Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Topic modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Simplified Predictive Models for CO 2 Sequestration Performance Assessment: Research Topical Report on Task #4 - Reduced-Order Method (ROM) Based Models

Reduced-order models provide a means for greatly accelerating the detailed simulations that will be required to manage CO 2 storage operations. In this work, we investigate the use of one such method, POD-TPWL, which has previously been shown to be effective in oil reservoir simulation problems. This method combines trajectory piecewise linearization (TPWL), in which the solution to a new (test) problem is represented through a linearization around the solution to a previously-simulated (training) problem, with proper orthogonal decomposition (POD), which enables solution states to be expressed in terms of a relatively small number of parameters. We describe the application of POD-TPWL for CO 2 -water systems simulated using a compositional procedure. Stanford’s Automatic Differentiation-based General Purpose Research Simulator (AD-GPRS) performs the full-order training simulations and provides the output (derivative matrices and system states) required by the POD-TPWL method. A new POD-TPWL capability introduced in this work is the use of horizontal injection wells that operate under rate (rather than bottom-hole pressure) control. Simulation results are presented for CO 2 injection into a synthetic aquifer and into a simplified model of the Mount Simon formation. Test cases involve the use of time-varying well controls that differ from those used in training runs. Results of reasonable accuracy are consistently achieved for relevant well quantities. Runtime speedups of around a factor of 370 relative to full- order AD-GPRS simulations are achieved, though the preprocessing needed for POD-TPWL model construction corresponds to the computational requirements for about 2.3 full-order simulation runs. A preliminary treatment for POD-TPWL modeling in which test cases differ from training runs in terms of geological parameters (rather than well controls) is also presented. Results in this case involve only small differences between training and test runs, though they do demonstrate that the approach is able to capture basic solution trends. The impact of some of the detailed numerical treatments within the POD-TPWL formulation is considered in an Appendix.

Mishra, Srikanta↗
DOE OSTI2023

Large language models generate functional protein sequences across diverse families

Deep-learning language models have shown promise in various biotechnological applications, including protein design and engineering. Here, in this paper, we describe ProGen, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. The model was trained on 280 million protein sequences from >19,000 families and is augmented with control tags specifying protein properties. ProGen can be further fine-tuned to curated sequences and tags to improve controllable generation performance of proteins from families with sufficient homologous samples. Artificial proteins fine-tuned to five distinct lysozyme families showed similar catalytic efficiencies as natural lysozymes, with sequence identity to natural proteins as low as 31.4%. ProGen is readily adapted to diverse protein families, as we demonstrate with chorismate mutase and malate dehydrogenase.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

SociAL Sensor Analytics: Measuring Phenomenology at Scale

The objective of this paper is to present a system for interrogating immense social media streams through analytical methodologies that characterize topics and events critical to tactical and strategic planning. First, we propose a conceptual framework for interpreting social media as a sensor network. Time-series models and topic clustering algorithms are used to implement this concept into a functioning analytical system. Next, we address two scientific challenges: 1) to understand, quantify, and baseline phenomenology of social media at scale, and 2) to develop analytical methodologies to detect and investigate events of interest. This paper then documents computational methods and reports experimental findings that address these challenges. Ultimately, the ability to process billions of social media posts per week over a period of years enables the identification of patterns and predictors of tactical and strategic concerns at an unprecedented rate through SociAL Sensor Analytics (SALSA).

analytics↗
DOE OSTI2024

The application of synthetic antibacterial minerals to combat topical infections: exploring a mouse model of MRSA infection

Abstract The development of new antibiotics has stalled, and novel strategies are needed as we enter the age of antibiotic resistance. Certain naturally occurring clays have been shown to be effective in killing antibiotic resistant bacteria. However, these natural clays are too variable to be used in clinical settings. Our study shows that synthetic antibacterial minerals exhibit potent antibacterial activity against topical MRSA infections and increase the rate of wound closure relative to controls. The antibacterial minerals maintain a redox cycle between Fe 2+ /Fe 3+ and the surfaces of pyrite minerals, which act as a semiconductor and produce reactive oxygen species (ROS), while smectite minerals act as a cation exchange reservoir. Acidic conditions are maintained throughout the application of the hydrated minerals and can mitigate the alkaline pH conditions observed in chronic non-healing wounds. These results provide evidence for the strategy of ‘iron overload’ to combat antibiotic resistant infections through the maintained release of Fe 2+ and generation of ROS via distinct geochemical reactions that can break the chronic wound damage cycle.

