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At least 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Orbital Selectivity Enhanced by Nematic Order in FeSe

Motivated by the recent low-temperature experiments on bulk FeSe, we study the electron correlation effects in a multiorbital model for this compound in the nematic phase using the U(1) slave-spin theory. Here, we find that a finite nematic order helps to stabilize an orbital selective Mott phase. Moreover, we propose that when the d- and s-wave bond nematic orders are combined with the ferro-orbital order, there exists a surprisingly large orbital selectivity between the xz and yz orbitals even though the associated band splitting is relatively small. Lastly, our results explain the seemingly unusual observation of strong orbital selectivity in the nematic phase of FeSe, uncover new clues on the nature of the nematic order, and set the stage to elucidate the interplay between superconductivity and nematicity in iron-based superconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Minimizing Selection Bias in Inclusive Jets in Heavy-Ion Collisions with Energy Correlators

The first-ever measurement of energy correlators within inclusive jets produced in heavy-ion collisions, revealed by the CMS Collaboration, shows a clear enhancement at large angles relative to the proton-proton (p-p) baseline. However, interpreting this enhancement is complicated due to selection bias from energy loss, which also distorts the energy correlator heavy-ion to p-p ratio in the hadronization region, hindering our understanding of parton/hadron dynamics in a colored medium. In this Letter, we introduce a new ratio of energy correlator observables that removes the leading effects of selection bias from the two-point energy correlator spectrum (E2C). PYTHIA and HERWIG simulations show that the impact of selection bias in the E2C is reduced by an order of magnitude, while sensitivity to any other medium modifications is retained. This quantity can be obtained directly from the experimental measurements presented by CMS, as illustrated in an accompanying note [C. Andres and J. Holguin, arXiv:2409.07526].

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

WinnowML: Stable feature selection for maximizing prediction accuracy of time-based system modeling

Online deep learning (ODL) has become an important methodology for modeling time-based performance of computer systems. An open problem is the intelligent selection of features from raw workload traces of computer systems. The best methods are overly sensitive to noisy data, causing frequent feature changes and re-training. Using all available features inflates training time and introduces model artifacts if some features should have been dropped. We present WinnowML, a method for automatically determining the most relevant feature subset for a predictive time-series model. WinnowML combines existing feature ranking algorithms and a history of each feature's ranking to iteratively rank a feature set to lower prediction error and maximize long term relevance. From this ranked feature set, the most relevant and stable subset is selected to train a model. Experimentally, we show how WinnowML can lower a model's mean absolute relative error up to 42% on average compared to the closest performing approach. Additionally, we lower the fluctuation in feature ranking and selection up to 65%. We also demonstrate how to combine WinnowML and a model search tool to provide improvements in performance of up to 14.5% when compared to using all the feature available.

Bel, Oceane MS↗
DOE OSTI2024

On Rank Selection for Nonnegative Matrix Factorization

Rank selection, i.e. the choice of factorization rank, is the first step in constructing Nonnegative Matrix Factorization (NMF) models. It is a long-standing problem which is not unique to NMF, but arises in most models which attempt to decompose data into its underlying components. Since these models are often used in the unsupervised setting, the rank selection problem is further complicated by the lack of ground truth labels. In this paper, we review and empirically evaluate the most commonly used schemes for NMF rank selection.

Eswar, Srinivas [Argonne National Laboratory]↗
DOE OSTI2015

An ILP based Algorithm for Optimal Customer Selection for Demand Response in SmartGrids

Demand Response (DR) events are initiated by utilities during peak demand periods to curtail consumption. They ensure system reliability and minimize the utility’s expenditure. Selection of the right customers and strategies is critical for a DR event. An effective DR scheduling algorithm minimizes the curtailment error which is the absolute difference between the achieved curtailment value and the target. State-of-the-art heuristics exist for customer selection, however their curtailment errors are unbounded and can be as high as 70%. In this work, we develop an Integer Linear Programming (ILP) formulation for optimally selecting customers and curtailment strategies that minimize the curtailment error during DR events in SmartGrids. We perform experiments on real world data obtained from the University of Southern California’s SmartGrid and show that our algorithm achieves near exact curtailment values with errors in the range of 10 -7 to 10 -5 , which are within the range of numerical errors. We compare our results against the state-of-the-art heuristic being deployed in practice in the USC SmartGrid. We show that for the same set of available customer strategy pairs our algorithm performs 103 to 107 times better in terms of the curtailment errors incurred.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Autonomous Selective Parts-Based Tracking

