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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.

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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

DISCOVERY OF A SUPERCLUSTER AT z ∼ 0.91 AND TESTING THE ΛCDM COSMOLOGICAL MODEL

The ΛCDM cosmological model successfully reproduces many aspects of the galaxy and structure formation of the universe. However, the growth of large-scale structures (LSSs) in the early universe is not well tested yet with observational data. Here, we have utilized wide and deep optical–near-infrared data in order to search for distant galaxy clusters and superclusters (0.8 < z < 1.2). From the spectroscopic observation with the Inamori Magellan Areal Camera and Spectrograph (IMACS) on the Magellan telescope, three massive clusters at z ∼ 0.91 are confirmed in the SSA22 field. Interestingly, all of them have similar redshifts within Δ z ∼ 0.01 with velocity dispersions ranging from 470 to 1300 km s{sup −1}. Moreover, as the maximum separation is ∼15 Mpc, they compose a supercluster at z ∼ 0.91, meaning that this is one of the most massive superclusters at this redshift to date. The galaxy density map implies that the confirmed clusters are embedded in a larger structure stretching over ∼100 Mpc. ΛCDM models predict about one supercluster like this in our surveyed volume, consistent with our finding so far. However, there are more supercluster candidates in this field, suggesting that additional studies are required to determine if the ΛCDM cosmological model can successfully reproduce the LSSs at high redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1980

Deep Space Station (DSS-13) automation demonstration

The data base collected during a six month demonstration of an automated Deep Space Station (DSS 13) run unattended and remotely controlled is summarized. During this period, DSS 13 received spacecraft telemetry data from Voyager, Pioneers 10 and 11, and Helios projects. Corrective and preventive maintenance are reported by subsystem including the traditional subsystems and those subsystems added for the automation demonstration. Operations and maintenance data for a comparable manned Deep Space Station (DSS 11) are also presented for comparison. The data suggests that unattended operations may reduce maintenance manhours in addition to reducing operator manhours. Corrective maintenance for the unmanned station was about one third of the manned station, and preventive maintenance was about one half.

Remer, D. S.↗
DOE OSTI2022

Hubble Space Telescope Observations of GW170817: Complete Light Curves and the Properties of the Galaxy Merger of NGC 4993

We present the complete set of Hubble Space Telescope imaging of the binary neutron star merger GW170817 and its optical counterpart AT 2017gfo. Including deep template imaging in F814W, F110W, F140W, and F160W at 3.4 yr post-merger, we reanalyze the full light curve of AT 2017gfo across 12 bands from 5 to 1273 rest-frame days after merger. We obtain four new detections of the short γ-ray burst 170817A afterglow from 109 to 170 rest-frame days post-merger. These detections are consistent with the previously observed β = -0.6 spectral index in the afterglow light curve with no evidence for spectral evolution. We also analyze our limits in the context of kilonova afterglow or IR dust echo emission but find that our limits are not constraining for these models. We use the new data to construct deep optical and IR stacks, reaching limits of M = -6.3 to -4.6 mag, to analyze the local environment around AT 2017gfo and low surface brightness features in its host galaxy NGC 4993. We rule out the presence of any globular cluster at the position of AT 2017gfo to 2.3 × 10$^{4}$L$_{⊙}$, including those with the reddest V - H colors. Finally, we analyze the substructure of NGC 4993 in deep residual imaging and find shell features that extend up to 71."8 (14.2 kpc) from NGC 4993. The shells have a cumulative stellar mass of 6.3 × 10$^{8}$M$_{⊙}$, roughly 2% of NGC 4993, and mass-weighted ages of >3 Gyr. We conclude that it was unlikely that the GW170817 progenitor system formed in the galaxy merger.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

Information Systems for Crew-Led Operations Beyond Low-Earth Orbit

On past and present human space missions, the management of vehicle health and status has primarily been executed from Earth. Missions such as Apollo, Space Shuttle, and ISS have relied on a safety net of ground-based experts with access to real-time telemetry data, broad and deep systems expertise, and powerful analytical and computing capabilities. The ground team monitors and manages the vehicle’s health in real-time and responds quickly to critical situations and malfunctions. Ground operators also provide real-time oversight and verbal guidance to flight crew members, especially during complex procedure execution and high-risk activities like extra-vehicular activities. However, this operational paradigm, in place for 60 years, will not transfer to long duration exploration missions beyond low Earth orbit (LEO). Lunar and deep-space crewed missions will encounter delayed communications that prohibit real-time operational and medical support. Additionally, there will be infrequent resupply and a diminished capacity to evacuate or rescue crew members. A small crew must operate independently, managing the vehicle’s state, responding to time-critical events, and executing complex procedures, all without the safety net of real-time support.