58 GEOSCIENCES↗
DOE OSTI2022

Best practice reporting guideline for building stock energy models

Buildings are responsible for 38% of global greenhouse gas (GHG) emissions and, therefore, pathways to reduce their impact are crucial to achieve climate targets. Building stock energy models (BSEMs) have long been used as a tool to assess the current and future energy demand and environmental impact of building stocks. BSEMs have become more and more complex and are often tailored to case-specific datasets, which results in a high degree of heterogeneity among models. This heterogeneity, together with a lack of consistency in the reporting hinders the understanding of these models and, thereby, an accurate interpretation and comparison of results. In this paper we present a reporting guideline in order to improve reporting practices of BSEMs. The guideline was developed by experts as part of the IEA's Annex 70 and builds upon reporting guidelines from other fields. It consists of five topics (Overview, Model Components, Input and Output, Quality Assurance and Additional Information), which are further subdivided into subtopics. We explain which model aspects should be described in each subtopic, and provide illustrative examples on how to apply the guideline. In closing, the reporting guideline is consistent with the model classification framework and online model registry also developed in the Annex.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Systems biology perspectives on the carcinogenic potential of radiation

This review focuses on recent experimental and modeling studies that attempt to define the physiological context in which high linear energy transfer (LET) radiation increases epithelial cancer risk and the efficiency with which it does so. Radiation carcinogenesis is a two-compartment problem: ionizing radiation can alter genomic sequence as a result of damage due to targeted effects (TE) from the interaction of energy and DNA; it can also alter phenotype and multicellular interactions that contribute to cancer by poorly understood nontargeted effects (NTE). Rather than being secondary to DNA damage and mutations that can initiate cancer, radiation NTE create the critical context in which to promote cancer. Systems biology modeling using comprehensive experimental data that integrates different levels of biological organization and time-scales is a means of identifying the key processes underlying the carcinogenic potential of high-LET radiation. We hypothesize that inflammation is a key process, and thus cancer susceptibility will depend on specific genetic predisposition to the type and duration of this response. Systems genetics using novel mouse models can be used to identify such determinants of susceptibility to cancer in radiation sensitive tissues following high-LET radiation. Improved understanding of radiation carcinogenesis achieved by defining the relative contribution of NTE carcinogenic effects and identifying the genetic determinants of the high-LET cancer susceptibility will help reduce uncertainties in radiation risk assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

GMFOLD: Subgraph matching for high-throughput DNA-aptamer secondary structure classification and machine learning interpretability

Aptamers are oligonucleotide receptors that bind to their targets with high affinity. Here, we consider aptamers comprised of single-stranded DNA that undergo target-binding-induced conformational changes, giving rise to unique secondary and tertiary structures. Given a specific aptamer primary sequence, there are well-established computational tools (notably mfold) to predict the secondary structure via free energy minimization algorithms. While mfold generates secondary structures for individual sequences, there is a need for a high-throughput process whereby thousands of DNA structures can be predicted in real-time for use in an interactive setting, when combined with aptamer selections that generate candidate pools that are too large to be experimentally interrogated. We developed a new Python code for high-throughput aptamer secondary structure determination (GMfold). GMfold uses subgraph matching methods to group aptamer candidates by secondary structure similarities. We also improve an open-source code, SeqFold, to incorporate subgraph matching concepts. We represent each secondary structure as a lowest-energy bipartite subgraph matching of the DNA graph to itself. These new tools enable thousands of DNA sequences to be compared based on their secondary structures, using machine-learning algorithms. This process is advantageous when analyzing sequences that arise from aptamer selections via systematic evolution of ligands by exponential enrichment (SELEX). This work is a building block for future machine-learning-informed DNA-aptamer selection processes to identify aptamers with improved target affinity and selectivity and advance aptamer biosensors and therapeutics.

