Search NASA⌕ Search

SEARCH · Search NASA

Results for “Factorization”

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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Annual Tropical‐Rainforest Productivity Through Two Decades: Complex Responses to Climatic Factors, [CO 2 ] and Storm Damage

Abstract Yearly changes in tropical carbon‐cycling have been a major biotic determinant of the interannual variation in the rise of atmospheric carbon dioxide ([CO 2 ]). The environmental responses underlying these changes remain poorly understood. A 21‐year field study (1997–2018) in a Costa Rican rainforest has produced the first multi‐decade record of landscape‐scale annual aboveground productivity for a tropical ecosystem. While none of the four production components (wood production; leaf‐, twig‐, and reproductive litterfall) increased over two decades, all significantly varied among years. Multi‐factor environmental models explained a large fraction of the inter‐year changes in two production components. Annual changes in three climatic factors and [CO 2 ] jointly accounted for 84% of the variation in annual wood production. While the strongest response of wood production was to decline in years of slightly warmer nights, it also significantly declined in years with more dry‐season hours hotter than 28°C. The yearly changes in reproductive litterfall were largely (52%) determined by the combined effects of increasing [CO 2 ] and declining annual radiation. Twig litterfall declined over the 21 years. Annual leaf litterfall, the largest component of field‐assessed aboveground production, showed no significant environmental responses. An extreme storm in the study's final year disrupted the environmental controls on production that prevailed through the prior 20 years. While important uncertainties are introduced by some of the field methods for assessing aboveground forest production, the findings of this first long‐term field study of annual landscape‐scale productivity raise deep concerns for the future of tropical forests as global warming proceeds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Geo‐Structurally Based Correction Factor for Apparent Dissolution Rates in Fractured Media

Abstract Field measurements of apparent geochemical weathering reaction rates in subsurface fractured porous media are known to deviate from laboratory measurements by multiple orders of magnitude. To date, there is no geologically based explanation for this discrepancy that can be used to predict reaction rates in field systems. Proposed correction factors are typically based on ad hoc characterizations related to geochemical kinetic models. Through a series of high‐fidelity reactive transport simulations of mineral dissolution within explicit 3D discrete fracture networks, we are able to link the geo‐structural attributes with reactive transport observations. We develop a correction factor to linear transition state theory for the prediction of the apparent dissolution rate based on measurable geological properties. The modified rate law shows excellent agreement with numerical simulations, indicating that geological structure could be a primary reason for the discrepancy between laboratory and field observations of apparent dissolution rates in fractured media.

58 GEOSCIENCES↗
DOE OSTI2025

Environmental Factors Associated With Fall Phytoplankton Blooms in the Northern Bering and Chukchi Seas

This study investigates environmental drivers of fall phytoplankton blooms in the Arctic, focusing on the northern Bering and Chukchi seas. Random Forests models were used to analyze covariates of fall phytoplankton blooms from 2013 to 2018, incorporating shipboard, remote sensing, and modeled environmental properties. Four regional models and one comprehensive all-station model considered fall as well as midsummer conditions. Midsummer properties included suspended particulate matter, chlorophyll-a, and the proportion of degraded pheophytin to chlorophyll-a used as a proxy for bloom stage. Open water duration was one of the highest ranked factors in predicting fall blooms. Open water duration also influences the stage of midsummer (July) blooms as indicated by pheophytin proportions, which in turn were the highest-ranked factor for predicting fall bloom events in the Chirikov Basin (northern Bering Sea between St. Lawrence Island and the Bering Strait) and the Chukchi Sea. Wind direction, specifically easterly winds, was an important predictor in the northern Bering Sea. Maximum wind speed ranked highly at stations located within the nutrient-poor Alaska Coastal Current in the Chukchi Sea. However, stormy days, average and maximum wind speeds generally ranked low in importance as a predictor of fall bloom events. Other parameters, including photosynthetic active radiation, modeled nutrient concentrations, mixed layer depth, and time since sea ice breakup date showed strong but regionally varying relationships with fall blooms. Altogether, results from these Random Forests models suggest that high wind events and storms in the absence of sea ice provide an incomplete narrative for initiating fall bloom events.

