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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Morphological and Chemical Differentiation between Tunisian Populations of Pinus halepensis , Pinus brutia , and Pinus pinaster

Abstract The lipid fraction of seeds from different pine species and populations was studied regarding total lipid content, fatty acid profile and vitamin E composition. The investigated seeds contained a high percentage of lipid (13.6 to 31.5 %). Lipid fractions were found to be rich in vitamin E, which varied significantly among species and populations. P. halepensis (Ph−Hn) showed the highest content of vitamin E (256.3 mg/kg of seeds) and the uppermost content of α ‐tocopherol (44 mg/kg). However, P. halepensis (Ph−Kas) was the richest in γ ‐tocopherol (204.9 mg/kg). Lipid fractions had a low content of δ ‐tocopherol (1.2 to 3.6 mg/kg. The highest content of γ ‐tocotrienol (∼18 %) was determined for P. halepensis (Ph−Dc and Ph−Hn). Thirteen fatty acids were identified by GC‐FID with significant variation between the investigated species. The linoleic acid was the major fatty acid followed by oleic acid and palmitic acid. The chemical differentiation among species for the composition of fatty acids and vitamin E was confirmed by PCA. Significant correlations were observed between the content of vitamin E and fatty acids and ecological parameters of P. halepensis populations.

Khouja, Mariem↗
DOE OSTI2020

Topologically derived dislocation theory for twist and stretch moiré superlattices in bilayer graphene

In this work, we develop a continuum dislocation description of twist and stretch moiré superlattices in two-dimensional material bilayers. The continuum formulation is based on the topological constraints introduced by the periodic dislocation network associated with the moiré structure. The approach is based on solving analytically for the structural distortion and displacement fields that satisfy the topological constraints and that minimize the total energy. The total energy is described by both the strain energy of each individual distorted layer and a Peierls-Nabarro-like interfacial contribution arising from stacking disregistry. The dislocation core emerges naturally within the formalism as a result of the competition between the two contributions. The approach presented here captures the structure and energetics of twist and stretch moiré superlattices of dislocations with arbitrary direction and character, without assuming an analytical solution a priori and while accounting naturally for dislocation-dislocation image interactions. In comparisons to atomistic simulations using classical potentials, the maximum structure deviation is 6%, while the maximum line energy deviation is 0.019 eV/Å. Several applications of our model are shown, including predicting the variation of structure with twist angle and describing dislocation line tension and junction energies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

TRISO fuel performance analysis: Uncertainty quantification toward optimization

Tri-structural isotropic (TRISO) fuel particles are a fuel form being considered for potential use in next-generation nuclear reactors (i.e., high-temperature gas-cooled reactors). Though the TRISO fuel manufacturing process has continually advanced in recent years, particle comparisons still reveal statistical variations and uncertainties in terms of geometric configurations and material properties. Given that the physical processes ongoing in TRISO fuel particles during reactor operation are highly correlated with each other, a small degree of uncertainty in one model may lead to significant uncertainty in another. This makes appropriate uncertainty quantification of TRISO fuel particles essential. However, one may wonder about the extent to which the current version of TRISO particles has been optimized, and whether any room remains for further improvements. This paper quantifies TRISO fuel performance model uncertainties that stem from geometric and material data. For this analysis, the BISON code was used, and the Advanced Gas Reactor (AGR)-2 experiment served as a reference case. A total of 10 5 calculations was performed for the uncertainty and optimization analysis, altering the geometric and material data within their uncertainty range. Lastly, the optimization potential of TRISO particles is evaluated from a fuel performance perspective.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Secondary electron emission of reticulated foam materials

Complex material surfaces can reduce secondary electron emission (SEE) and sputtering via geometric trapping. In this work, the SEE yields for a range of open-cell reticulated carbon foam geometries are characterized using scanning electron microscopy. The total reduction in the SEE yield from carbon foams with a 3% volume fill density and 10–100 pores per inch (PPI) is shown to be between 23.5% and 35.0%. Contributions of a foam backplate are assessed by experimentally and analytically defining the critical parameter, transparency. The transparency of a foam is quantified and is shown to affect the primary electron angular dependence on the SEE yield. For the same thickness of 6 mm, it is found that higher PPI decreases foam transparency from 32% to 0% and reduces the SEE yield. The SEE yield from carbon foams is also shown to have weaker dependence on the morphology of the surface compared with fuzzes and velvets and less variation across individual sample surfaces due to the rigidity of their ligament structures and isotropic geometries.