Aptamer↗
DOE OSTI2016

Isospin effects in N ≈ Z nuclei in extended density functional theory

This paper overviews various phenomena related to the concept of isospin symmetry. The focus is on N ≈ Z nuclei, which are excellent laboratories of isospin physics. The theoretical framework applied is nuclear density functional theory and its isospin- and angular-momentum projected extensions, as well as symmetry-projected multi-reference models. The topics covered include: isospin impurities, superallowed beta decays, beta-transitions in mirror nuclei, isospin breaking hadronic interactions, mirror and triplet binding energy differences, and isoscalar pairing.

Physics↗
DOE OSTI2014

Sci—Fri PM: Topics — 01: A monte carlo model of a miniature low-energy x-ray tube using EGSnrc

The INTRABEAM system (Carl Zeiss, Oberkochen, Germany) is a miniature x-ray generator for use in intraoperative radiotherapy and brachytherapy. The device accelerates electrons to up to 50 keV, which are then steered down an evacuated needle probe to strike a thin gold target. For accurate dosimetry of the INTRABEAM system, it is important that the photon spectrum be well understood. Measurements based on air-kerma are heavily impacted by photon spectra, particularly for low photon energies due to the large photoelectric contribution in air mass energy absorption coefficient. While low energy photons have little clinical significance at treatment depths, they may have a large effect on air-kerma measurements. In this work, we have developed an EGSnrc-based monte carlo (MC) model of the Zeiss INTRABEAM system to study the source photon spectra and half-value layers (HVLs) of the bare probe and with various spherical applicators. HVLs were calculated using the analytical attenuation of air-kerma spectra. The calculated bare probe spectrum was compared with simulated and measured results taken from literature. Differences in the L-line energies of gold were found between the spectra predicted by EGSnrc and Geant4. This is due to M and N shell averaging during atomic transitions in EGSnrc. The calculated HVLs of the bare probe and spherical applicators are consistent with literature reported measured values.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2013

Experimental High Energy Physics Brandeis University Final Report

During the past three years, the Brandeis experimental particle physics group was comprised of four faculty (Bensinger, Blocker, Sciolla, and Wellenstein), one research scientist, one post doc, and ten graduate students. The group focused on the ATLAS experiment at LHC. In 2011, the LHC delivered 5/fb -1 of pp colliding beam data at a center-of-mass energy of 7 TeV. In 2012, the center-of-mass energy was increased to 8 TeV, and 20/fb -1 were delivered. The Brandeis group focused on two aspects of the ATLAS experiment $-$ the muon detection system and physics analysis. Since data taking began at the LHC in 2009, our group actively worked on ATLAS physics analysis, with an emphasis on exploiting the new energy regime of the LHC to search for indications of physics beyond the Standard Model. The topics investigated were Z' → ll, Higgs → ZZ* -. 4l, lepton flavor violation, muon compositeness, left-right symmetric theories, and a search for Higgs → ee. The Brandeis group has for many years been a leader in the endcap muon system, making important contributions to every aspect of its design and production. During the past three years, the group continued to work on commissioning the muon detector and alignment system, development of alignment software, and installation of remaining chambers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

An Investigation of Aerosol-Cloud-Precipitation Interactions in the South-East Pacific Using DOE G-1 Data and WRF/Chem Large Eddy Simulations

Under this grant, we investigated marine boundary layer clouds, their properties, behavior, and response to human activity and climate change, with a focus on scales and processes that cannot be studied with climate models. Investigated topics and results are summarized in this section. Detailed information are given in subsequent sections, followed by a list of resulting publications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Helios: Understanding Solar Evolution Through Text Analytics

This proof-of-concept project focused on developing, testing, and validating a range of bibliometric, text analytic, and machine-learning based methods to explore the evolution of three photovoltaic (PV) technologies: Cadmium Telluride (CdTe), Dye-Sensitized solar cells (DSSC), and Multi-junction solar cells. The analytical approach to the work was inspired by previous work by the same team to measure and predict the scientific prominence of terms and entities within specific research domains. The goal was to create tools that could assist domain-knowledgeable analysts in investigating the history and path of technological developments in general, with a focus on analyzing step-function changes in performance, or “breakthroughs,” in particular. The text-analytics platform developed during this project was dubbed Helios. The project relied on computational methods for analyzing large corpora of technical documents. For this project we ingested technical documents from the following sources into Helios: Thomson Scientific Web of Science (papers), the U.S. Patent & Trademark Office (patents), the U.S. Department of Energy (technical documents), the U.S. National Science Foundation (project funding summaries), and a hand curated set of full-text documents from Thomson Scientific and other sources.