Gaffey, C. B. [Clark University, Worcester, MA (Un↗
DOE OSTI2022

A global lipid map reveals host dependency factors conserved across SARS-CoV-2 variants

A comprehensive understanding of host dependency factors for SARS-CoV-2 remains elusive. Here, we map alterations in host lipids following SARS-CoV-2 infection using nontargeted lipidomics. We find that SARS-CoV-2 rewires host lipid metabolism, significantly altering hundreds of lipid species to effectively establish infection. We correlate these changes with viral protein activity by transfecting human cells with each viral protein and performing lipidomics. We find that lipid droplet plasticity is a key feature of infection and that viral propagation can be blocked by small-molecule glycerolipid biosynthesis inhibitors. We find that this inhibition was effective against the main variants of concern (alpha, beta, gamma, and delta), indicating that glycerolipid biosynthesis is a conserved host dependency factor that supports this evolving virus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Disordered-to-ordered transitions in assembly factors allow the complex II catalytic subunit to switch binding partners

Complex II (CII) activity controls phenomena that require crosstalk between metabolism and signaling, including neurodegeneration, cancer metabolism, immune activation, and ischemia-reperfusion injury. CII activity can be regulated at the level of assembly, a process that leverages metastable assembly intermediates. The nature of these intermediates and how CII subunits transfer between metastable complexes remains unclear. In this work, we identify metastable species containing the SDHA subunit and its assembly factors, and we assign a preferred temporal sequence of appearance of these species during CII assembly. Structures of two species show that the assembly factors undergo disordered-to-ordered transitions without the appearance of significant secondary structure. The findings identify that intrinsically disordered regions are critical in regulating CII assembly, an observation that has implications for the control of assembly in other biomolecular complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes

Abstract Properties can be tailored by tuning composition in high-order composition spaces. For spaces with complex phase behavior, modeling the properties as a function of composition and phase distribution remains a formidable challenge. We present materials structure–property factorization (MSPF) as an approach to automate modeling of such data and identify synergistic phase interactions. MSPF is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks (DRNets) to matrix factorization-based modeling of the representative properties of each phase in a dataset. MSPF is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation, which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity relationships. Comparing the measured photoactivity to a learned model for non-interacting phases, synergistic phase interactions are identified to guide further photoactivity optimization and understanding. MSPF identifies synergistic interactions of a BiVO 4 -like phase with both Cu 2 V 2 O 7 -like and CuV 2 O 6 -like phases, creating avenues for understanding complex photoelectrocatalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Spatial variability in Arctic–boreal fire regimes influenced by environmental and human factors

Abstract Wildfire activity in Arctic and boreal regions is rapidly increasing, with severe consequences for climate and human health. Regional long-term variations in fire frequency and intensity characterize fire regimes. The spatial variability in Arctic–boreal fire regimes and their environmental and anthropogenic drivers, however, remain poorly understood. Here we present a fire tracking system to map the sub-daily evolution of all circumpolar Arctic–boreal fires between 2012 and 2023 using 375 m Visible Infrared Imaging Radiometer Suite active fire detections and the resulting dataset of the ignition time, location, size, duration, spread and intensity of individual fires. We use this dataset to classify the Arctic–boreal biomes into seven distinct ‘pyroregions’ with unique climatic and geographic environments. We find that these pyroregions exhibit varying responses to environmental drivers, with boreal North America, eastern Siberia and northern tundra regions showing the highest sensitivity to climate and lightning density. In addition, anthropogenic factors play an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic–boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events.

Geology↗
DOE OSTI2020

Novel genetic risk factors influence progression of islet autoimmunity to type 1 diabetes

Type 1 diabetes arises from the autoimmune destruction of insulin-producing beta-cells of the pancreas, resulting in dependence on exogenously administered insulin to maintain glucose homeostasis. In this study, our aim was to identify genetic risk factors that contribute to progression from islet autoimmunity to clinical type 1 diabetes. We analyzed 6.8 million variants derived from whole genome sequencing of 160 islet autoantibody positive subjects, including 87 who had progressed to type 1 diabetes. The Cox proportional-hazard model for survival analysis was used to identify genetic variants associated with progression. We identified one novel region, 20p12.1 (TASP1; genome-wide P < 5 × 10 –8 ) and three regions, 1q21.3 (MRPS21–PRPF3), 2p25.2 (NRIR), 3q22.1 (COL6A6), with suggestive evidence of association (P < 8.5 × 10 –8 ) with progression from islet autoimmunity to type 1 diabetes. Once islet autoimmunity is initiated, functional mapping identified two critical pathways, response to viral infections and interferon signaling, as contributing to disease progression. These results provide evidence that genetic pathways involved in progression from islet autoimmunity differ from those pathways identified once disease has been established. These results support the need for further investigation of genetic risk factors that modulate initiation and progression of subclinical disease to inform efforts in development of novel strategies for prediction and intervention of type 1 diabetes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization

