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At least 127 records · Page 7

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DOE OSTI2020

Extracting dependencies between network assets using deep learning

A network analysis tool receives network flow information and uses deep learning—machine learning that models high-level abstractions in the network flow information—to identify dependencies between network assets. Based on the identified dependencies, the network analysis tool can discover functional relationships between network assets. For example, a network analysis tool receives network flow information, identifies dependencies between multiple network assets based on evaluation of the network flow information, and outputs results of the identification of the dependencies. When evaluating the network flow information, the network analysis tool can pre-process the network flow information to produce input vectors, use deep learning to extract patterns in the input vectors, and then determine dependencies based on the extracted patterns. The network analysis tool can repeat this process so as to update an assessment of the dependencies between network assets on a near real-time basis.

Carroll, Thomas E.↗

DOE OSTI2020

The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio

Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, here, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences in time series meter data, 2) compares daily MP values with typical-day MP distributions using the Kolmogorov-Smirnov test, and 3) identifies daily load profile discords in a large building portfolio. We evaluate ALDI using the metering data of both an academic campus and a residential neighborhood. Our results demonstrate that ALDI efficiently discovers measurement errors by system malfunctions and low energy consumption days in the academic campus portfolio, and it detects unique load shape patterns likely driven by occupant behavior and extreme weather conditions in the residential neighborhood.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

WigglyRivers: A tool to characterize the multiscale nature of meandering channels

Channel sinuosity is ubiquitous along river networks, producing complex patterns that encapsulate and influence morphodynamic processes and ecosystem services. Accurately characterizing these patterns is challenging with traditional curvature-based algorithms. Here, in this study, we present WigglyRivers, a Python package that builds on existing wavelet-based methods to create an unsupervised meander identification and characterization tool. The package uses planimetric information the user provides or from the USGS’s High-Resolution National Hydrography Dataset to characterize individual reaches or entire river networks. WigglyRivers also includes a supervised river identification tool for manually selecting individual meandering features. Here, we provide examples of idealized river transects and show the capabilities of WigglyRivers. We also use the supervised identification tool to validate the unsupervised identification on river transects across the continental US. WigglyRivers is a tool to understand better the multiscale characteristics of river networks and the link between river geomorphology and river corridor connectivity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Utility of parentage‐based tagging for monitoring Coho salmon ( Oncorhynchus kisutch ) in the interior Columbia River basin

Abstract By the 1980s, after decades of declining numbers in the mid‐1900s, Coho salmon ( Oncorhynchus kisutch ) were considered extirpated from the interior Columbia River. In the mid‐1990s, the Confederated Tribes of the Umatilla Indian Reservation, the Confederated Tribes and Bands of the Yakama Nation, and the Nez Perce Tribe began successful reintroduction programs of Coho salmon upstream of Bonneville Dam, but which were initially sourced from lower Columbia River hatcheries. Here we present the first Coho salmon parentage‐based tagging (PBT) baseline from seven hatchery programs located in the interior Columbia River basin, and two sites at or downstream of Bonneville Dam, composed of over 32,000 broodstock samples. Analyses of baseline collections revealed that genetic structure followed a temporal pattern based on 3‐year broodlines rather than geographic location or stocking history. Across hatchery programs, similar levels of genetic diversity was present. The PBT baseline provided multiple direct applications such as identification of origin for Coho salmon collected in a mixed stock at Priest Rapids Dam and the detection of the proportion and distribution of hatchery‐origin fish on the spawning grounds in the Methow River basin. The PBT baseline for Coho salmon is freely available for use and can be downloaded from FishGen.net .

