Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Consensus”

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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Coevolution of the Ess1-CTD axis in polar fungi suggests a role for phase separation in cold tolerance

Most of the world’s biodiversity lives in cold (-2° to 4°C) and hypersaline environments. To understand how cells adapt to such conditions, we isolated two key components of the transcription machinery from fungal species that live in extreme polar environments: the Ess1 prolyl isomerase and its target, the carboxy-terminal domain (CTD) of RNA polymerase II. Polar Ess1 enzymes are conserved and functional in the model yeast, Saccharomyces cerevisiae. By contrast, polar CTDs diverge from the consensus (YSPTSPS) 26 and are not fully functional in S. cerevisiae. These CTDs retain the critical Ess1 Ser-Pro target motifs, but substitutions at Y1, T4, and S7 profoundly affected their ability to undergo phase separation in vitro and localize in vivo. We propose that environmentally tuned phase separation by the CTD and other intrinsically disordered regions plays an adaptive role in cold tolerance by concentrating enzymes and substrates to overcome energetic barriers to metabolic activity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A composite electrodynamic mechanism to reconcile spatiotemporally resolved exciton transport in quantum dot superlattices

Quantum dot (QD) solids are promising optoelectronic materials; further advancing their device functionality requires understanding their energy transport mechanisms. The commonly invoked near-field Förster resonance energy transfer (FRET) theory often underestimates the exciton hopping rate in QD solids, yet no consensus exists on the underlying cause. In response, we use time-resolved ultrafast stimulated emission depletion (STED) microscopy, an ultrafast transformation of STED to spatiotemporally resolve exciton diffusion in tellurium-doped cadmium selenide–core/cadmium sulfide–shell QD superlattices. We measure the concomitant time-resolved exciton energy decay due to excitons sampling a heterogeneous energetic landscape within the superlattice. The heterogeneity is quantified by single-particle emission spectroscopy. This powerful multimodal set of observables provides sufficient constraints on a kinetic Monte Carlo simulation of exciton transport to elucidate a composite transport mechanism that includes both near-field FRET and previously neglected far-field emission/reabsorption contributions. Uncovering this mechanism offers a much-needed unified framework in which to characterize transport in QD solids and additional principles for device design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Physical science research needed to evaluate the viability and risks of marine cloud brightening

Marine cloud brightening (MCB) is the deliberate injection of aerosol particles into shallow marine clouds to increase their reflection of solar radiation and reduce the amount of energy absorbed by the climate system. From the physical science perspective, the consensus of a broad international group of scientists is that the viability of MCB will ultimately depend on whether observations and models can robustly assess the scale-up of local-to-global brightening in today’s climate and identify strategies that will ensure an equitable geographical distribution of the benefits and risks associated with projected regional changes in temperature and precipitation. To address the physical science knowledge gaps required to assess the societal implications of MCB, we propose a substantial and targeted program of research—field and laboratory experiments, monitoring, and numerical modeling across a range of scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Angiotensin and biased analogs induce structurally distinct active conformations within a GPCR

Biased agonists of G protein–coupled receptors (GPCRs) preferentially activate a subset of downstream signaling pathways. In this work, we present crystal structures of angiotensin II type 1 receptor (AT1R) (2.7 to 2.9 angstroms) bound to three ligands with divergent bias profiles: the balanced endogenous agonist angiotensin II (AngII) and two strongly β-arrestin–biased analogs. Compared with other ligands, AngII promotes more-substantial rearrangements not only at the bottom of the ligand-binding pocket but also in a key polar network in the receptor core, which forms a sodium-binding site in most GPCRs. Divergences from the family consensus in this region, which appears to act as a biased signaling switch, may predispose the AT1R and certain other GPCRs (such as chemokine receptors) to adopt conformations that are capable of activating β-arrestin but not heterotrimeric G q protein signaling.

