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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

WRF Output from 270m domain running simulation with no 3D morphology, NUDAPT 3D morphology, 100m resolution 3D morphology and 10m resolution 3D morphology

This is a group of four datasets that were run for an experiment testing the effect of the resolution and the coverage of 3D urban morphological inputs on meteorological output. This data is associated with the paper "Sensitivity of Urban Morphology to the Resolution of Urban Morphological Feature Inputs: Implications for Characterizing Urban Sustainability."

integrated multisector multiscale modeling↗
DOE OSTI2024

WRF Output from 270m domain running simulation with no 3D morphology, NUDAPT 3D morphology, 100m resolution 3D morphology and 10m resolution 3D morphology

This is a group of four datasets that were run for an experiment testing the effect of the resolution and the coverage of 3D urban morphological inputs on meteorological output. This data is associated with the paper "Sensitivity of Urban Morphology to the Resolution of Urban Morphological Feature Inputs: Implications for Characterizing Urban Sustainability."

integrated multisector multiscale modeling↗
DOE OSTI2025

Projected Urban Morphology of the Los Angeles Area by the Year 2100

This dataset provides projections of urban building morphologies for the Los Angeles urban area at 30-meter spatial resolution. It contains 192 raster files that detail two primary building attributes: building footprint fractions (ranging from 0 to 1) and average building heights (ranging from 0 to 75 meters). The projections account for a wide range of future pathways, covering two Shared Socioeconomic Pathway (SSP) scenarios (SSP3 and SSP5), two population scenarios, two developed land intensification scenarios, and four distinct levels of intensification. The dataset was created using dual Generative Adversarial Networks (GANs) trained on 2015 land cover and building properties from the National Land Cover Database (NLCD) and Model America datasets. Supporting information on the dataset has been described in the LAUrbanAreaMorphologyProjections2100_README.txt file.

Pandey, Bhartendu↗
DOE OSTI2021

DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology

Abstract The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual and time-consuming, and require specific expertise. Deep learning has the potential to revolutionize image-based screens by greatly improving their scope, speed, and efficiency. Here, we used transfer learning and a convolutional neural network (CNN) to analyze over 47,000 confocal microscopy images from Arabidopsis wild-type and mutant plants with abnormal division of one of three essential energy organelles: chloroplasts, mitochondria, or peroxisomes. We have built a deep-learning framework, DeepLearnMOR (Deep Learning of the Morphology of Organelles), which can rapidly classify image categories and identify abnormalities in organelle morphology with over 97% accuracy. Feature visualization analysis identified important features used by the CNN to predict morphological abnormalities, and visual clues helped to better understand the decision-making process, thereby validating the reliability and interpretability of the neural network. This framework establishes a foundation for future larger-scale research with broader scopes and greater data set diversity and heterogeneity.

Plant Sciences↗
DOE OSTI2022

Quantitative Morphological Characterization of Carbide Inclusions in Uranium Metal

Uranium carbides (UCs) are prevalent inclusions in U metal that form during melting operations from interactions with crucible walls and the casting chamber atmosphere. Although UCs have been studied extensively since the beginning of U metal foundry operations, there are still unknowns regarding the effects of thermomechanical processing on their sizes and morphology. Here, we present the results of a series of controlled cooling experiments with molten uranium to elucidate the effect of cooling rate on inclusion morphology in a-U. Samples were melted using a vacuum induction melter and manually cooled at rates of 2.5, 1.7, 1.1, 0.8, 0.3 (± 1%) K/s from ~1600 K to <700 K in under 1 hour. Subsequent scanning electron microscopy (SEM) was performed on cross-sections of the samples, revealing a complex mixture of UC morphologies that are indicative of diffusion and growth influenced by the thermal processing of the U matrix. Image analysis using the Morphological Analysis of Materials (MAMA) software showed that UC sizes generally grew larger with slower cooling rates, and the two slowest cooling rates noticeably impacted the inclusion circularity and ellipse aspect ratio. These results indicate that UC morphology is sensitive to short cooling rates (<1 hour) and could therefore be controlled in the production of metallic nuclear fuels. Additionally, inclusion speciation and morphologies could potentially provide forensic clues about processing history of unknown metal samples. Understanding the driving forces involved in UC morphology evolution is beneficial for evaluating metal fuels for next generation nuclear reactors and for identifying signatures for nuclear forensics.

Athon, Matthew T.↗
DOE OSTI2020

Confinement and Processing Can Alter the Morphology and Periodicity of Bottlebrush Block Copolymers in Thin Films

Bottlebrush block copolymers (BBCPs) are intriguing architectural variations on linear BCPs with highly tunable structure. Confinement can have a significant impact on polymer assembly, giving rise to changes in morphology, assembly kinetics, and properties like the glass transition. Given that confinement leads to significant changes in the persistence length of bottlebrush homopolymers, it is reasonable to expect that BBCPs will see significant changes in their structure and periodicity relative to the bulk morphology. Understanding how confinement influences assembly will be important for designing BBCPs for thin film applications including membranes, integrated photonic structures, and potentially BCP lithography. In this work, in order to study the effects of confinement on BBCP conformation and morphology, a blade coating was used to prepare films with continuous variation in film thickness. Unlike thin films of linear BCPs, islands/holes were not observed, and instead mixtures of parallel and perpendicular morphologies emerge after annealing. The lamellar periodicity ($L_0$) of the morphologies is found to be thickness dependent, increasing $L_0$ with decreasing film thickness for blade coated films. Films coated out of tetrahydrofuran (THF) resulted in a single well-defined lamellar periodicity, verified through atomic force microscopy (AFM) and grazing incidence small-angle X-ray scattering (GISAXS), which increases dramatically from the bulk value (30.6 nm) and continues to increase as the film thickness decreases. The largest observed $L_0$ was 65.5 nm, and this closely approaches the estimated upper limit of 67 nm corresponding to a fully extended backbone in a bilayer arrangement. Films coated out of propylene glycol methyl ether acetate (PGMEA) resulted in a mixture of perpendicular lamellae and a smaller, likely cylindrical morphology. The lamellar portion of the film shows the same thickness dependence as the lamellae observed in the THF coated films. The scaling of the lamellar $L_0$ with respect to film thickness follows predictions for confined semiflexible polymers with weak excluded volume interactions and can be related to models for confinement of DNA. Spin coated films shows the same reduction in periodicity, although at very different film thicknesses. This result suggests that the material has shallow free-energy barriers to transitioning between different $L_0$ and morphologies, a property that could be taken advantage of for patterning diverse structures with a single material.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Supramolecular Interactions and Morphology of Self-Assembling Peptide Amphiphile Nanostructures