Horn, Rebekah L.↗

DOE OSTI2025

DriveSense: A Noise-Resilient Framework for Driving Mode Identification

Accurate drive mode classification is essential for enhancing the reliability and predictive maintenance of heavy-duty electric trucks. This study proposes a novel fuzzy logic-based framework, DriveSense, for real-time drive mode classification, addressing key challenges such as sensor noise, transitional behaviors, and computational efficiency. The proposed approach integrates a two-stage filtering pipeline, combining adaptive outlier removal and a dynamic Kalman filter to enhance data quality. A fuzzy inference system with smoothened trapezoidal membership functions is then applied to classify driving modes into standstill, constant speed, acceleration, and deceleration while mitigating the effects of noise and edge cases. Performance evaluation using real-world and simulated drive cycles demonstrates significant improvements in classification accuracy (up to 97.8%), F1-score (up to 0.97), and robustness against noise, while reducing false positives. Comparative analysis against baseline models, demonstrates DriveSense’s superior accuracy and generalizability across diverse driving patterns. The framework’s lightweight and interpretable fuzzy inference engine operates with low computational latency, ensuring compatibility with real-time embedded systems typical of heavy-duty electric trucks. Moreover, DriveSense models transitional behaviors through overlapping fuzzy sets and adaptive borderline classification logic, enabling smooth identification of subtle shifts such as rolling stops or gradual deceleration. These results highlight DriveSense’s potential to enhance predictive maintenance strategies, reduce downtime, and support scalable, fleet-wide diagnostics.

Kumar, Praveen [Oak Ridge National Laboratory (ORN↗

DOE OSTI2023

The use of gas chromatography – high resolution mass spectrometry for suspect screening and non-targeted analysis of per- and polyfluoroalkyl substances

This study is a workflow development for the analysis, identification, and categorization of per- and polyfluoroalkyl substances (PFAS) using gas chromatography-high resolution mass spectrometry (GC-HRMS) with non-targeted analysis (NTA) and suspect screening techniques. The behavior of various PFAS in a GC-HRMS was studied with regards to retention indices, ionization susceptibility, fragmentation patterns, etc. A custom PFAS database was constructed from 141 diverse PFAS. The database contains mass spectra from electron ionization (EI) mode, as well as MS and MS/MS spectra from positive and negative chemical ionization (PCI and NCI, respectively) modes. Common fragments of PFAS were identified across a diverse set of 141 PFAS analyzed. A workflow for suspect screening of PFAS and partially fluorinated products of incomplete combustion/destruction (PICs/PIDs) was developed which utilized both the custom PFAS database and external databases. PFAS and other fluorinated compounds were identified in both a challenge sample (designed to test the identification workflow) and incineration samples suspected to contain PFAS and fluorinated PICs/PIDs. The challenge sample resulted in a 100% true positive rate (TPR) for PFAS which were present in the custom PFAS database. In conclusion, several fluorinated species were tentatively identified in the incineration samples using the developed workflow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Data for Training and Testing Radiation Detection Algorithms in an Urban Environment

The US government routinely performs radiological response deployments to search for the presence of illicit nuclear materials (e.g., highly enriched uranium and weapons-grade plutonium) in a specified area. The deployments can be intelligence driven, in support of law enforcement, and for planned events such as WrestleMania, presidential inaugurations, or political conventions. In a typical deployment, radiation detection systems carried by human operators or mounted on vehicles move in a clearing pattern through the search area. Search teams rely on radiation detection algorithms running on these systems in real time to alert them to the presence of an illicit threat source. The detection and identification of sources is complicated by large variation of natural radiation background throughout a search area and the potential presence of localized non-threat sources such as patients undergoing treatment with medical isotopes. As a result, detection algorithms must be carefully balanced between missing real sources (false negatives) and reporting too many false alarms (false positives).The purpose of this data set is to spur innovations in detecting, identifying, and localizing nuclear materials inurban search missions.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2026

Attention-based explainability for structure–property relationships

Machine learning methods are emerging as a universal paradigm for constructing correlative structure–property relationships in materials science based on multimodal characterization. However, this necessitates the development of methods for the physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure–property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTiO3 thin films as a case study. Through the analysis of attention scores, we disentangle the influence of distinct domain patterns on the polarization switching process. The attention-based Transformer model is explored both as a direct interpretability tool and as a surrogate for explaining representations learned via unsupervised machine learning, enabling the identification of physically grounded correlations. We compare attention-derived interpretability scores with classical SHapley Additive exPlanations analysis and show that, in contrast to applications in natural language processing, attention mechanisms in materials science exhibit high efficiency in highlighting meaningful structural features.

