input.

Heavy-flavor transport↗

DOE OSTI2025

More land is needed for solar and wind infrastructure under a high renewables scenario in the Western US by 2050

Expanding United States electricity infrastructure to meet growing demand could require extensive power plant development footprints and land use conversion, depending on the mix of generation types chosen. Understanding where future power plant sitings are likely to take place and identifying potential conflicts and land-use tradeoffs will be key to identifying feasible and affordable investments and evaluating regional planning coordination needs. Here we use an integrated modeling framework that combines capacity expansion planning, hourly grid operations, and geospatial techno-economic analysis to develop projections (2025-2050) of power plant sitings in the Western United States (US) at a 1 km 2 resolution for a business-as-usual scenario and a high renewables penetration scenario. We find that 30% more land will be needed in the high renewables scenario as compared to business-as-usual, and that 75% of that development is projected to be located within 10 km of natural areas.

Mongird, Kendall [Pacific Northwest National Labor↗

DOE OSTI2017

Projected coupled cluster theory

Coupled cluster theory is the method of choice for weakly correlated systems. But in the strongly correlated regime, it faces a symmetry dilemma, where it either completely fails to describe the system or has to artificially break certain symmetries. On the other hand, projected Hartree-Fock theory captures the essential physics of many kinds of strong correlations via symmetry breaking and restoration. Here, we combine and try to retain the merits of these two methods by applying symmetry projection to broken symmetry coupled cluster wave functions. The non-orthogonal nature of states resulting from the application of symmetry projection operators furnishes particle-hole excitations to all orders, thus creating an obstacle for the exact evaluation of overlaps. Here we provide a solution via a disentanglement framework theory that can be approximated rigorously and systematically. Results of projected coupled cluster theory are presented for molecules and the Hubbard model, showing that spin projection significantly improves unrestricted coupled cluster theory while restoring good quantum numbers. The energy of projected coupled cluster theory reduces to the unprojected one in the thermodynamic limit, albeit at a much slower rate than projected Hartree-Fock.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

A minimal model of fire-vegetation feedbacks and disturbance stochasticity generates alternative stable states in grassland–shrubland–woodland systems

Altered disturbance regimes in the context of global change are likely to have profound consequences for ecosystems. Interactions between fire and vegetation are of particular interest, as fire is a major driver of vegetation change, and vegetation properties (e.g., amount, flammability) alter fire regimes. Mediterranean-type ecosystems (MTEs) constitute a paradigmatic example of temperate fire-prone vegetation. Although these ecosystems may be heavily impacted by global change, disturbance regime shifts and the implications of fire-vegetation feedbacks in the dynamics of such biomes are still poorly characterized. We developed a minimal modeling framework incorporating key aspects of fire ecology and successional processes to evaluate the relative influence of extrinsic and intrinsic factors on disturbance and vegetation dynamics in systems composed of grassland, shrubland, and woodland mosaics, which characterize many MTEs. In this theoretical investigation, we performed extensive simulations representing different background rates of vegetation succession and disturbance regime (fire frequency and severity) processes that reflect a broad range of MTE environmental conditions. Varying fire-vegetation feedbacks can lead to different critical points in underlying processes of disturbance and sudden shifts in the vegetation state of grassland-shrubland-woodland systems, despite gradual changes in ecosystem drivers as defined by the environment. Vegetation flammability and disturbance stochasticity effectively modify system behavior, determining its heterogeneity and the existence of alternative stable states in MTEs. Small variations in system flammability and fire recurrence induced by climate or vegetation changes may trigger sudden shifts in the state of such ecosystems. The existence of threshold dynamics, alternative stable states, and contrasting system responses to environmental change has broad implications for MTE management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A gated graph transformer for protein complex structure quality assessment and its performance in CASP15

Abstract Motivation Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery. Results In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures. Availability and implementation The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Steepest-descent algorithm for simulating plasma-wave caustics via metaplectic geometrical optics