data representation↗
DOE OSTI2019

Krypton storage capacity of the Earth's lower mantle

Noble gases are important geochemical tracers allowing reconstructing global volatile cycles in Earth's reservoirs. To constrain these fundamental processes, precise data on their partitioning behavior at deep Earth conditions are needed. Such data are only available at moderate pressures up to 25 GPa due to experimental challenges. We have investigated the possibility of noble gas storage in the Earth's lower mantle up to 115 GPa. We studied the incorporation of krypton in the second most abundant lower mantle mineral (Mg 1-x ,Fe x )O (ferropericlase) as well as in liquid metal-alloys by performing experiments up to 115 GPa and 3700 K using the laser-heated diamond anvil cell coupled to post-mortem EMPA analysis and X-ray absorption spectroscopy. The results reveal that, at these extreme conditions, up to 3 wt.% of krypton can be stored in (Mg 1-x ,Fe x )O and 3000 ppm in the Fe-rich liquid metal. For both phases the storage capacities increase with pressure (between 40 GPa and 60 GPa) at a constant high temperature of 2300 K. Fpc has never been considered as a NG host, despite being the second most abundant mineral in the Earth's LM. Using recent accurate compressibility data, we demonstrate that a substitution of krypton into the anion site of (Mg 1-x ,Fe x )O in form of neutral oxygen Schottky defects at diluted lower mantle conditions is possible. This noble gas incorporation mechanism is in agreement with a previous study on bridgmanite. We show that (Mg 1-x ,Fe x )O exhibits higher noble gas storage capacities than bridgmanite through the lower mantle using lattice strain modeling and including experimental solubility and thermoelastic data for neon, argon, krypton and xenon. We also demonstrate that both phases exhibit the highest solubilities for argon and krypton. We used the solubility data from lattice strain modeling to predict noble gas abundances stored in the solid lower mantle after magma ocean crystallization. The modeled abundances show apparent similarities with estimates for the deep noble gas reservoir that are based on either 3He abundances in ocean island basalts or radiogenic 40Ar abundances in the bulk Earth. This strongly indicates that the crystalline lower mantle may play an important role as deep noble gas storage reservoir. We propose, based on considerations on noble gas replenishment from the lower mantle to the atmosphere, that the lower mantle can only contribute to a small fraction of the present-day atmospheric noble gases. This suggests that the lower mantle is an un-degassed reservoir.

58 GEOSCIENCES↗
NASA NTRS1984

Plasmasheet magnetic fields in the distant tail

Data from two deep passes of ISEE-3 through the earth's magnetotail are used to discuss the evolution of the plasmasheet magnetic field as a function of radial distance and geomagnetic activity (Kp). Attention is focused on plasmasheet intervals lasting at least 10 min as recorded during passes lasting 3 and 5 mos with apogees of 221 and 238 earth radii (ER). The dependencies of the By and Bz components on Kp were minimal, although Bz displayed a radial dependence. No relationship was observed between By and Bz. The Bz component changed from a northward orientation at 60-100 ER to a N-S dependence at 200-238 ER. Variations in field measurements indicated turbulence throughout the plasmasheet, also independent of Kp. The data support formation of a plasmoid during periods of high Kp, and can serve as limiting conditions for developing more accurate magnetotail models.

Tsurutani, B. T.↗
DOE OSTI2020

ContourNet: Salient Local Contour Identification for Blob Detection in Fusion Plasma Simulation Data

We present ContourNet, a deep learning approach to identify salient local isocontours as blobs in large-scale 5D gyrokinetic tokamak simulation data. Blobs-regions of high turbulence that run along the edge wall down toward the diverter and can damage the tokamak-are non-well-defined features but have been empirically localized by isocontours in 2D normalized fluctuating density fields. The key of our study is to train ContourNet to follow the empirical rules to detect blobs over the time-varying simulation data. The architecture of ContourNet is a convolutional neural segmentation network: the inputs are the density field and a rasterized isocontour; the output is a set of isocontour encircling blobs. At the training stage, we feed the network with manually identified isocontours and propagated labels. At the inference stage, we extract isocontours from the segmented blob regions. Results show that our approach can achieve both high accuracy and performance, which enables scientists to understand the blob dynamics influencing the confinement of the plasma.