The morphology of supramolecular peptide nanostructures is difficult to predict given their complex energy landscapes. We investigated peptide amphiphiles containing β-sheet forming domains that form twisted nanoribbons in water. We explained the morphology based on a balance between the energetically favorable packing of molecules in the center of the nanostructures, the unfavorable packing at the edges, and the deformations due to packing of twisted β-sheets. We find that morphological polydispersity of PA nanostructures is determined by peptide sequences, and the twisting of their internal β-sheets. Here, we also observed a change in the supramolecular chirality of the nanostructures as the peptide sequence was modified, although only amino acids with l-configuration were used. Upon increasing charge repulsion between molecules, we observed a change in morphology to long cylinders and then rodlike fragments and spherical micelles. Understanding the self-assembly mechanisms of peptide amphiphiles into nanostructures should be useful to optimize their well-known functions.

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

Urban morphology from a landscape perspective: How building morphology distribution land models (BMDLM) emulate pattern and process

Urban form (e.g., building morphology such as height or footprint) can be used to predict environmental footprints, such as energy/water consumption and carbon emissions. Although progress has been made in predicting building characteristics to fill gaps in observation or derive 3-D representations, the relationships between morphology and other variables such as land use and population are poorly understood. Understanding these relationships may enable projections for how cities will evolve with landscapes in the future. A suite of random forest models, the Building Morphology Distribution Land Models (BMDLM), was developed to determine how well building morphology for two distinct statistical measures (central tendency and frequency) can be predicted using land use (e.g., zoning) and population at different resolutions. Clark County, Nevada and Los Angeles County, California are explored as case studies. Generally, 1-km models outperformed 30-m models. Frequency distribution models had the best performance, especially in LA County. Frequency models significantly outperformed spatial autocorrelative models using inverse distance weighting (IDW). BMDLM offers a new take on modeling urban form in which generalized landscape patterns are characterized to understand the influence of population and zoning on urban development, as described by urban scaling theory.

Sturtevant, Jillian [Baylor Univ., Waco, TX (Unite↗
DOE OSTI2020

Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ~2800 galaxies with visual classification from GZ1, we reach an accuracy of ~0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ~2.5 percent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Subaru Hyper Suprime-Cam revisits the large-scale environmental dependence on galaxy morphology over 360 deg2 at z = 0.3–0.6

This study investigates the role of large-scale environments on the fraction of spiral galaxies at z = 0.3–0.6 sliced to three redshift bins of Δz = 0.1. Here, we sample 276220 massive galaxies in a limited stellar mass of 5 × 1010 solar mass (~M*) over 360 deg2, as obtained from the Second Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). By combining projected two-dimensional density information (Shimakawa et al. 2021, MNRAS, 503, 3896) and the CAMIRA cluster catalog (Oguri et al. 2018, PASJ, 70, S20), we investigate the spiral fraction across large-scale overdensities and in the vicinity of red sequence clusters. We adopt transfer learning to reduce the cost of labeling spiral galaxies significantly and then perform stacking analysis across the entire field to overcome the limitations of sample size. Here we employ a morphological classification catalog by the Galaxy Zoo Hubble (Willett et al., 2017, MNRAS, 464, 4176) to train the deep learning model. Based on 74103 sources classified as spirals, we find moderate morphology–density relations on a 10 comoving Mpc scale, thanks to the wide-field coverage of HSC-SSP. Clear deficits of spiral galaxies have also been confirmed, in and around 1136 red sequence clusters. Furthermore, we verify whether there is a large-scale environmental dependence on rest-frame u - r colors of spiral galaxies; such a tendency was not observed in our sample.

Astronomy & Astrophysics↗
DOE OSTI2024

Morphology and particle size (MaPS) exercise: testing the applications of image analysis and morphology descriptions for nuclear forensics

Image analysis techniques have been applied and shown to be a valuable tool in nuclear forensics analysis. The interlaboratory exercise reported here has tested quantitative and qualitative approaches for characterizing nuclear materials. Particle size, surface features and morphology descriptions were compared by four laboratories on a common image set generated by Scanning Electron Microscopy and Digital Light Microscopy. Quantitative analysis of the image sets through the Morphological Analysis for MAterials software highlighted the strength of image analysis, but also that the application of the software alone can introduce significant bias in the analysis. Qualitative morphology descriptions following the process outlined by Tamasi et al. (J Radioanal Nuclear Chem 307:1611–1619, 2015) were compared with a discussion on the robustness and reproducibility of the results. Finally, future work should continue to focus on proficiency and standardization of image analysis through continued exercises within the extended nuclear forensics community.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