The design and optimization of radiofrequency-wave systems for fusion applications is often performed using ray-tracing codes, which rely on the geometrical-optics (GO) approximation. However, GO fails at wave cutoffs and caustics. To accurately model the wave behavior in these regions, more advanced and computationally expensive “full-wave” simulations are typically used, but this is not strictly necessary. A new generalized formulation called metaplectic geometrical optics (MGO) has been proposed that reinstates GO near caustics. The MGO framework yields an integral representation of the wavefield that must be evaluated numerically in general. We present an algorithm for computing these integrals using Gauss-Freud quadrature along the steepest-descent contours. Benchmarking is performed on the standard Airy problem, for which the exact solution is known analytically. Furthermore, the numerical MGO solution provided by the new algorithm agrees remarkably well with the exact solution and significantly improves on previously derived analytical approximations of the MGO integral.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Normalizing Flows for Microscopic Many-Body Calculations: An Application to the Nuclear Equation of State

We report that normalizing flows are a class of machine learning models used to construct a complex distribution through a bijective mapping of a simple base distribution. We demonstrate that normalizing flows are particularly well suited as a Monte Carlo integration framework for quantum many-body calculations that require the repeated evaluation of high-dimensional integrals across smoothly varying integrands and integration regions. As an example, we consider the finite-temperature nuclear equation of state. An important advantage of normalizing flows is the ability to build highly expressive models of the target integrand, which we demonstrate enables precise evaluations of the nuclear free energy and its derivatives. Furthermore, we show that a normalizing flow model trained on one target integrand can be used to efficiently calculate related integrals when the temperature, density, or nuclear force is varied. This work will support future efforts to build microscopic equations of state for numerical simulations of supernovae and neutron star mergers that employ state-of-the-art nuclear forces and many-body methods.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

Evaluating Online Global Recovery with Fenix Using Application-Aware In-Memory Checkpointing Techniques

Exascale systems promise the potential for computation at unprecedented scales and resolutions, but achieving exascale by the end of this decade presents significant challenges. A key challenge is due to the very large number of cores and components and the resulting mean time between failures (MTBF) in the order of hours or minutes. Since the typical run times of target scientific applications are longer than this MTBF, fault tolerance techniques will be essential. An important class of failures that must be addressed is process or node failures. While checkpoint/restart (C/R) is currently the most widely accepted technique for addressing processor failures, coordinated, stable-storage-based global C/R might be unfeasible at exascale when the time to checkpoint exceeds the expected MTBF. This paper explores transparent recovery via implicitly coordinated, diskless, application-driven checkpointing as a way to tolerate process failures in MPI applications at exascale. The discussed approach leverages User Level Failure Mitigation (ULFM), which is being proposed as an MPI extension to allow applications to create policies for tolerating process failures. Specifically, this paper demonstrates how different implementations of application-driven in-memory checkpoint storage and recovery compare in terms of performance and scalability. We also experimentally evaluate the effectiveness and scalability of the Fenix online global recovery framework on a production system -the Titan Cray XK7 at ORNL- and demonstrate the ability of Fenix to tolerate dynamically injected failures using the execution of four benchmarks and mini-applications with different behaviors.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Decision-Dependent Uncertainty-Aware Distribution System Planning Under Wildfire Risk

The interaction between power systems and wildfires can be dangerous and costly. Distribution grids can be liable for the outbreak of wildfires during extreme weather. In wildfire-prone areas, investment planning should consider the impact of operational actions on wildfire-related uncertainties affecting line failure likelihood. Here, in this case, endogenous-based uncertainty modeling should comprise the backbone of the investment planning model viz-a-viz the inability of standard exogenous-based uncertainty modeling. Therefore, we propose a decision-dependent uncertainty (DDU) aware methodology to optimize investment portfolios for distribution systems, considering that high power-flow levels in high-threat areas can ignite wildfires and increase line failure probability. The methodology identifies the best combination of upgrades (new lines, hardening existing lines, and placing switching devices). Methodologically, we propose a two-stage distributionally robust planning optimization problem with DDU that considers the distribution system's multiperiod operation. The first stage determines optimal switching actions and line investments, and the second stage evaluates the worst-case expected operational cost under a DDU framework designed to account for the endogenous impact of power-flow levels and hardening investment decisions in the line failure probabilities. An iterative method is tailored to handle the problem and numerical experiments demonstrate a more prepared grid to deal with wildfire risk.