Blob detection↗
DOE OSTI2018

The Clustering of High-redshift (2.9 ≤ $z$ ≤ 5.1) Quasars in SDSS Stripe 82

We present a measurement of the two-point autocorrelation function of photometrically selected high-$z$ quasars over ~100 deg 2 on the Sloan Digital Sky Survey Stripe 82 field. Selection is performed using three machine-learning algorithms in a six-dimensional optical/mid-infrared color space. Optical data from the Sloan Digital Sky Survey are combined with overlapping deep mid-infrared data from the $Spitzer$ IRAC Equatorial Survey and the $Spitzer$-HETDEX Exploratory Large-Area survey. Our selection algorithms are trained on the colors of known high-$z$ quasars. The selected quasar sample consists of 1378 objects and contains both spectroscopically confirmed quasars and photometrically selected quasar candidates. These objects span a redshift range of 2.9 ≤ $z$ ≤ 5.1 and are generally fainter than $i$ = 20.2, a regime that has lacked sufficient number density to perform autocorrelation function measurements of photometrically classified quasars. We compute the angular correlation function of these data, marginally detecting quasar clustering. We fit a single power law with an index of $δ$ = 1.39 ± 0.618 and amplitude of $θ_0$ = 0$^{\prime}_{.}$71 ± 0$^{\prime}_{.}$546 . A dark matter model is fit to the angular correlation function to estimate the linear bias. At the average redshift of our survey ($\langle z\rangle =3.38$), the bias is $b$ = 6.78 ± 1.79. Using this bias, we calculate a characteristic dark matter halo mass of 1.70–9.83$\times {10}^{12}{h}^{-1}\,{M}_{\odot }$. Our bias estimate suggests that quasar feedback intermittently shuts down the accretion of gas onto the central supermassive black hole at early times. If confirmed, these results hint at a level of luminosity dependence in the clustering of quasars at high-$z$.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Data-Driven Distribution System Coordinated PV Inverter Control Using Deep Reinforcement Learning

The deployment of distributed solar photovoltaic (PV) systems has increased consistently over the past decades. High penetrations of PVs could cause a series of adverse grid impacts, such as voltage violations. The recent development of smart inverter technologies rises the incentives of developing PV control solutions that regulate the inverter output power and seeking the optimization on system operational objectives. This paper proposes a data-driven control solution based on deep reinforcement learning (DRL) to optimize PV inverters for voltage regulation. The proposed solution can minimize PV real power curtailment while maintaining network voltage at an acceptable range. Comparison results between the proposed DRL control algorithms with deep deterministic policy gradient (DDPG) and volt-var control on a real feeder in west Colorado highlight the advantage of the proposed framework in controlling the system voltage while minimizing the PV real power curtailment.

deep reinforcement learning↗
DOE OSTI2021

Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data

Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit from the use of machine learning. Incorporating these computational tools in the experimental framework presents new challenges. This paper reports on the implementation of the end-to-end deep learning with the CMS software framework and the scaling of the end-to-end deep learning with multiple GPUs. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation for particle and event identification. We demonstrate the end-to-end implementation on a top quark benchmark and perform studies with various hardware architectures including single and multiple GPUs and Google TPU.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗
DOE OSTI2019

Major shifts in nutrient and phytoplankton dynamics in the North Pacific Subtropical Gyre over the last 5000 years revealed by high-resolution proteinaceous deep-sea coral δ 15 N and δ 13 C records