Physics↗
DOE OSTI2026

SPARTAN and IMPROVE Comparison Experiment (SPICE) Interim Campaign Report

SPICE (the SPARTAN and IMPROVE Comparison Experiment) aims to obtain and quantify comparisons between aerosol PM 2.5 mass concentration measurements from the University of Oklahoma (OU) Surface Particulate Matter Network (SPARTAN) station and the U.S. Environmental Protection Agency (EPA) Interagency Monitoring of Protected Visual Environments (IMPROVE) station hosted by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility at ARM’s Southern Great Plains (SGP) observatory in Oklahoma. Aerosols—both natural and anthropogenic—affect human populations in multiple ways. Much of ARM’s research focuses on how aerosols influence weather and climate through their optical and radiative effects, as well as their impacts on clouds and precipitation. However, aerosols also pose direct risks to humans and other organisms through inhalation, with the severity of health impacts depending on particle size, chemical composition, and duration of exposure. The IMPROVE network was established by the U.S. EPA to monitor air quality, including visible clarity as well as total and chemically speciated aerosol mass concentrations. The ARM SGP site hosts the IMPROVE SOGP station. Separately, the SPARTAN network operates a globally distributed set of stations similar to IMPROVE but with an emphasis on remote deployment and semi-autonomous operation for use beyond the borders of the United States (IMPROVE only operates within the U.S.). The University of Oklahoma operates a SPARTAN station. To establish confidence in the OU SPARTAN instrumentation and measurement protocol relative to the EPA-certified IMPROVE station, the OU SPARTAN station is currently deployed at SGP in close proximity to the IMPROVE SOGP station. The SPICE campaign was envisioned as a contiguous calendar-year effort for 2025 to capture seasonal variation in mass loading as well as composition. However, independent of the SPICE campaign, the SPARTAN network adopted a new filter construction part-way through the year, interrupting our contiguous data set. Thus, to obtain a contiguous data set with a uniform consistent configuration, SPICE desires an extension through 2026.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multiwavelength Bulge–Disk Decomposition for the Galaxy M81 (NGC 3031). I. Morphology

A panchromatic investigation of morphology for the early-type spiral galaxy M81 is presented in this paper. We perform bulge–disk decomposition in M81 images at a total of 20 wave bands from far-UV to near-IR (NIR) obtained with GALEX, Swift, Sloan Digital Sky Survey, WIYN, Two Micron All Sky Survey, Wide-field Infrared Survey Explorer, and Spitzer. Morphological parameters such as Sérsic index, effective radius, position angle, and axis ratio for the bulge and the disk are thus derived at all of the wave bands, which enables quantifying the morphological K -correction for M81 and makes it possible to reproduce images for the bulge and the disk in the galaxy at any wave band. The morphology as a function of wavelength appears as a variable-slope trend of the Sérsic index and the effective radius, in which the variations are steep at UV–optical and shallow at optical–NIR bands; the position angle and the axis ratio keep invariable at least at optical–NIR bands. It is worth noting that the Sérsic index for the bulge reaches ~4–5 at optical and NIR bands, but drops to ~1 at UV bands. This difference brings forward a caveat that a classical bulge is likely misidentified for a pseudobulge or no bulge at high redshifts where galaxies are observed through rest-frame UV channels with optical telescopes. The next work of this series is planned to study spatially resolved spectral energy distributions for the bulge and the disk, respectively, and thereby explore stellar population properties and star formation/quenching history for the galaxy composed of the subsystems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Fe Kα XANES, Fe Kβ HERFD XANES and EPMA flank method determinations of the oxidation state of Fe in garnet