14 SOLAR ENERGY↗
DOE OSTI2022

Qualification of SAS4A/SASSYS-1 for Sodium-Cooled Fast Reactor Authorization and Licensing (Final CRADA Report)

This project focuses on assessment and qualification of the SAS4A/SASSYS-1 advanced reactor safety analysis code and its capacity to fulfill requirements associated with license/authorization of a sodium-cooled fast reactor (SFR). Specific objectives of this proposal include: development of a formal documentation requirements matrix that complies with a vendor’s NQA-1 program to identify needs for qualification documentation; completion of software methods qualification gap analysis to identify and close gaps in the verification and validation basis of the software; generation of an Acceptance Test Report to confirm compliance with the software requirements specification; and closure of gaps in Software Design Description (SDD) to assist with the eventual generation of a Modeling Licensing Topic Report (LTR).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Water narratives in local newspapers within the United States

Sustainable use of water resources continues to be a challenge across the globe. This is in part due to the complex set of physical and social behaviors that interact to influence water management from local to global scales. Analyses of water resources have been conducted using a variety of techniques, including qualitative evaluations of media narratives. This study aims to augment these methods by leveraging computational and quantitative techniques from the social sciences focused on text analyses. Specifically, we use natural language processing methods to investigate a large corpus (approx. 1.8M) of newspaper articles spanning approximately 35 years (1982–2017) for insights into human-nature interactions with water. Focusing on local and regional United States publications, our analysis demonstrates important dynamics in water-related dialogue about drinking water and pollution to other critical infrastructures, such as energy, across different parts of the country. Our assessment, which looks at water as a system, also highlights key actors and sentiments surrounding water. Extending these analytical methods could help us further improve our understanding of the complex roles of water in current society that should be considered in emerging activities to mitigate and respond to resource conflicts and climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Review: Inelastic Constitutive Modeling: Polycrystalline Materials

This article provides a literature review that details the development of inelastic constitutive modeling as it relates to polycrystalline materials. This review distinguishes between inelastic constitutive models that account for nonlinear behavior at the microstructural level, time-independent classic plasticity models, and time-dependent unified models. Particular emphasis is placed on understanding the underlying theoretical framework for unified viscoplasticity models where creep and classical plasticity behavior are considered the result of applied boundary conditions instead of separable rates representing distinct physical mechanisms. This article establishes a clear understanding of the advantages of the unified approach to improve material modeling. This review also discusses recent topics in constitutive modeling that offer new techniques that bridge the gap between the microstructure and the continuum.

36 MATERIALS SCIENCE↗
DOE OSTI2018

An Assessment of the National Risk Assessment Program’s CO 2 Sequestration Leakage Modeling Tools, Subtask 6.1 – NRAP Assessment Topical Report

The CarbonSAFE East Sub-Basin project team explored and assessed the carbon sequestration site characterization National Risk Assessment Partnership (NRAP) Toolset, developed by the National Energy Technology Laboratory, Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and Pacific Northwest National Laboratory (PNNL) for the NRAP. We ran the Integrated Assessment Model (IAM) tool using inputs derived from verified regional geologic data. We investigated CO 2 and brine leakage rates, and total CO 2 and brine leaked, from a hypothetical cemented injection well borehole to the nearest underground source of drinking water (USDW), identified as the St. Peter Sandstone, in a hypothetical monitoring well. The distance of the monitoring well from the injection well was varied to identify leakage variations with increasing distance from the injection well and to establish an area of review (AOR) for the project, and the effect of high and low wellbore cement permeability on model results was compared.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

59 BASIC BIOLOGICAL SCIENCES↗