The North Pacific Subtropical Gyre (NPSG) is the largest continuous ecosystem on Earth and is a critical component of global oceanic biogeochemical cycling and carbon sequestration. We report here multi-millennial-scale, sub-decadal-resolution records of bulk stable nitrogen ( δ 15 N) and carbon ( δ 13 C) isotope records from proteinaceous deep-sea corals. Data from three Kulamanamana haumeaae specimens from the main Hawaiian Islands extend the coral-based time-series back ~5000 yrs for the NPSG and bypass constraints of low resolution sediment cores in this oligotrophic ocean region. We interpret these records in terms of shifting biogeochemical cycles and plankton community structure, with a main goal of placing the extraordinarily rapid ecosystem biogeochemical changes documented by recent coral records during the Anthropocene in a context of broader Late-Holocene variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Newton-Raphson AC Power Flow Convergence Based on Deep Learning Initialization and Homotopy Continuation

Power flow forms the basis of many power system studies. With the increased penetration of renewable energy, grid planners tend to perform multiple power flow simulations under various operating conditions and not just selected snapshots at peak or light load conditions. Getting a converged AC power flow (ACPF) case remains a significant challenge for grid planners especially in large power grid networks. This paper proposes a two-stage approach to improve Newton-Raphson ACPF convergence and was applied to a 6102 bus Electric Reliability Council of Texas (ERCOT) system. The first stage utilizes a deep learning-based initializer with data re-training. Here a deep neural network (DNN) initializer is developed to provide better initial voltage magnitude and angle guesses to aid in power flow convergence. This is because Newton-Raphson ACPF is quite sensitive to the initial conditions and bad initialization could lead to divergence. The DNN initializer includes a data re-training framework that improves the initializer's performance when faced with limited training data. The DNN initializer successfully solved 3,285 cases out of 3,899 non-converging dispatch and performed better than random forest and DC power flow initialization methods. ACPF cases not solved in this first stage are then passed through a hot-starting algorithm based on homotopy continuation with switched shunt control. The hot-starting algorithm successfully converged 416 cases out of the remaining 614 non-converging ACPF dispatch. In conclusion, the combined two-stage approach achieved a 94.9% success rate, by converging a total of 3,701 cases out of the initial 3,899 unsolved cases.

Deep learning↗
DOE OSTI2016

SEARCHING FOR BULK MOTIONS IN THE INTRACLUSTER MEDIUM OF MASSIVE, MERGING CLUSTERS WITH CHANDRA CCD DATA

We search for bulk motions in the intracluster medium (ICM) of massive clusters showing evidence of an ongoing or recent major merger with spatially resolved spectroscopy in Chandra CCD data. We identify a sample of six merging clusters with >150 ks Chandra exposure in the redshift range 0.1 < z < 0.3. By performing X-ray spectral analysis of projected ICM regions selected according to their surface brightness, we obtain the projected redshift maps for all of these clusters. After performing a robust analysis of the statistical and systematic uncertainties in the measured X-ray redshift z{sub X}, we check whether or not the global z{sub X} distribution differs from that expected when the ICM is at rest. We find evidence of significant bulk motions at more than 3σ in A2142 and A115, and less than 2σ in A2034 and A520. Focusing on single regions, we identify significant localized velocity differences in all of the merger clusters. We also perform the same analysis on two relaxed clusters with no signatures of recent mergers, finding no signs of bulk motions, as expected. Our results indicate that deep Chandra CCD data enable us to identify the presence of bulk motions at the level of v{sub BM} > 1000 km s{sup −1} in the ICM of massive merging clusters at 0.1 < z < 0.3. Although the CCD spectral resolution is not sufficient for a detailed analysis of the ICM dynamics, Chandra CCD data constitute a key diagnostic tool complementing X-ray bolometers on board future X-ray missions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics

In recent years deep neural networks have been proposed as a lightweight data-driven model to capture high-dimensional, nonlinear physical processes to predict building thermal responses. However, the need of a large amount of data for the training process of deep neural networks clashes with the potential limited data availability in most existing or new buildings. Transfer learning aims to enhance the performance of a target learner exploiting knowledge from related and similar environments. This study conducted a suite of experiments that leveraged 250 data-driven models based on a synthetic dataset of a building archetype to study the influence of data availability, energy efficiency level, occupancy and climate for the transfer process of thermal dynamics. The performance of the transfer learning process was compared against a classical machine learning approach. Here, the results suggest that building thermal dynamics can be effectively transferred under the same climatic conditions, increasing performance when dealing with different occupancy schedules, efficiency levels and low data availability. Furthermore, the paper compares the performance of both transfer learning and machine learning approaches in an online fashion, to support the implementation in real-world deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2014