The ferric to total iron ratios (Fe 3+ /ΣFe) of garnets can be paired with thermodynamic mineral activity models to quantify the oxygen fugacity of garnet-bearing rocks. However, techniques with a high analytical and spatial resolution are necessary to distinguish differences in garnet Fe 3+ /ΣFe ratios at the percent level and to accurately measure garnets that are zoned or contain inclusions. We acquired conventional Fe Kα and high-resolution energy fluorescence detection (HERFD) Fe Kβ X-ray absorption near edge structure (XANES) spectra and electron microprobe flank method analyses on a suite of 27 peridotitic and eclogitic garnets with Fe 3+ /ΣFe ratios previously determined by Mössbauer spectroscopy to evaluate the precision of each technique. We examined variations in the energy and intensity of three XANES spectral features as a function of Fe 3+ /ΣFe ratios: 1) the intensity ratio of two-post edge features (I-ratio; Fe Kα only); 2) the energy of the Fe edge at 90% normalized intensity (E 0.9 ; Fe Kα only) and 3) the pre-edge centroid energy (Fe Kα and HERFD Fe Kβ). In accordance with previous work, we find the energies of garnet pre-edge centroids are relatively insensitive to Fe 3+ /ΣFe ratios. The I-ratios of peridotitic and eclogitic garnets are offset from each other at low Fe 3+ /ΣFe ratios (≤0.13); I-ratio garnet XANES calibrations are composition-specific. The E 0.9 feature is independent of garnet major element composition in spectra that have been corrected for the effects of self-absorption. We produce two Fe Kα garnet XANES calibrations based on variations in the E0.9 feature; one calibration with all garnet reference materials included (Fe 3+ /ΣFe up to 1.0; “all garnet calibration”) and another calibration specific to garnets with low Fe 3+ /ΣFe ratios (“low ferric calibration”). Fe 3+ /ΣFe ratios calculated from the mean of up to 25 flank method measurements on eight garnet reference materials fall within 4% absolute of a one-to-one correlation with Fe 3+ /ΣFe ratios measured by Mössbauer. The standard error of the mean Fe 3+ /ΣFe ratio calculated from flank method approaches the Mössbauer-determined Fe 3+ /ΣFe ratio within estimated error (3%) after three analyses. Flank method precision is enhanced at higher beam current; however, the precision of the flank method does not approach the precision of XANES under any microprobe analytical condition tested here. Garnet reference materials detailed here are available by request to the Smithsonian Institution.

58 GEOSCIENCES↗
DOE OSTI2025

Universal method for the optimization of HDC coating uniformity on non-planar, non-stationary substrates for inertial confinement fusion targets

The thickness uniformity of chemical vapor deposited (CVD) diamond coatings on non-planar, non-stationary substrates depends on both the intrinsic instantaneous coating thickness distribution (ICTD) of the coating conditions used and, if applicable, on the frequency of substrate reorientation. While important for many CVD diamond applications, the relative impact of the ICTD and substrate reorientation on the coating thickness uniformity has not been studied. In this work, we systematically investigate the effect of these factors for microwave-plasma chemical vapor deposition (MPCVD) of diamond (referred to as high density carbon (HDC) in the inertial confinement fusion (ICF) community) coatings on spherical, rolling substrates. This coating technique is used to fabricate capsules for ICF experiments, which require extreme coating uniformity with <0.3 % thickness variation (so-called Mode 1 or M1) to ensure symmetric compression of imploding targets. To extract the otherwise unobservable reorientation timescale (Δt), Monte Carlo simulations were performed using experimental ICTD data as input. This combined approach confirms scaling relationships between the substrate reorientation timescale as well as coating thickness and coating uniformity, as expected from a 3D random walk. Simulations confirm that M1 is Rayleigh-distributed and scales as (Δt) 1/2 , consistent with the randomization of two angles that determine orientation of a sphere. We also demonstrate that, under the conditions studied, Δt is the dominant factor in determining thickness uniformity while the intrinsic ICTD has minimal impact. Finally, experiments show that Δt can be affected by total batch size under constant agitation conditions due to space constraints that limit the capsule reorientation kinetics. In conclusion, this study highlights the utility of a combined experiment-simulation approach as a general methodology for understanding and improving coating uniformity on non-planar, non-stationary substrates.