Power systems investment planning↗

DOE OSTI2020

BioSTEAMDevelopmentGroup/biosteam

BioSTEAM is a fast and flexible package for the design, simulation, and techno-economic analysis of biorefineries under uncertainty. BioSTEAM’s framework is built to streamline and automate early-stage technology evaluations and to enable rigorous sensitivity and uncertainty analyses. Complete biorefinery configurations are available at the Bioindustrial-Park GitHub repository, BioSTEAM’s premier repository for biorefinery models and results. The long-term growth and maintenance of BioSTEAM is supported through both community-led development and the research institutions invested in BioSTEAM. Through the open-source and community-lead platform, BioSTEAM aims to foster communication and transparency within the biorefinery research community for an integrated effort to expedite the evaluation of candidate biofuels and bioproducts. Additionally, an agile life cycle assessment (LCA) platform has been designed to interface with BioSTEAM, BioSTEAM-LCA. This open-source, installable package allows users to perform streamlined LCAs of biorefineries. The focus of BioSTEAM-LCA is to streamline and automate early-stage environmental impact analyses of processes and technologies, and to enable rigorous sensitivity and uncertainty analyses linking process design, performance, economics, and environmental impacts.

Cortes-Peña, Yoel↗

DOE OSTI2024

Promoting Sustainable Transportation Modes: A Systematic Review of Behavior-Change Strategies

In previous studies, many travel-behavior-change strategies often relied on single behavior determinants or psychological theories, overlooking the incorporation of sociopsychological theories for guidance in their design. Integrating these theories could offer consistent guidance for program developers and enhance intervention effectiveness. This paper systematically reviews interventions targeting travel-behavior change, with a focus on self-determination theory and its principles of satisfying individuals’ competence, autonomy, and relatedness needs for enacting change. Additionally, experiment design methods, including randomized controlled trials and quasi-experimental designs, are reviewed and discussed. Key findings highlight the effectiveness of personalized interventions and integrating feedback with goal-setting strategies. Given the limited direct references to sociopsychological theories in existing studies, we explore relevant sociopsychological theories applicable to travel-behavior-change programs to provide examples of how strategies could be designed based on them. This review contributes valuable insights into the development of strategies for changing travel behavior, offering a theoretical framework for researchers and practitioners to guide intervention design, experimentation, and evaluation. In conclusion, leveraging these theories not only facilitates reproducibility but also provides a standardized approach for transportation demand management program developers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Carbon Storage Planning Framework Dashboard

The Carbon Storage Planning Framework dashboard (beta version) provides easy access to explore, query, and evaluate multiple data layers to support and accelerate carbon storage resource and feasibility assessments and planning efforts. The interactive dashboard currently contains a range of authoritative datasets associated with geologic, technical, social, and environmental factors. The data sets can be filtered geographically for an area of interest to update statistics and charts within the dashboard. Terms of Use: NETL is sharing this beta/prototype tool with the request that users will provide feedback to NETL about the function and content of the dashboard, alerting NETL to any bugs or providing suggestions for additional resources to help mature the platform.

CCS,EDX4CCS,carbon storage,dashboard↗

DOE OSTI2020

General Analysis of Data Collected from DOE Lidar Buoy Deployments Off Virginia and New Jersey

Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinel lidar buoys for the U.S. Department of Energy’s Wind Energy Technologies. The purpose of these buoys is to collect hub-height winds and supporting meteorological and oceanographic information to facilitate the development of offshore wind energy in the U.S. In general, each buoy is deployed for a year or more at a given location in order to capture at least a full annual cycle of weather conditions. The initial deployment for one buoy was off the coast of Virginia beginning in 2015, and the other buoy was first deployed off the coast of New Jersey beginning in 2016. Over the last two years, PNNL has had an opportunity to analyze the data collected during these first two deployments. This report describes a substantial analysis of data collected by the two lidar buoys operated off the coasts of Virginia and New Jersey. The centerpiece instrument for each buoy as deployed off Virginia and New Jersey is a lidar system, which is designed to measure the horizontal wind vector from approximately 40 m to 200 m above the sea surface with vertical resolution of 40 m. Since the initial deployments, the original lidars have been replaced with more powerful Leosphere 866 v2 systems. The analyses in this report will apply to the original Vindicator systems. In addition to the wind profiles from the lidars, the buoys collect near-surface measurements of wind speed and direction, air temperature, relative humidity, barometric pressure, and solar irradiance. Oceanographic variables measured include the two-dimensional wave spectrum, water temperature and conductivity, and ocean current vectors to a depth of 90 m. An assessment of overall data recovery and a basic analysis of the data collected was provided in a previous report This report substantially extends that analysis. The various sections describe the development and application of an inertial measurement unit (IMU) data recovery scheme for the New Jersey deployment; a climatological analysis of winds at hub height and at the surface together with thermodynamic variables measured at the surface for the full deployment periods; an analysis of oceanographic observations describe sea state; the development of a refinement for NOAA’s WaveWatch III model to allow its application to near-shore areas; a basic climatology of ocean currents observed from the buoys; an analysis of observed winds in the framework of Monin-Obukhov Similarity Theory; and the development and evaluation of techniques to extract turbulence intensity and turbulence kinetic energy from the lidars. The analyses contained in this report provide a great deal of new information about offshore conditions on the U.S. East Coast. In addition, the experience gained will inform both configurations and analysis of data from future deployments of these lidar buoy systems.