Wingler, Laura M. (ORCID:0000000236885088)↗
DOE OSTI2022

Regulation of L - and D -Aspartate Transport and Metabolism in Acinetobacter baylyi ADP1

Here, the regulated uptake and consumption of d-amino acids by bacteria remain largely unexplored, despite the physiological importance of these compounds. Unlike other characterized bacteria, such as Escherichia coli, which utilizes only l-Asp, Acinetobacter baylyi ADP1 can consume both d-Asp and l-Asp as the sole carbon or nitrogen source. As described here, two LysR-type transcriptional regulators (LTTRs), DarR and AalR, control d- and l-Asp metabolism in strain ADP1. Heterologous expression of A. baylyi proteins enabled E. coli to use d-Asp as the carbon source when either of two transporters (AspT or AspY) and a racemase (RacD) were coexpressed. A third transporter, designated AspS, was also discovered to transport Asp in ADP1. DarR and/or AalR controlled the transcription of aspT, aspY, racD, and aspA (which encodes aspartate ammonia lyase). Conserved residues in the N-terminal DNA-binding domains of both regulators likely enable them to recognize the same DNA consensus sequence (ATGC-N7-GCAT) in several operator-promoter regions. In strains lacking AalR, suppressor mutations revealed a role for the ClpAP protease in Asp metabolism. In the absence of the ClpA component of this protease, DarR can compensate for the loss of AalR. ADP1 consumed l- and d-Asn and l-Glu, but not d-Glu, as the sole carbon or nitrogen source using interrelated pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Serodominant SARS-CoV-2 Nucleocapsid Peptides Map to Unstructured Protein Regions

The SARS-CoV-2 nucleocapsid (N) protein is highly immunogenic, and anti-N antibodies are commonly used as markers for prior infection. While several studies have examined or predicted the antigenic regions of N, these have lacked consensus and structural context. Using COVID-19 patient sera to probe an overlapping peptide array, we identified six public and four private epitope regions across N, some of which are unique to this study. We further report the first deposited X-ray structure of the stable dimerization domain at 2.05 Å as similar to all other reported structures. Structural mapping revealed that most epitopes are derived from surface-exposed loops on the stable domains or from the unstructured linker regions. An antibody response to an epitope in the stable RNA binding domain was found more frequently in sera from patients requiring intensive care. Since emerging amino acid variations in N map to immunogenic peptides, N protein variation could impact detection of seroconversion for variants of concern.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Resilient Distributed Field Estimation

We study resilient distributed field estimation under measurement attacks. A network of agents or devices measures a large, spatially distributed physical field parameter. An adversary arbitrarily manipulates the measurements of some of the agents. Each agent's goal is to process its measurements and information received from its neighbors to estimate only a few specific components of the field. Here, we present SAFE, the saturating adaptive field estimator, a consensus + innovations distributed field estimator that is resilient to measurement attacks. Under sufficient conditions on the compromised measurement streams, the physical coupling between the field and the agents' measurements, and the connectivity of the cyber communication network, SAFE guarantees that each agent's estimate converges almost surely to the true value of the components of the parameter in which the agent is interested. Finally, we illustrate the performance of SAFE through numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

I/O Access Patterns in HPC Applications: A 360-Degree Survey

The high-performance computing I/O stack has been complex due to multiple software layers, the inter-dependencies among these layers, and the different performance tuning options for each layer. In this complex stack, the definition of an “I/O access pattern” has been reappropriated to describe what an application is doing to write or read data from the perspective of different layers of the stack, often comprising a different set of features. It has become common to have to redefine what is meant when discussing a pattern in every new study, as no assumption can be made. This survey aims to propose a baseline taxonomy, harnessing the I/O community’s knowledge over the past 20 years. This definition can serve as a common ground for high-performance computing I/O researchers and developers to apply known I/O tuning strategies and design new strategies for improving I/O performance. We seek to summarize and bring a consensus to the multiple ways to describe a pattern based on common features already used by the community over the years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Tandem Predictions for HPC Jobs