Object tracking from videos is still a challenging task due to various changes throughout a video sequence including occlusions, motion blur, scale and other deformation changes. In this paper, we propose a selective parts-based approach, using correlation filters, that makes choices based on a consensus of the parts and global tracking. Moreover, we further enhance our parts-based approach by introducing a segmentation-assisted parts initialization. In addition, we present a genetic algorithm-based method to autonomously select various parameters of the tracking algorithm, as opposed to the common practice of manually tuning those parameters. In contrast to existing part-based methods, the proposed method does not dilute accurate tracking by averaging results over multiple parts at every frame. Instead, we take a selective approach based on the relative weight of the responses across parts. Moreover, we only make location corrections when a part diverges, and rely on these location corrections to maintain an accurate appearance model. In the case of occlusions, which are among the main reasons for using a parts-based approach, our proposed approach consistently achieves the best performance. It is due to the ability to handle occlusion and not dilute decisions with incorrect parts, that our proposed approach enables state-of-the-art performance. The approach we propose was evaluated on videos from three different challenging benchmark datasets. Our approach has resulted in better overall precision and success rates for three different base tracking approaches.

42 ENGINEERING↗
DOE OSTI2025

Tomographic Sparse View Selection Using the View Covariance Loss

Standard computed tomography (CT) reconstruction algorithms such as filtered back projection (FBP) and Feldkamp-Davis-Kress (FDK) require many views for producing high-quality reconstructions, which can slow image acquisition and increase cost in non-destructive evaluation (NDE) applications. Over the past 20 years, a variety of methods have been developed for computing high-quality CT reconstructions from sparse views. However, the problem of how to select the best views for CT reconstruction remains open. In this paper, we present a novel view covariance loss (VCL) function that measures the joint information of a set of views by approximating the normalized mean squared error (NMSE) of the reconstruction. We present fast algorithms for computing the VCL along with an algorithm for selecting a subset of views that approximately minimizes its value. Our experiments on simulated and measured data indicate that for a fixed number of views our proposed view covariance loss selection (VCLS) algorithm results in reconstructions with lower NRMSE, fewer artifacts, and greater accuracy than current alternative approaches.

Lin, Jingsong [Purdue University]↗
DOE OSTI2025

Unravelling the Glycan Code: Molecular Dynamics and Quantum Chemistry Reveal How O-Glycan Functional Groups Govern OgpA Selectivity in Mucin Degradation by Akkermansia muciniphila

Mucins, heavily O-glycosylated glycoproteins, are a key component of mucus, and certain gut microbiota, including Akkermansia muciniphila , can utilise mucin glycans as a carbon source. Akkermansia muciniphila produces the O-glycopeptidase enzyme OgpA, which cleaves peptide bonds at the N-terminus of serine (Ser) or threonine (Thr) residues carrying O-glycan substitutions, with selectivity influenced by the O-glycan functional groups. Using molecular dynamics (MD) simulations and quantum chemistry calculations, we explored how different O-glycan groups affect OgpA's selectivity. Our results show that peptides bind to the enzyme via hydrogen bonds, π–π interactions, van der Waals forces and electrostatic interactions, with key residues, including Tyr90, Val138, Gly176, Tyr210 and Glu91, playing important roles. The primary determinant of selectivity is the interaction between the peptide's functional group and the enzyme's binding cavity, while peptide–enzyme interface interactions are secondary. Quantum chemistry calculations reveal that OgpA prefers peptides with a lower electrophilic character. This study provides new insights into mucin degradation by gut microbiota enzymes, advancing our understanding of this critical biological process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Signatures of Selection for Resistance/Tolerance to Perkinsus olseni in Grooved Carpet Shell Clam ( Ruditapes decussatus ) Using a Population Genomics Approach