17 WIND ENERGY↗

DOE OSTI2020

Sensor Impacts Evaluation and Verification: Expert Interview Responses

The sensor configuration/deployment method has critical impacts on energy efficient building control and thermal comfort. However, traditional sensor techniques for building operation and fault detection and diagnostics (FDD) are not necessarily optimal in terms of energy efficiency and thermal comfort, and their global effects are not thoroughly investigated. In an effort to address and overcome this limitation, a multilaboratory and multiyear project, “Sensor Impact Evaluation and Verification,” was proposed. Its purpose is to develop a framework to investigate the impacts of sensor deployment and configuration on building energy optimization, FDD, occupant thermal comfort, and potentially grid efficiency. The first project task was a literature review to establish a solid knowledge of and a background related to sensor technologies and placement. To accomplish this task, an extensive review of previous research literature was performed. A series of expert interviews were conducted to augment the findings of the literature review. This report summarizes the interview design and interview results and findings. The interview was designed and performed to (1) investigate the current status and limitations of sensor configuration, (2) identify the research gaps and expectations for potential improvements in sensor configuration and deployment, and (3) integrate expert (e.g., researcher, building operation practitioner) knowledge and experience to develop use-case scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Statistically-driven Experimental Design to Improve Reference-free Quantification of Small Molecules by Liquid Chromatography-Mass Spectrometry

Non-targeted analysis of small molecules and metabolites in unknown, complex samples using liquid chromatography-tandem mass spectrometry remains challenging. One of the main bottlenecks is the extensive unannotated regions of metabolomics mass spectrometry data, resulting in knowledge gaps. Small molecule annotation in mass spectrometry data has conventionally relied on reference standards and libraries for compound identification and confirmation, which can constrain compound identification to those molecules already known, thus limiting the ability to discover new knowledge and new markers. Retention time prediction can facilitate and expedite unknown compound identification in non-targeted analysis of complex metabolomics samples. Additionally, accurate retention time predictions can also inform sample mixture design for LC-MS/MS analyses. However, current machine learning-based methods for retention time prediction are typically developed for specific chromatographic platforms and are not generalizable across scales. And while technologies and methods to improve reference-free metabolite identification for more comprehensive annotation of unknowns has received much attention, development of the same for quantitation without reference standards has been much more limited, despite its importance in toxicological, environmental, food safety, forensics, and clinical applications. We believe that a reference-free quantitation strategy that exploits mass spectrometry data already collected for reference-free identification can provide much more insight on unknowns, and move the metabolomics field for more complete unknowns characterization. As such, we pursue two efforts to improve upon current state-of-the-art methods in non-targeted analysis: (1) machine learning-based retention time prediction and (2) statistical design of experiments framework for reference-free quantitation. In this work, we develop and demonstrate (1) a generalizable retention time prediction capability across chromatographic conditions and scales, and (2) a statistical design-based framework for response factor contribution elucidation and reference-free quantitation. Evaluation of our retention time prediction model, PrediToR, showed approximately 24% improvement over current models, and we observed approximately 10X improvement in concentration estimation accuracy from our statistical design-based response factor model over a primarily ionization efficiency-based model. We expect that future efforts to improve upon these new capabilities will further advance non-targeted analysis of small molecules towards truly reference-free metabolomics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