Slautin, Boris [Independent Researcher]↗

DOE OSTI2022

Systematic quark/gluon identification with ratios of likelihoods

Discriminating between quark- and gluon-initiated jets has long been a central focus of jet substructure, leading to the introduction of numerous observables and calculations to high perturbative accuracy. At the same time, there have been many attempts to fully exploit the jet radiation pattern using tools from statistics and machine learning. We propose a new approach that combines a deep analytic understanding of jet substructure with the optimality promised by machine learning and statistics. After specifying an approximation to the full emission phase space, we show how to construct the optimal observable for a given classification task. This procedure is demonstrated for the case of quark and gluons jets, where we show how to systematically capture sub-eikonal corrections in the splitting functions, and prove that linear combinations of weighted multiplicity is the optimal observable. In addition to providing a new and powerful framework for systematically improving jet substructure observables, we demonstrate the performance of several quark versus gluon jet tagging observables in parton-level Monte Carlo simulations, and find that they perform at or near the level of a deep neural network classifier. Combined with the rapid recent progress in the development of higher order parton showers, we believe that our approach provides a basis for systematically exploiting subleading effects in jet substructure analyses at the Large Hadron Collider (LHC) and beyond.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Methane emission hotspots in a boreal forest-fen mosaic potentially linked to deep taliks

Permafrost thaw is transforming boreal forests into mosaics of wetlands and drier uplands. Topographic controls on hydrological and ecological conditions impact methane (CH 4 ) fluxes, contributing to uncertainty in local and regional CH 4 budgets and underlying drivers. The objective of this study was to explore CH 4 fluxes and their drivers in a transitioning boreal forest-fen ecosystem (Goldstream Valley, Alaska, USA). This landscape is characterized by thawing discontinuous permafrost and heterogeneous mosaics of fens, collapse-scar channels, and small mounds of permafrost soils. From a survey in July 2021, observed chamber CH4 fluxes included fen areas with intermediate to very high emissions (29.8–635.3 mg CH 4 m −2 d −1 ), clustered locations with CH 4 uptake (−2.11 to −0.7 mg CH 4 m −2 d −1 ), and three anomalous emission hotspots (342.4–772.4 mg CH 4 m −2 d −1 ) that were located near samples with lower emissions. Some surface and near-surface variables partially explained the spatial variation in CH 4 flux. Log-transformed CH 4 flux had a positive linear relationship with soil moisture at 20 cm depth ( R 2 = 0.31, p -value < 1e-5) and negative linear relationships with microtopography ( R 2 = 0.13, p -value < 0.006) and slope ( R 2 = 0.28, p -value < 2e-5). Methane emissions generally occurred in flat, wet, graminoid-dominated fens, whereas CH 4 uptake occurred on permafrost mounds dominated by feather mosses and woody vegetation. However, the CH 4 hotspots occurred on drier, slightly sloped locations with low or undetectable near-surface methanogen abundance, suggesting that CH 4 was produced in deeper soils. When the hotspot samples were omitted, log-transformed CH 4 flux had a positive linear relationship with near-surface methanogen abundance ( R 2 = 0.29, p -value = 0.0023), and stronger linear relationships with soil moisture, slope, and soil macronutrient concentrations. Our findings suggest that some CH 4 emission hotspots could arise from CH 4 in deep taliks. The inference that methanogenesis occurs in deep taliks was strengthened by the identification of intrapermafrost taliks across the study area using low-frequency geophysical induction. This study assesses surface spatial heterogeneity in the context of subsurface permafrost conditions and highlights the complexity of CH 4 flux patterns in transitioning forest-wetland ecosystems. To better inform regional CH 4 budgets, further research is needed to understand the spatial distribution of terrestrial CH 4 hotspots and to resolve their surface, near-surface, and subsurface drivers.