Capsule↗
DOE OSTI2025

Estimation of intensity, footprint, and capacity of surface urban heat islands using a direction-enhanced adaptive synchronous extraction (DEASE) method

Here, the surface urban heat island (SUHI) effect, assessed through remotely sensed land surface temperature (LST), remains a focal point in urban climate research. Conventional indicators like SUHI intensity (SUHII) and footprint (SUHIF) capture peak values and spatial extent but fail to account for the cumulative thermal load—a critical dimension reflecting the total heat exposure imposed by spatially continuous warming, which directly limits a holistic assessment of ecological and societal impacts of the SUHI effect. Therefore, this study introduces an indicator termed SUHI capacity (SUHIC), designed to quantify the aggregated SUHI effect by integrating the magnitude of the warming signal across all affected areas, thereby enabling a more comprehensive evaluation of urban thermal environments. Furthermore, a direction-enhanced adaptive synchronous extraction (DEASE) method is proposed for the quantification of SUHIC. This method can dynamically identify the optimal background reference area based on the urban-rural LST gradients in various directions within the city, without relying on predefined mathematical models as previously. The results from 102 European cities first confirm that the directional variations in urban-rural LST gradients, and the DEASE method can effectively capture these distinctions for the simultaneous estimation of SUHII, SUHIF, and SUHIC. Secondly, the spatial patterns of absolute SUHIC values show strong associations with those of SUHIF (R2>0.86), while its relative values (normalized by the area of urban) align more closely with SUHII (R2 > 0.64). More importantly, SUHIC can serve as a crucial reference for assessing the urban thermal signal when SUHII and SUHIF diverge. The proposed method and framework contribute to standardizing the quantification of the SUHI effect.

Indicator↗
DOE OSTI2026

Employing Machine Learning for New Particle Formation Identification and Mechanistic Analysis: Insights From a Six‐Year Observational Study in the Southern Great Plains

We present a supervised machine learning (ML) framework to automatically identify new particle formation (NPF) events and analyze key atmospheric factors associated with their occurrence and growth. We applied ML to detect NPF events using start time and particle concentrations across size ranges, while identifying atmospheric variables including ambient temperature, relative humidity, solar radiation intensity (SRI), wind speed, wind direction, boundary layer height, total organics, sulfate, nitrate, total surface area concentration, sulfur dioxide, and turbulent kinetic energy (TKE). We analyzed a 6-year data set from the Atmospheric Radiation Measurement at the Southern Great Plains (SGP) site in Oklahoma, USA. Using long-term ground-based measurements, we identified NPF events and applied Random Forest Classifiers, which achieved 90%–95% prediction accuracy. Feature importance analysis highlighted SRI, relative humidity, and ambient temperature as the most influential variables, contributing normalized importances of 28%, 17%, and 10%. Partial Dependence Plots (PDPs) indicated that higher SRI and lower relative humidity were critical in promoting NPF formation at SGP. Seasonally, NPF events were more frequent in winter (42.1%) and spring (35.5%), and least in summer (4.0%). Particle growth rates also exhibited a seasonal variation, with the lowest in winter (below 2 nm hr −1 ) and highest in late spring and early summer (exceeding 5 nm hr −1 ). Temperature, turbulent kinetic energy, and aerosol properties were the primary factors of growth rate variability. This study advances predictive modeling of NPF, offers insights for future campaign deployments, and demonstrates the effectiveness of ML in understanding the formation and growth of atmospheric aerosols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