ABSTRACT The grooved carpet shell clam ( Ruditapes decussatus ) is a bivalve of high commercial value distributed throughout the European coast. Its production has suffered a decline caused by different factors, especially by the parasite Perkinsus olsenii . Improving production of R . decussatus requires genomic resources to ascertain the genetic factors underlying resistance/tolerance to P. olseni i . In this study, the first reference genome of R . decussatus was assembled through long‐ and short‐read sequencing (1677 contigs; 1.386 Mb) and further scaffolded at chromosome level with Hi‐C (19 superscaffolds; 95.4% of assembly). Repetitive elements were identified (32%) and masked for annotation of 38,276 coding‐ and 13,056 non‐coding genes. This genome was used as a reference to develop a 2bRAD‐Seq 13,438 SNP panel for a genomic screening on six shellfish beds distributed across the Atlantic Ocean and Mediterranean Sea. Beds were selected by perkinsosis prevalence and the infection level was individually evaluated in all the samples. Genetic diversity was significantly higher in the Mediterranean than in the Atlantic region. The main genetic breakage was detected between those regions (F ST = 0.224), being the Mediterranean more heterogeneous than the Atlantic. Several loci under divergent selection (394 outliers; 261 genomic windows) were detected across shellfish beds. Samples were also inspected to detect signals of selection for resistance/tolerance to P. olseni i by using infection‐level and population‐genomics approaches, and 90 common divergent outliers for resistance/tolerance to perkinsosis were identified and used for gene mining. Candidate genes and markers identified provide invaluable information for controlling perkinsosis and for improving production of the grooved carpet shell clam.

Sambade, Inés M. [Department of Zoology, Genetics ↗
DOE OSTI2022

Resource selection and species interactions between native and non‐native fishes in a simulated stream system

Abstract Effective fishery management necessitates understanding of resource partitioning by fishes that inhabit complex systems composed of biotic and abiotic features. Evaluations of non‐native species introductions have continually demonstrated adverse effects associated with abundance and distribution of native fishes. Therefore, understanding resource selection and interactions between native and non‐native species is important for recovery efforts. Habitat use by two native fish species (largescale sucker Catostomus macrocheilus [Girard] and mountain whitefish Prosopium williamsoni [Girard]) and one non‐native fish species (pumpkinseed Lepomis gibbosus [Linnaeus]) of the Kootenai River, Idaho, were evaluated in a laboratory stream system. Trials were conducted in allopatry and in sympatry with and without the presence of wood to describe habitat selection in the context of on‐going habitat rehabilitation efforts. Interactions were evident between native largescale sucker and non‐native pumpkinseed concerning use of a woody structure and current velocity. Mountain whitefish used low‐velocity habitats and selected locations that were further from wood when in sympatry with pumpkinseed. Our research suggests that habitat use of native, large‐river fishes may be influenced by the presence of a non‐native species, and that considering such interactions is critical when designing and implementing habitat rehabilitation efforts in river ecosystems.

Branigan, Philip R.↗
DOE OSTI2019

Selection and characterization of ssDNA aptamers specifically recognizing pathogenic Vibrio alginolyticus

Abstract Vibrio alginolyticus ( V. alginolyticus ) is a major opportunistic pathogen to both marine animals and humans, which has also caused heavy economic losses to mariculture. The aim of this study was to develop highly specific aptamers for V. alginolyticus . Single‐stranded DNA (ssDNA) aptamers with high binding affinity to viable V. alginolyticus were generated by Systematic Evolution of Ligands by Exponential Enrichment (SELEX) and identified by flow cytometric analysis in this study. The selected aptamers showed high specificity for V. alginolyticus and low apparent binding for other bacteria. The aptamers formed distinct stem‐loop structures, which could form the basis of aptamers’ specific binding to the target V. alginolyticus . Aptamer VA2 and VA8 showed particularly high binding affinity constant (K d ) of 14.31 ± 4.26 and 90.00 ± 13.51 nM, respectively. The aptamers produced no cytotoxic effects in vitro and in vivo. ssDNA aptamers were successfully selected against the viable bacteria pathogen V. alginolyticus by SELEX. The aptamers selected in this study could be not only applied as specific chemical molecular probes for studying V. alginolyticus pathogenesis to Trachinotus ovatus , but also developing rapid convenient diagnosis assay for V. alginolyticus infection, even when applied to the complex sample matrix, such as food and environment samples.