boreal↗

DOE OSTI2025

Modelling detector-specific reconstruction uncertainties in LAr-TPC

The Short-Baseline Neutrino (SBN) program features three Liquid Argon Time Projection Chamber (LAr-TPC) detectors positioned along the Booster Neutrino Beam (BNB) axis: the Short Baseline Neutrino Near Detector, MicroBooNE, and the ICARUS T600. As the largest operational LAr-TPC, ICARUS T600 serves as the far detector, located 600 m from the BNB target. While its primary goal is to record neutrino events, it also detects other ionizing events, including cosmic rays. This work focuses on analyzing and modeling detector-specific reconstruction uncertainties in LAr-TPC. These inefficiencies, identified during the Pattern Recognition phase handled by the PANDORA algorithm, impact subsequent Particle Fits and Offline Analysis. Specifically, inaccuracies in track reconstruction can lead to significant physical consequences, such as erroneous particle energy estimates and poor Particle Identification (PID), reducing the efficiency of neutrino event characterization. A key issue addressed is split tracks, caused by missing hits or incomplete track stitching by PANDORA. The aim of this internship is to characterize, model, and quantify the impact of split tracks on track reconstruction.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response

Motivation: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems such as protein complexes, metabolic reactions, and signal transduction pathways. Hypergraphs are generalizations of graphs that naturally model multi-way interactions in data, and we therefore seek to understand how they can more faithfully identify, and potentially predict, complex relationships in genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges are differentially expressed genes and vertices represent conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Our results demonstrate the utility of using hypergraphs to represent complex biological systems, and highlight potentially interesting biological results about host response to highly pathogenic viruses.

systems biology, hypergraph, viral infection, biol↗

DOE OSTI2024

On principles of emergent organization

After more than a century of concerted effort, physics still lacks basic principles of spontaneous organization. To appreciate why, we first state the problem, outline historical approaches, and survey the present state of the physics of self-organization. This frames the particular challenges arising from mathematical intractability and the resulting need for computational approaches, as well as those arising from a chronic failure to define structure. Then an overview of two modern mathematical formulations of organization—intrinsic computation and evolution operators—lays out a way to overcome these challenges. Together, the vantage point they afford shows how to account for the emergence of structured states via a statistical mechanics of systems arbitrarily far from equilibrium. The result is a constructive path forward to principles of organization that builds on mathematical identification of structure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Hybridization History and Repetitive Element Content in the Genome of a Homoploid Hybrid, Yucca gloriosa (Asparagaceae)

Hybridization in plants results in phenotypic and genotypic perturbations that can have dramatic effects on hybrid physiology, ecology, and overall fitness. Hybridization can also perturb epigenetic control of transposable elements, resulting in their proliferation. Understanding the mechanisms that maintain genomic integrity after hybridization is often confounded by changes in ploidy that occur in hybrid plant species. Homoploid hybrid species, which have no change in chromosome number relative to their parents, offer an opportunity to study the genomic consequences of hybridization in the absence of change in ploidy. Yucca gloriosa (Asparagaceae) is a young homoploid hybrid species, resulting from a cross between Yucca aloifolia and Yucca filamentosa. Previous analyses of ~11 kb of the chloroplast genome and nuclear-encoded microsatellites implicated a single Y. aloifolia genotype as the maternal parent of Y. gloriosa. Using whole genome resequencing, we assembled chloroplast genomes from 41 accessions of all three species to re-assess the hybrid origins of Y. gloriosa. We further used re-sequencing data to annotate transposon abundance in the three species and mRNA-seq to analyze transcription of transposons. The chloroplast phylogeny and haplotype analysis suggest multiple hybridization events contributing to the origin of Y. gloriosa, with both parental species acting as the maternal donor. Transposon abundance at the superfamily level was significantly different between the three species; the hybrid was frequently intermediate to the parental species in TE superfamily abundance or appeared more similar to one or the other parent. In only one case—Copia LTR transposons—did Y. gloriosa have a significantly higher abundance relative to either parent. Expression patterns across the three species showed little increased transcriptional activity of transposons, suggesting that either no transposon release occurred in Y. gloriosa upon hybridization, or that any transposons that were activated via hybridization were rapidly silenced. The identification and quantification of transposon families paired with expression evidence paves the way for additional work seeking to link epigenetics with the important trait variation seen in this homoploid hybrid system.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Leveraging large language models to automate the identification of healthcare access barriers for veterans