At the core of the predictive analytics applied to High Performance Computing (HPC), the most prominent tasks are the prediction of job runtimes and the prediction of job queue times, both of which have the potential for informing HPC users during their every-day decision making. Accurate runtime predictions can help users better choose so-called wallclock times at job submission, decreasing the odds of their jobs waiting in queues longer than necessary. The accurate and timely queue time predictions offered for the available partitions can inform the favorable selection of partitions for running jobs. This potential is well understood as we see in the abundance of research studies that propose solutions for these tasks, including the work published in the last several years. These tasks are seemingly receptive to the Machine Learning (ML) solutions, considering that there is no shortage of training data where HPC centers over time run millions and millions of jobs. However, we study the existing research literature, as well as look for examples in the toolchains supported on the exemplar HPC facilities, and, surprisingly, do not find any practical solutions that are ready to be adopted. We interpret this as a manifestation of the shortage of UX/UI efforts that support HPC analytics and also as a sign that the research has not come to the consensus on solving these tasks. In this study, we aim to shed new light on the long-running task of job queue time prediction by exploring the utility of runtime predictions in improving prediction accuracy and, actually, predicting these two metrics together, in tandem. In other words, we show how runtime predictions become valuable input in the queue time modeling. We challenge the existing approaches to feature engineering for the queue time prediction and describe promising results we obtained for a large dataset of HPC jobs from a supercomputer at the National Renewable Energy Laboratory.

HPC↗
DOE OSTI2025

Adversarial Binaries: AI-guided Instrumentation Methods for Malware Detection Evasion

Adversarial binaries are executable files that have been altered without loss of function by an AI agent in order to deceive malware detection systems. Progress in this emergent vein of research has been constrained by the complex and rigid structure of executable files. Although prior work has demonstrated that these binaries deceive a variety of malware classification models which rely on disparate feature sets, a consensus as to the best approach has not been reached, either in terms of the optimization algorithms or the instrumentation methods. Furthermore, although inconsistencies in the data sets, target classifiers, and functionality verification methods make head-to-head comparisons difficult, here we extract lessons learned and make recommendations for future research.

malware obfuscation↗
DOE OSTI2022

Predicted Impacts of Pt and Ionomer Distributions on Low-Pt-Loaded PEMFC Performance

Low-cost, high performance proton exchange membrane fuel cells (PEMFCs) have been difficult to develop due to limited understanding of coupled processes in the cathode catalyst layer (CCL). Low-Pt-loaded PEMFCs suffer losses beyond those predicted solely due to reduced catalyst area. Although consensus links these losses to thin ionomer films in the CCL, a precise mechanistic explanation remains elusive. In this publication, we present a physically based PEMFC model with novel structure-property relationships for thin-film Nafion, validated against PEMFC data with low Pt loading. Results suggest that flooding exacerbates kinetic limitations in low-loaded PEMFCs, shifting the Faradaic current distribution. As current density increases, protons travel further into the CCL, resulting in higher Ohmic overpotentials. We also present a parametric study of CCL design parameters. We find that graded Pt and ionomer loadings reduce Ohmic losses and flooding, but individually do not provide significant improvements. However, a dual-graded CCL (i.e., graded Pt and ionomer) is predicted to significantly improve the maximum power density and limiting current compared to uniformly loaded CCLs. This work highlights the importance of accurate transport parameters for thin-film Nafion and provides a pathway to low-cost PEMFCs via precise control of CCL microstructures.