Extreme Blazars Studied with Fermi-Lat and Suzaku: 1ES 0347-121 and Blazar Candidate HESS J1943+213

We report on our study of high-energy properties of two peculiar TeV emitters: the "extreme blazar" 1ES 0347-121 and the "extreme blazar candidate" HESS J1943+213 located near the Galactic plane. Both objects are characterized by quiescent synchrotron emission with flat spectra extending up to the hard X-ray range, and both were reported to be missing GeV (gigaelectronvolt) counterparts in the Fermi Large Area Telescope (LAT) two-year Source Catalog. We analyze a 4.5 year accumulation of the Fermi-LAT data, resulting in the detection of 1ES 0347-121 in the GeV band, as well as in improved upper limits for HESS J1943+213. We also present the analysis results of newly acquired Suzaku data for HESS J1943+213. The X-ray spectrum is well represented by a single power law extending up to 25 keV (kiloelectronvolts) with photon index 2.00 plus or minus 0.02 and a moderate absorption in excess of the Galactic value, which is in agreement with previous X-ray observations. No short-term X-ray variability was found over the 80 kilosecond duration of the Suzaku exposure. Under the blazar hypothesis, we modeled the spectral energy distributions of 1ES 0347-121 and HESS J1943+213, and we derived constraints on the intergalactic magnetic field strength and source energetics. We conclude that although the classification of HESS J1943+213 has not yet been determined, the blazar hypothesis remains the most plausible option since, in particular, the broadband spectra of the two analyzed sources along with the source model parameters closely resemble each other, and the newly available Wide-field Infrared Survey Explorer (WISE) and UKIRT (United Kingdom Infrared Telescope) Infrared Deep Sky Survey data for HESS J1943+213 are consistent with the presence of an elliptical host at the distance of approximately 600 Mpc (megaparsecs).

blazar↗
DOE OSTI2020

Accurate Identification of Deamidation and Citrullination from Global Shotgun Proteomics Data Using a Dual-Search Delta Score Strategy

While proteins with deamidated/citrullinated amino acids play critical roles in the pathogenesis of many human diseases, identifying these modifications in complex biological samples has been an ongoing challenge. Herein we present a method to accurately identify these modifications from shotgun proteomics data from a deep proteome profiling study of human pancreatic islets obtained by laser capture microdissection. All MS/MS spectra were searched against database by MSGF+ twice with or without a +0.9840 Da mass shift on amino acids asparagine, glutamine, and arginine (NQR) as a dynamic modification. Consequently, for each spectrum the resulting two peptide-to-spectrum matches (PSM) with their respective MSGF+ scores were used for Delta Score calculation. It was observed that all PSMs with positive Delta Score values were clustered with mass errors around 0 ppm, while PSMs with negative Delta Score values were distributed nearly equally within the defined mass error range (20 ppm) for database searching. To estimate false discovery rate (FDR), a “pseudo-decoy” approach was applied whether datasets were searched against a database with a “real modification” mass shift (+0.9840 Da) and a “mock modification” mass shift (+1.0227 Da). FDR was controlled to ~2% with a Delta Score filter greater than zero. Manual inspection of spectra showed that PSMs with positive Delta Score value contained deamidated/citrullinated fragments in their MS/MS spectra. Finally, the results demonstrated that in-situ deamidated/citrullinated peptides can be accurately identified from shotgun tissue proteomics data by the Dual-search Delta Score Strategy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers

The concentration of dissolved oxygen (DO), an important measure of water quality and river metabolism, varies tremendously in time and space. Riverine DO is commonly perceived as regulated by interacting and competing drivers (light, temperature and flow) that define rivers’ climate. Its continental-scale drivers, however, have remained elusive, partly due to the scarcity and spatio-temporal inconsistency of water quality data. Here we show, via a deep learning model (long short-term memory) trained using data from 580 rivers, that temperature predominantly drives daily DO dynamics in the contiguous United States. Light comes a close second, whereas flow imparts minimal influence. This work showcases the promise of using deep learning models for data filling that enables large-scale systematic analysis of patterns and drivers. Results show fairly accurate prediction of DO by temperature alone, and declining DO in warming rivers, which has important implications for water security and ecosystem health in the future climate.

54 ENVIRONMENTAL SCIENCES↗