Yu, Qing↗
DOE OSTI2023

Property-Graded Stainless Steel 316L by Selective Laser Melting: Characterization & Design

Abstract The purpose of this research work is to characterize and inform the design of (mechanical) property-graded bulk structures made from a single metallic alloy via a laser powder bed fusion (LPBF) process, with an end goal of creating repeatable/reproducible functionally-graded additively manufactured (FGAM) parts. This paper specifically investigates the manufacture of stainless steel (SS) 316L structures via a pulsed selective laser melting (SLM) process, and the underlying causes of property variations (within a functionally-acceptable range) through various material characterization techniques. For this, a design of experiments spanning the volumetric energy density (VED) based process parameter design space was utilized to investigate the range of functionally-acceptable physical/mechanical properties achievable in SS 316L. Five sample conditions (made via different process parameter combinations) were down-selected for in-depth microstructure analysis and mechanical/physical property characterization; these were suitably selected to impart a wide and controllable property range (209–318 HV hardness, 90–99.9% relative density, and 154–211 GPa modulus). It was observed that property variations resulted from combinations of porosity types/amounts, martensitic phase fractions, and grain sizes. Based on these findings, property-graded standard test specimens were designed and manufactured for further investigation—tensile specimens having a monotonic hardness change along its gauge length, four-point bending specimens with varying elastic moduli as a function of the distance from the neutral axis, and Moore’s rotating beam fatigue specimens with moduli variations based on the distance from the center. Altogether, this work lays the foundation for understanding and designing the local and global mechanical performance of FGAM bulk structures.

Engineering↗
DOE OSTI2025

A Comprehensive Review of Working Fluids for High-Temperature Heat Pumps: History, Selection, and Evaluation

High-temperature heat pumps (HTHPs) are essential for enhancing energy efficiency across various industrial applications, especially in terms of integrating with renewable energy sources and recovering waste heat. This article thoroughly investigates suitable working fluids for HTHPs, highlighting the evolution from traditional refrigerants to contemporary alternatives with low global warming potential (GWP). It proposes comprehensive selection criteria for these working fluids, pre-selects low-GWP working fluids, and outlines a screening methodology. The pre-selected low-GWP working fluids are evaluated for applications in three typical industrial scenarios involving HTHPs. Furthermore, this study demonstrates that regulatory compliance and environmental impacts significantly influence the development of next-generation refrigerants. The choice of working fluids is closely linked to the types of vapor compression cycles, tailored to the specific industrial applications for HTHPs. This study emphasizes areas for future research, including the development of innovative working fluids; integrated strategies that account for performance, safety, and regulatory standards; alignment of HTHP components; exploration of natural working fluids; and broadening the applications of existing working fluids.

Compression↗
DOE OSTI2016

Inherent substrate-dependent growth initiation and selective-area atomic layer deposition of TiO{sub 2} using “water-free” metal-halide/metal alkoxide reactants

Titanium dioxide atomic layer deposition (ALD) is shown to proceed selectively on oxidized surfaces with minimal deposition on hydrogen-terminated silicon using titanium tetrachloride (TiCl{sub 4}) and titanium tetra-isopropoxide [Ti(OCH(CH{sub 3}){sub 2}){sub 4}, TTIP] precursors. Ex situ x-ray photoelectron spectroscopy shows a more rapid ALD nucleation rate on both Si–OH and Si–H surfaces when water is the oxygen source. Eliminating water delays the oxidation of the hydrogen-terminated silicon, thereby impeding TiO{sub 2} film growth. For deposition at 170 °C, the authors achieve ∼2 nm of TiO{sub 2} on SiO{sub 2} before substantial growth takes place on Si–H. On both Si–H and Si–OH, the surface reactions proceed during the first few TiCl{sub 4}/TTIP ALD exposure steps where the resulting products act to impede subsequent growth, especially on Si–H surfaces. Insight from this work helps expand understanding of “inherent” substrate selective ALD, where native differences in substrate surface reaction chemistry are used to promote desired selective-area growth.