U.S. Renewables Portfolio Standards 2021 Status Update: Early Release

Berkeley Lab’s annual status report on U.S. renewables portfolio standards (RPS) provides an overview of key trends associated with U.S. state RPS policies. The report, published in slide-deck form, describes recent legislative revisions, key policy design features, compliance with interim targets, past and projected impacts on renewables development, and compliance costs. The 2021 Early Release, published in lieu of a 2020 edition, presents historical data through year-end 2019, with some limited results for the year 2020. Key trends from this edition of the report include the following: Evolution of state RPS programs: States continue to refine and revise their RPS policies. Among other significant changes since the start of 2019, eight states enacted higher RPS targets or created new clean-energy/zero-carbon targets (AZ, DC, MD, NM, NV, VA, WA), in most cases setting targets equal to at least 50% of retail sales. Historical impacts on renewables development: Roughly half of all growth in U.S. renewable electricity (RE) generation and capacity since 2000 is associated with state RPS requirements, though that percentage has declined in recent years, representing 23% of all U.S. RE capacity additions in 2019. However, within particular regions-namely, the Northeast and Mid-Atlantic-RPS policies continue to serve a central role in motivating RE growth. Future RPS demand and incremental needs: RPS demand growth through 2030 will require roughly 90 GW of new RE capacity and will require total U.S. non-hydro RE generation to reach 17% of electricity sales (compared to 12% in 2019). Relative to EIA projections, this amounts to roughly one-third of projected RE growth over the next decade. RPS target achievement to-date: States have generally met their interim RPS targets in recent years, with only a few exceptions reflecting unique, state-specific issues. REC pricing trends: Prices for NEPOOL Class I RECs rose steeply over 2019, reaching $40/MWh and remaining at roughly that level over 2020. PJM Tier I REC prices continued to rise at a modest pace over the course of 2020, reaching $10/MWh by year-end. Prices for solar RECs remained relatively stable over 2020, and continue to exhibit wide variation across states, with the highest prices ($200-450/MWh) in NJ, MA, and DC.RPS compliance costs and cost caps: RPS compliance costs in 2019 averaged roughly 2.6% of retail electricity bills in RPS states, compared to 2.3% in 2018, with costs in most states ranging from 0.5% to 4.5% of retail electricity bills.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Piston Bowl Geometry Effects on Gasoline Compression Ignition in a Heavy-Duty Diesel Engine

A design optimization campaign was conducted to search for improved combustion profiles that enhance gasoline compression ignition in a heavy-duty diesel engine with a geometric compression ratio of 17.3. A large-scale design of experiments approach was used for the optimization, employing three-dimensional computational fluid dynamics simulations. The main parameters explored include geometric features, injector specifications, and swirl motion. Both stepped-lip and re-entrant bowls were included in order to assess their respective performance implications. A total of 256 design candidates were prepared using the software package CAESES for automated and simultaneous geometry generation and combustion recipe perturbation. The design optimization was conducted for three engine loads representing light to medium load conditions. The design candidates were evaluated for fuel efficiency, emissions, fuel-air mixing, and global combustion behavior. Simulation results showed that the optimum designs were all stepped-lip bowls, due to improvements in fuel-air mixing, as well as reduced heat loss and emissions formation. Improvements in indicated specific fuel consumption of up to 3.2% were achieved while meeting engine-out NOx emission targets of 1-1.5 g/kW center dot h. Re-entrant bowls performed worse compared to the baseline design, and significant performance variations occurred across the load points. Specifically, the re-entrant bowls were on par with the stepped-lip bowls under light load conditions, but significant deteriorations occurred under higher load conditions. As a final task, selected optimized designs were then evaluated under full-load conditions.

CFD↗
DOE OSTI2020

Carbon budget of the Harvard Forest Long-Term Ecological Research site: pattern, process, and response to global change