08 HYDROGEN↗
DOE OSTI2022

MRI-Based Radiotherapy Planning to Reduce Rectal Dose in Excess of Tolerance

Background and Purpose. Chronic rectal toxicity significantly decreases the quality of life for men who receive radiotherapy for prostate cancer. The most significant predictor of rectal toxicity is rectal dose-volume exceeding tolerance. To minimize the volume of rectum in the high dose field, it is essential to accurately define the prostate-rectum interface. This can be challenging to do by computed tomography (CT) imaging alone. The current study was undertaken to formally demonstrate in a clinical trial setting that image-guided intensity-modulated radiation therapy (IG-IMRT) planning using magnetic resonance imaging (MRI) can reduce the volume of rectum exceeding 70 Gy, a validated metric that predicts the risk of late rectal toxicity. Materials and Methods. This prospective single-arm study enrolled 15 men treated with IG-IMRT for localized prostate cancer. All participants received a dedicated 3 Tesla MRI examination of the prostate in addition to a pelvic CT examination for treatment planning. Two volumetric modulated arc therapy (VMAT) plans with a prescription dose of 79.2 Gy were designed using identical constraints based on CT- and MRI-defined consensus volumes. The volume of rectum exposed to 70 Gy or more was compared using the Wilcoxon paired signed rank test. Results. For CT-based treatment plans, the median volume of rectum receiving 70 Gy or more was 9.3 cubic centimeters (cc) (IQR 7.0 to 10.2) compared with 4.9 cc (IQR 4.1 to 7.8) for MRI-based plans. This resulted in a median volume reduction of 2.1 cc (IQR 0.5 to 5.3, P < .001). Conclusions. Using MRI to plan prostate IG-IMRT to a dose of 79.2 Gy reduces the volume of rectum receiving radiation dose in excess of tolerance (70 Gy or more) and should be considered in men who are at high risk for late rectal toxicity and are not good candidates for other rectal sparing techniques such as hydrogel spacer. This trial is registered with NCT02470910.

Schmidt, Daniel R.↗
DOE OSTI2020

Independent Metadata Update VOL Connector for HDF5 (IMM VOL) v0.1

We have built a new framework that enables independent metadata updates within HDF5 files. Our framework includes the following components: an overlay network that's optimized for 1-to-All and All-to-All communication; root-less/server-less Bcast and AllReduce operations for reduced synchronization; a distributed consensus-making platform; an execution order keeping system, and a progress engine for generic threadless concurrence handling.

Li, Tonglin↗
DOE OSTI2022

HPXML Version Translator

HPXML is a consensus data transfer standard for residential buildings. Over several years, multiple versions of the standard have been released. This tool accepts an HPXML file in an older version and translates it to a newer version.

Merket, Noel↗
DOE OSTI2024

Blockchain-based fault tolerant grid operations system

To maintain/improve the distribution system fault-tolerance for a grid with high penetration of distributed energy resources requires improvement, Blockchain can add value to improve fault-tolerant grid operations. That can be achieved using blockchain's core features of distributed consensus-based decision-making process and immutability.

Bereta dos Reis, Fernando↗
DOE OSTI2021

The phylogenetic roots of addiction: compulsive drug seeking, natural and drug-sensitive reward, and the acquisition of learned habits

Our rational faculties permit us humans to maximize the utility of our actions. We perform a fundamental type of cost-benefit analysis in which we frame a problem, assign values to the different paths, and then choose from among a set of available options, the course that promises the most favorable outcome. So why then does addiction appear to be so impervious to the associated costs, so unaffected by undesired consequences, and ultimately so resistant to cognitive oversight? The answer may likely be found in the fact that the drivers for compulsive drug seeking and drug taking are located in affective brain circuits, circuits that are structured and patterned by learning with repeated activation. These deep processes exhibit significant resistance to control by our cognitive faculties. The general consensus is that addiction arises from mechanisms that overvalue the magnitude of reward, discount the associated risks, and thereby bias individuals towards compulsive pursuit of addictive drugs. Behavioral disruption and dependence appear to arise at the intersection of a number of connected but separate phenomena: expectations for the occurrence of specific events, behaviors that seek encounters with them, the ability to notice and learn nonrandom, co-occurring conditions, the prediction and valuation of consequences, the forming of enduring memories, and the drivers of focused behavior through compulsion, habits, and acquired routines. It is important to recognize that each one of these individual faculties are present and well developed across the entire phylogenetic tree of bilateral metazoans. The goal of this special volume is dedicated to exploring the degree to which inherent elements can account for addiction and addiction-associated phenomena. In much of the literature on addiction, the underlying processes are often viewed as distinctly mammalian, arising, in part, from the strong cognitive capacities of this taxon. Some phenomena may even be regarded to exist only in primates, or even solely in humans. This supposition arises from the fact that studies are conducted almost exclusively in mammals and primates, while evolutionary antecedents of the behavior are rarely considered. A more comprehensive perspective that examines drug reward and reinforcement in a wider range of organisms demonstrates that many of the component traits are actually well developed across the greater metazoan lineage, and they may well predate the emergence of a mammalian clade by a wide margin. The collection of papers assembled here supports the notion that the capacity to associate cues and quences has not arisen in mammals de novo. Rather, the neural mechanisms for detecting contingencies and for predicting future outcomes are very deeply rooted across broad phylogenetic divisions. Our understanding of an ability to associate paired events has been enriched by work in invertebrate preparations in both classical and operant conditioning scenarios [Cook and Carew, 1986, 1989a, 1989b]. The ability to learn allows us to connect cues and behavioral actions to their associated consequences. Pavlovian conditioning enriches surrounding cues with predictive value. Outcomes with positive valence generate appetitive responses and approach to the associated cues, while those perceived as aversive bring cue avoidance and withdrawal. Humans are not the only life forms capable of such short- and long-term modulations of behavior