36 MATERIALS SCIENCE↗
DOE OSTI2017

ScAlN etch mask for highly selective silicon etching

Here, this work reports the utilization of a recently developed film, ScAlN, as a silicon etch mask offering significant improvements in high etch selectivity to silicon. Utilization of ScAlN as a fluorine chemistry based deep reactive ion etch mask demonstrated etch selectivity at 23 550:1, four times better than AlN, 11 times better than Al 2 O 3 , and 148 times better than silicon dioxide with significantly less resputtering at high bias voltage than either Al 2 O 3 or AlN. Ellipsometry film thickness measurements show less than 0.3 nm/min mask erosion rates for ScAlN. Micromasking of resputtered Al for Al 2 O 3 , AlN, and ScAlN etch masks is also reported here, utilizing cross-sectional scanning electron microscope and confocal microscope roughness measurements. With lower etch bias, the reduced etch rate can be optimized to achieve a trench bottom surface roughness that is comparable to SiO 2 etch masks. Etch mask selectivity enabled by ScAlN is likely to make significant improvements in microelectromechanical systems, wafer level packaging, and plasma dicing of silicon.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Impact of carrier wafer on etch rate, selectivity, morphology, and passivation during GaN plasma etching

The choice of carrier wafer was found to significantly influence etch rates, selectivity, and morphology in GaN micropillar etching in a Cl 2 -Ar high-density inductively coupled plasma. 7 × 7 mm 2 GaN on sapphire chips with a plasma-enhanced chemical vapor deposition SiO 2 hard mask was etched on top of 4-in. fused silica, silicon carbide, silicon, sapphire, aluminum nitride, and high purity aluminum carriers. Silicon and silicon carbide carriers reduced GaN:SiO 2 selectivity because incidental SiCl x and CCl x etch products from the carriers attack the SiO 2 mask. Aluminum nitride and high-purity aluminum carriers yielded the highest GaN:SiO 2 selectivities due to the deposition of Al-based etched by-products, while the highest GaN etch rate was achieved using the sapphire carrier since it was the most inert carrier and did not sink any Cl 2 . Overall, results indicate that SiO 2 and Al may be used as passivation materials during GaN etching, as vertical profiles were achieved when SiO 2 or Al is redeposited from the fused silica and aluminum carriers, respectively. Floor pitting, trenching, sidewall roughness, and faceting were all influenced by carrier wafer type and will be discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗
DOE OSTI2014

SU-E-I-60: The Correct Selection of Pitch and Rotation Time for Optimal CT Scanning : The Big Misconception

Purpose: To provide correct guidance in the proper selection of pitch and rotation time for optimal CT imaging with multi-slice scanners. Methods: There exists a widespread misconception concerning the role of pitch in patient dose with modern multi-slice scanners, particularly with the use of mA modulation techniques. We investigated the relationship of pitch and rotation time to image quality, dose, and scan duration, with CT scanners from different manufacturers in a way that clarifies this misconception. This source of this misconception may concern the role of pitch in single slice CT scanners. Results: We found that the image noise and dose are generally independent of the selected effective mAs (mA*time/ pitch) with manual mA technique settings and are generally independent of the selected pitch and /or rotation time with automatic mA modulation techniques. However we did find that on certain scanners the use of a pitch just above 0.5 provided images of equal image noise at a lower dose compared to the use of a pitch just below 1.0. Conclusion: The misconception that the use of a lower pitch over-irradiates patients by wasting dose is clearly false. The use of a lower pitch provides images of equal or better image quality at the same patient dose, whether using manual mA or automatic mA modulation techniques. By decreasing the pitch and the rotation times by equal amounts, both helical and patient motion artifacts can be reduced without affecting the exam time. The use of lower helical pitch also allows better scanning of larger patients by allowing a greater scan effective mAs, if the exam time can be extended. The one caution with the use of low pitch is not related to patient dose, but to the length of the scan time if the rotation time is not set short enough. Partial Research funding from GE HealthCare.

60 APPLIED LIFE SCIENCES↗