How, where, and why carbon (C) moves into and out of an ecosystem through time are long-standing questions in biogeochemistry. Here, we bring together hundreds of thousands of C-cycle observations at the Harvard Forest in central Massachusetts, USA, a mid-latitude landscape dominated by 80–120-yr-old closed-canopy forests. These data answered four questions: (1) where and how much C is presently stored in dominant forest types; (2) what are current rates of C accrual and loss; (3) what biotic and abiotic factors contribute to variability in these rates; and (4) how has climate change affected the forest’s C cycle? Harvard Forest is an active C sink resulting from forest regrowth following land abandonment. Soil and tree biomass comprise nearly equal portions of existing C stocks. Net primary production (NPP) averaged 680–750 g C·m –2 ·yr –1 ; belowground NPP contributed 38–47% of the total, but with large uncertainty. Mineral soil C measured in the same inventory plots in 1992 and 2013 was too heterogeneous to detect change in soil-C pools; however, radiocarbon data suggest a small but persistent sink of 10–30 g C·m –2 ·yr –1 . Net ecosystem production (NEP) in hardwood stands averaged ~300 g C·m –2 ·yr –1 . NEP in hemlock-dominated forests averaged ~450 g C·m –2 ·yr –1 until infestation by the hemlock woolly adelgid turned these stands into a net C source. Since 2000, NPP has increased by 26%. For the period 1992–2015, NEP increased 93%. The increase in mean annual temperature and growing season length alone accounted for ~30% of the increase in productivity. Interannual variations in GPP and NEP were also correlated with increases in red oak biomass, forest leaf area, and canopy-scale light-use efficiency. Compared to long-term global change experiments at the Harvard Forest, the C sink in regrowing biomass equaled or exceeded C cycle modifications imposed by soil warming, N saturation, and hemlock removal. Furthermore, the results of this synthesis and comparison to simulation models suggest that forests across the region are likely to accrue C for decades to come but may be disrupted if the frequency or severity of biotic and abiotic disturbances increases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Deep groundwater↗
DOE OSTI2025

Cross-Code Verification of Neutronics Analysis Tools at INL Applied for 238 Pu Production in the Advanced Test Reactor

Here, analyses are completed for experiments prior to experiment irradiation in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). Various codes are used to qualify all experiments planned for insertion in the reactor, thereby ensuring that all safety and programmatic requirements are satisfied preirradiation. Among the common experiment analysis tools at INL are MCNP5 coupled to ORIGEN2 (MOPY) and MC21. MOPY uses MCNP5 for transport calculations along with calculations for fluxes and select reaction rates, and then ORIGEN2 handles the step-by-step and postirradiation depletion. MC21 handles all in-reactor transport and step-by-step, during-irradiation, depletion calculations, and then ORIGEN (SCALE 6.2.3) is used for decay and dose calculations postirradiation. The MOPY results, along with those obtained via two variations of the MC21 model, were compared in terms of 238 Pu production in the ATR’s H10 position. For the MOPY model, the MC21 model utilizing the HELIOS-based fission product (FP) library, and the MC21 model utilizing the expanded 1300 FP library, the during-cycle irradiation in-core heating results were sufficiently equivalent; however, the MOPY model and the MC21 model with the HELIOS library showed some differences relating to the respective FP libraries. Ultimately, the MC21 model with a 1300 FP library produced the most consistent results throughout the cycle, whereas the MC21 model that utilized the (smaller) HELIOS library was able to handle during-irradiation analysis but lacked certain short-lived FPs that significantly contributed to the total decay heat at shutdown. MOPY, on the other hand, was found to overpredict fission gas production, as a result of limitations in the ORIGEN2 code.

ATR↗
DOE OSTI2020

Feature‐shared adaptive‐boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images

Purpose In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules. These nodules can be classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). The automatic determination of a nodule's invasiveness based on chest CT scans can guide treatment planning. However, it is challenging, owing to the insufficiency of training data and their interclass similarity and intraclass variation. To address these challenges, we propose a two‐stage deep learning strategy for this task: prior‐feature learning followed by adaptive‐boost deep learning. Methods The adaptive‐boost deep learning is proposed to train a strong classifier for invasiveness classification of subsolid nodules in chest CT images, using multiple 3D convolutional neural network (CNN)‐based weak classifiers. Because ensembles of multiple deep 3D CNN models have a huge number of parameters and require large computing resources along with more training and testing time, the prior‐feature learning is proposed to reduce the computations by sharing the CNN layers between all weak classifiers. Using this strategy, all weak classifiers can be integrated into a single network. Results Tenfold cross validation of binary classification was conducted on a total of 1357 nodules, including 765 noninvasive (AAH and AIS) and 592 invasive nodules (MIA and IAC). Ablation experimental results indicated that the proposed binary classifier achieved an accuracy of with an AUC of 81.3 . These results are superior compared to those achieved by three experienced chest imaging specialists who achieved an accuracy of , , and , respectively. About 200 additional nodules were also collected. These nodules covered 50 cases for each category (AAH, AIS, MIA, and IAC, respectively). Both binary and multiple classifications were performed on these data and the results demonstrated that the proposed method definitely achieves better performance than the performance achieved by nonensemble deep learning methods. Conclusions It can be concluded that the proposed adaptive‐boost deep learning can significantly improve the performance of invasiveness classification of pulmonary subsolid nodules in CT images, while the prior‐feature learning can significantly reduce the total size of deep models. The promising results on clinical data show that the trained models can be used as an effective lung cancer screening tool in hospitals. Moreover, the proposed strategy can be easily extended to other similar classification tasks in 3D medical images.