Objective: To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency. Methods: We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis. Results: The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1. Conclusion: Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.

Healthcare access barriers↗

DOE OSTI2020

Charged particle track reconstruction with SπRIT Time Projection Chamber

In this work, we present a software framework, SπRITROOT, which is capable of track reconstruction and analysis of heavy-ion collision events recorded with the SRIT time projection chamber. The track-fitting toolkit GENFIT and the vertex reconstruction toolkit RAVE are applied to a box-type detector system. A pattern recognition algorithm which performs helix track finding and handles overlapping pulses is described. The performance of the software is investigated using experimental data obtained at the Radioactive Isotope Beam Facility (RIBF) at RIKEN. This work focuses on data from 132 Sn + 124 Sn collision events with beam energy of 270 AMeV. Particle identification is established using < dE/dx >and magnetic rigidity, with pions, hydrogen isotopes, and helium isotopes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Spring Regional Sea Surface Temperatures as a Precursor of European Summer Heatwaves

Different spring and early summer North Atlantic sea surface temperature anomalies (SSTAs) have been shown to precede recent European summer heatwaves (EuSHWs). So far, the limited number of observed events associated with several physical mechanisms has prevented a robust identification of SSTAs as precursors. Here, we extend beyond previous studies by combining 100 historical simulations (1850–2005) of the MPI Grand-Ensemble with an explainable neural-network method. We find that the spring tripolar North Atlantic pattern with positive SSTAs in the Subtropical Gyre is a precursor of EuSHWs. In addition, positive SSTAs west of the Iberian Peninsula, and in the North Sea, the Baltic Sea and the Mediterranean Sea relate to distinct early summer soil moisture anomaly patterns and are precursors of western and southeastern EuSHWs, respectively. While the phase of the tripolar North Atlantic pattern indicates whether a EuSHW might emerge, regional SSTAs indicate the spatial characteristics of EuSHWs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Mass spectrometry imaging of natural carbonyl products directly from agar-based microbial interactions using 4-APEBA derivatization

Aliphatic carboxylic acids, aldehydes, and ketones play diverse roles in microbial adaptation to their microenvironment, from excretion as toxins to adaptive metabolites for membrane fluidity. However, the spatial distribution of these molecules throughout biofilms and how microbes in these environments exchange these molecules remain elusive for many of these bioactive species due to inefficient molecular imaging strategies. Herein, we apply on-tissue chemical derivatization (OTCD) using 4-(2-((4-bromophenethyl)dimethylammonio)ethoxy)benzenaminium dibromide (4-APEBA) on a co-culture of a soil bacterium (Bacillus subtilis NCIB 3610) and fungus (Fusarium sp. DS 682) grown on agar as our model system. Using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), we spatially resolved more than 300 different metabolites containing carbonyl groups within this model system. Various spatial patterns are observable in these species, which indicate possible extracellular or intercellular processes of the metabolites and their up- or downregulation during microbial interaction. The unique chemistry of our approach allowed us to bring additional confidence in accurate carbonyl identification, especially when multiple isomeric candidates were possible, and this provided the ability to generate hypotheses about the potential role of some aliphatic carbonyls in this B. subtilis/Fusarium sp. interaction. The results shown here demonstrate the utility of 4-ABEBA-based OTCD MALDI-MSI in probing interkingdom interactions directly from microbial co-cultures, and these methods will enable future microbial interaction studies with expanded metabolic coverage.

4-APEBA↗