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Evaluating Probabilistic Deep Learning Methods for Uncertainty Quantification of Precipitation Bias Correction

Climate models often exhibit biases in their precipitation predictions, particularly underestimating high-intensity events and overestimating low precipitation. Deep learning approaches offer promising solutions, but their epistemic uncertainty associated with a deep learning–based bias correction method has not previously been quantified for reliable downstream climate impact studies. While methods for capturing the epistemic uncertainty in deep learning frameworks exist, there is currently no consensus on the best method. In this work, we compare three uncertainty quantification (UQ) methods—Deep Ensembles (DEns), Monte Carlo Dropout (MCD), and Flipout—by assessing the reliability of their uncertainty estimates using standard measures such as sharpness and calibration. These UQ methods are applied to an existing deep learning precipitation bias correction model known as UFNet: a coupled U-Net and fully connected neural network. The methods utilized to assess the models’ uncertainties are 1) calibration, which ensures that the expected probabilities of the model align with reality and 2) sharpness, which is a measure of the precision of the model’s probabilistic predictions. Of the three UQ methods evaluated, the DEns and MCD methods demonstrated the best-calibrated performance (expected calibration error of 0.36 and 0.35, respectively), compared to Flipout (0.58). In contrast, Flipout had the sharpest predictions and the highest metric performance in bias correcting precipitation—especially for higher-order moments such as kurtosis with a spatial correlation of 72% compared to 32% and 55% spatial correlation for DEns and MCD, respectively. Of the three UQ methods, MCD was found to be the most suitable method for UQ purposes based on its calibration, sharpness, and computational requirements.

Bayesian methods↗
DOE OSTI2025

Evaluating Entrainment–Mixing Characteristics through Direct Comparisons of Drop Size Distributions Using In Situ Observations from ACE-ENA

Abstract Constraining the impacts of entrainment and associated mixing (i.e., entrainment–mixing) on cloud properties continues to be difficult, partly due to observational uncertainties as well as a lacking consensus of which methodologies for diagnosing entrainment–mixing are most appropriate. This study introduces a novel method to evaluate the presence and degree of inhomogeneous and homogeneous mixing using ∼100 h of in situ observations from a research aircraft over the northeastern Atlantic. Specifically, drop size distributions are compared between regions containing negligible and significant entrainment for select flight legs, making a direct characterization of the degree of homogeneous and inhomogeneous mixing possible. A measure of drop concentration variance is used as a proxy variable to diagnose entrainment–mixing. Results correspond well with entrainment–mixing metrics, showing lower Damköhler numbers where drop size distributions shift toward smaller drop sizes (i.e., inhomogeneous mixing) and greater transition length scales where drop size distributions do not (i.e., homogeneous mixing). Inhomogeneous mixing occurs in most samples from Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA) (regardless of homogeneous mixing frequencies increasing with increasing spatial resolution from ∼100 to ∼10 m) and is associated with decreased drop size relative dispersion and both greater aerosol and drop concentrations compared with homogeneous mixing. Precipitating clouds have a greater frequency of homogeneous mixing compared with nonprecipitating clouds. The proposed methodology is similarly applied to in situ observations of southeast Pacific stratocumulus, shallow convective clouds over central Oklahoma and low-level clouds over the Southern Ocean. All four locations are primarily dominated by inhomogeneous mixing with minimal variability among each region.

Clouds↗