The complexity and variability of biological data has promoted the increased use of machine learning methods to understand processes and predict outcomes. These same features complicate reliable, reproducible, interpretable, and responsible use of such methods, resulting in questionable relevance of the derived. outcomes. Here we systematically explore challenges associated with applying machine learning to predict and understand biological processes using a well- characterized in vitro experimental system. We evaluated factors that vary while applying machine learning classifers: (1) type of biochemical signature (transcripts vs. proteins), (2) data curation methods (pre- and post-processing), and (3) choice of machine learning classifier. Using accuracy, generalizability, interpretability, and reproducibility as metrics, we found that the above factors significantly mod- ulate outcomes even within a simple model system. Our results caution against the unregulated use of machine learning methods in the biological sciences, and strongly advocate the need for data standards and validation tool-kits for such studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Electrically tunable effective g-factor of a single hole in a lateral GaAs/AlGaAs quantum dot

Electrical tunability of the $g$-factor of a confined spin is a long-time goal of the spin qubit field. Here we utilize the electric dipole spin resonance (EDSR) to demonstrate it in a gated GaAs double-dot device confining a hole. This tunability is a consequence of the strong spin-orbit interaction (SOI) in the GaAs valence band. The SOI enables a spin-flip interdot tunneling, which, in combination with the simple spin-conserving charge transport leads to the formation of tunable hybrid spin-orbit molecular states. EDSR is used to demonstrate that the gap separating the two lowest energy states changes its character from a charge-like to a spin-like excitation as a function of interdot detuning or magnetic field. In the spin-like regime, the gap can be characterized by the effective $g$-factor, which differs from the bulk value owing to spin-charge hybridization, and can be tuned smoothly and sensitively by gate voltages.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Targeting transcription factors through an IMiD independent zinc finger domain

Abstract Immunomodulatory imide drugs (IMiDs) degrade specific C2H2 zinc finger degrons in transcription factors, making them effective against certain cancers. SALL4, a cancer driver, contains seven C2H2 zinc fingers in three clusters, including an IMiD degron in zinc finger cluster one (ZFC1). Surprisingly, IMiDs do not inhibit the growth of SALL4-expressing cancer cells. To overcome this limit, we focused on a non-IMiD domain, SALL4 zinc finger cluster four (ZFC4). By combining ZFC4-DNA crystal structure and an in silico docking algorithm, in conjunction with cell viability assays, we screened several chemical libraries against a potentially druggable binding pocket, leading to the discovery of SH6, a compound that selectively targets SALL4-expressing cancer cells. Mechanistic studies revealed that SH6 degrades SALL4 protein through the CUL4A/CRBN pathway, while deletion of ZFC4 abolished this activity. Moreover, SH6 treatment led to a significant 87% tumor growth inhibition of SALL4+ patient-derived xenografts and demonstrated good bioavailability in pharmacokinetic studies. In summary, these studies represent a new approach for IMiD independent drug discovery targeting C2H2 transcription factors such as SALL4 in cancer.

Liu, Bee Hui↗
DOE OSTI2018

Ni and Se co-doping increases the power factor and thermoelectric performance of CoSbS

In this paper, we present a new investigation on paracostibite (CoSbS), a potential thermoelectric material for power generation in the intermediate temperature range (700–900 K) consisting of earth abundant elements. We report an improved synthesis of single phase materials of Co 1–x Ni x SbS 1–y Se y (x = 0–0.07, y = 0–0.09) using vacuum melting and annealing followed by SPS processing. The effects of Ni and Se co-doping on the electronic and thermal transport properties of CoSbS were investigated. Doping with Ni on the Co site increases the carrier concentration and moves the Fermi level deep into the conduction band, producing an enhanced power factor. First-principles electronic band structure calculations indicate a multi valley nature of the conduction band where Ni doping leads to n-type behaviour and a sharp increase of the density of states effective mass, enhancing the Seebeck coefficient. Unlike the Ni doping which has little effect on thermal transport, alloying Se on the S site intensifies the alloying scattering resulting in a lower lattice thermal conductivity of 2.4 W m –1 K –1 at 900 K for Co 0.93 Ni 0.07 SbS 0.93 Se 0.07 . Alloying Se on the S site of the structure further boosts the power factor to 22 μW cm –1 K –2 and leads to a maximum ZT of 0.58 at 900 K, which is the best ZT value achieved in the system.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Halogenated PETN derivatives: interplay between physical and chemical factors in explosive sensitivity

Determining the factors that influence and can help predict energetic material sensitivity has long been a challenge in the explosives community. Decades of literature reports identify a multitude of factors both chemical and physical that influence explosive sensitivity; however no unifying theory has been observed. Recent work by our team has demonstrated that the kinetics of “trigger linkages” (i.e., the weakest bonds in the energetic material) showed strong correlations with experimental drop hammer impact sensitivity. These correlations suggest that the simple kinetics of the first bonds to break are good indicators for the reactivity observed in simple handling sensitivity tests. Herein we report the synthesis of derivatives of the explosive pentaerythritol tetranitrate (PETN) in which one, two or three of the nitrate ester functional groups are substituted with an inert group. Experimental and computational studies show that explosive sensitivity correlates well with Q (heat of explosion), due to the change in the number of trigger linkages removed from the starting material. In addition, this correlation appears more significant than other observed chemical or physical effects imparted on the material by different inert functional groups, such as heat of formation, heat of explosion, heat capacity, oxygen balance, and the crystal structure of the material.