Wang, Jun↗
DOE OSTI2023

Bi-Level Linear Programming Model for Automatic Load Shedding: A Distributed Wide-Area Measurement System-based Solution

Load shedding is currently implemented as a two-step based approach. In the first step, manual load shedding is taken place, were system operators, using estimates, inform distribution utilities of predicted stressful conditions. Information provided include the potential use of energy reserves, as well as load shedding amount. In a second step, automatic load shedding is done. The latter is realized using protection relays. While considering frequency variation, pre-defined values of load to be shed and correspondent number of stages for such to be realized are transformed into relay settings. Under-frequency protection relays use only local measurements towards decision making, thus operate in a decentralized architecture. Decision making is done in milliseconds plus breaker time. While this approach has provided much system reliability, considering the new smart grid paradigm, where system dynamics are much faster due to increasing renewable resources penetration, in some operating conditions it will generate sub-optimal solutions, such as islanding. Phasor measurement units provide a source of information which can be useful for this problem. Centralized architecture-based solutions for automatic load shedding, as present in the state-of-the-art, require though total processing times which are not acceptable for real-life implementation. In this work, considering the above, a bi-level linear programming model is presented. The model is implemented considering a distributed architecture while leveraging phasor measurement units data. The upper-level model estimates the current system state. Results of this model are embedded in a lower-level model, which decision variables are the location and load value to be shed. Easy-to-implement model, built-on the classic weighted least squares solution, highlight potential aspects towards real-life applications.

Bretas, Arturo Suman↗
DOE OSTI2021

The Transcriptional Response of Soil Bacteria to Long-Term Warming and Short-Term Seasonal Fluctuations in a Terrestrial Forest

Terrestrial ecosystems are an important carbon store, and this carbon is vulnerable to microbial degradation with climate warming. After 30 years of experimental warming, carbon stocks in a temperate mixed deciduous forest were observed to be reduced by 30% in the heated plots relative to the controls. In addition, soil respiration was seasonal, as was the warming treatment effect. We therefore hypothesized that long-term warming will have higher expressions of genes related to carbohydrate and lipid metabolism due to increased utilization of recalcitrant carbon pools compared to controls. Because of the seasonal effect of soil respiration and the warming treatment, we further hypothesized that these patterns will be seasonal. We used RNA sequencing to show how the microbial community responds to long-term warming (~30 years) in Harvard Forest, MA. Total RNA was extracted from mineral and organic soil types from two treatment plots (+5°C heated and ambient control), at two time points (June and October) and sequenced using Illumina NextSeq technology. Treatment had a larger effect size on KEGG annotated transcripts than on CAZymes, while soil types more strongly affected CAZymes than KEGG annotated transcripts, though effect sizes overall were small. Although, warming showed a small effect on overall CAZymes expression, several carbohydrate-associated enzymes showed increased expression in heated soils (~68% of all differentially expressed transcripts). Further, exploratory analysis using an unconstrained method showed increased abundances of enzymes related to polysaccharide and lipid metabolism and decomposition in heated soils. Compared to long-term warming, we detected a relatively small effect of seasonal variation on community gene expression. Together, these results indicate that the higher carbohydrate degrading potential of bacteria in heated plots can possibly accelerate a self-reinforcing carbon cycle-temperature feedback in a warming climate.

54 ENVIRONMENTAL SCIENCES↗