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

Decoding substrate specificity determining factors in glycosyltransferase-B enzymes – insights from machine learning models

Substrate specificity is an essential characteristic of any enzyme's function and an understanding of the factors that determine this specificity is crucial for enzyme engineering. Unlike the structure of an enzyme which is directly impacted by its sequence, substrate specificity as an enzyme attribute involves a rather indirect relationship with sequence as it also depends on structural aspects that dictate substrate accessibility and active site dynamics. In this study, we explore the performance of classifier-based machine learning models trained on curated sequence and structural data for a class of glycosyltransferases (GTs), namely GT-Bs, to understand their substrate specificity determining factors. GTs enable the transfer of sugar moieties to other biomolecules such as oligosaccharides or proteins and are found in all kingdoms of life. In plants, GTs participate in the biosynthesis of plant cell wall biopolymers (e.g.: hemicelluloses and pectins) and are an integral part of the enzymatic machinery that enables the storage of carbon and energy as plant biomass. To elucidate the substrate specificity of uncharacterized GT-Bs, we constructed multi-label machine learning models (Support Vector Classifier, K-Nearest Neighbors, Gaussian Naïve-Bayes, Random Forest) that incorporate both sequence and structural features. These models achieve good predictive accuracies on test datasets. However, despite our use of structural information, we highlight that there is further scope for improvement in training these models to draw interpretable relationships between sequence, structure and substrate specificity determining motifs in GT-Bs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Influence of the polarization anisotropy on the linewidth enhancement factor and reflection sensitivity of 1.55- μ m InP-based InAs quantum dash lasers

This work investigates the effect of the polarization anisotropy on the linewidth enhancement factor and the reflection sensitivity of InAs/InP quantum dash semiconductor lasers. The results show that the small linewidth enhancement factor and high stability against external optical feedback are obtained for nanostructures oriented perpendicular to the cavity axis as opposed to those oriented parallel to the cavity axis. Effective simulations on the critical feedback level of these two lasers are also in agreement with experimental results. Such anisotropy is attributed to the polarization dependence of the transition matrix element in these quantum nanostructures.

Dong, B. (ORCID:0000000158266723)↗
DOE OSTI2020

Heterostructure design to achieve high quality, high density GaAs 2D electron system with g -factor tending to zero

Hydrostatic pressure is a useful tool that can tune several key parameters in solid state materials. For example, the Landé g-factor in GaAs two-dimensional electron systems (2DESs) is expected to change from its bulk value g ≃ -0.44 to zero and even to positive values under sufficiently large hydrostatic pressure. Although this presents an intriguing platform to investigate electron-electron interaction in a system with g = 0, studies are quite limited because the GaAs 2DES density decreases significantly with increasing hydrostatic pressure. Here, we show that a simple model, based on pressure-dependent changes in the conduction band alignment, quantitatively explains this commonly observed trend. Furthermore, we demonstrate that the decrease in the 2DES density can be suppressed by more than a factor of 3 through an innovative heterostructure design.

Physics↗
DOE OSTI2021

Intrinsic quality factor extraction of multi-port cavity with arbitrary coupling

In this work, we derived S-parameter based expressions for the intrinsic quality factor of an arbitrary coupled multiport microwave cavity and a non-ideal test fixture. Practical accuracy limitations of the obtained expressions specifically for superconducting accelerator cavities were evaluated both analytically and with the simulation software. The resulting formulas can be used to extract the intrinsic quality factor of normal-conducting and superconducting cavities directly from the calibrated S-parameter measurements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Increased thermal conductivity and decreased electron–phonon coupling factor of the aluminum scandium intermetallic phase (Al 3 Sc) compared to solid solutions

Aluminum scandium alloys and their intermetallic phases have arisen as potential candidates for the next generation of electrical interconnects. Here, in this work, we measure the in-plane thermal conductivity and electron–phonon coupling factor of aluminum scandium alloy thin films deposited at different temperatures, where the temperature is used to control the grain size and volume fraction of the Al 3 Sc intermetallic phase. As the Al 3 Sc intermetallic formation increases with higher deposition temperature, we measure increasing in-plane thermal conductivity and a decrease in the electron–phonon coupling factor, which corresponds to an increase in grain size. Our findings demonstrate the role that chemical ordering from the formation of the intermetallic phase has on thermal transport.

Chemical elements↗