Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids
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14 SOLAR ENERGY↗
SEARCH · Engineering Papers
Results for “spatial analysis”
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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OpenCRUMS USA: An Open Machine Learning Framework for Characterizing Variability in Aerosol Reanalysis Data
Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.
54 ENVIRONMENTAL SCIENCES↗
Multimodal Tip-Enhanced Nonlinear Optical Nanoimaging of Plasmonic Silver Nanocubes
Optical field localization at plasmonic tip-sample nanojunctions has enabled high spatial resolution chemical analysis through tip-enhanced linear optical spectroscopies, including Raman scattering and photoluminescence. In this work, we illustrate that nonlinear optical processes, including parametric four-wave mixing (4WM), second harmonic/sum-frequency generation (SHG and SFG), and two-photon photoluminescence (TPPL), can be enhanced at plasmonic junctions and spatio-spectrally resolved simultaneously with few-nm spatial resolution under ambient conditions. More importantly, through a detailed analysis of our spectral nano-images, we find that the efficiencies of the local nonlinear signals are determined by sharp tip-sample junction resonances that vary over the few-nanometer length scale because of the corrugated nature of the probe. Namely, plasmon resonances centered at or around the different nonlinear signals are tracked through TPPL, and they are found to selectively enhance nonlinear signals with closely matched optical resonances.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Nanomechanical sampling of material for nanoscale mass spectrometry chemical analysis
The ability to spatially resolve the chemical distribution of compounds on a surface is important in many applications ranging from biological to material science. To this extent, we have recently introduced a hybrid atomic force microscopy (AFM)-mass spectrometry (MS) system for direct thermal desorption and pyrolysis of material with nanoscale chemical resolution. However, spatially resolved direct surface heating using local thermal desorption becomes challenging on material surfaces with low melting points, because the material will undergo a melting phase transition due to heat dissipation prior to onset of thermal desorption. Therefore, here we developed an approach using mechanical sampling and collection of surface materials on an AFM cantilever probe tip for real-time analysis directly from the AFM tip. This approach allows for material to be concentrated directly onto the probe for subsequent MS analysis. We evaluate the performance metrics of the technique and demonstrate localized MS sampling from a candelilla wax matrix containing UV stabilizers avobenzone and oxinoxate from areas down to 250 nm × 250 nm. Overall, this approach removes heat dissipation into the bulk material allowing for a faster desorption and concentration of the gas phase analyte from a single heating pulse enabling higher signal levels from a given amount of material in a single sampling spot.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
rmap: An R package to plot and compare tabular data on customizable maps across scenarios and time
`rmap` is an R package that allows users to easily plot tabular data (CSV or R data frames) on maps without any Geographic Information Systems (GIS) knowledge. Maps produced by `rmap` are `ggplot` objects and thus capitalize on the flexibility and advancements of the `ggplot2` package and all elements of each map are thus fully customizable. Additionally `rmap` automatically detects and produces comparison maps if the data has multiple scenarios or time periods as well as animations for time series data. Advanced users can load their own shapefiles if desired. `rmap` comes with a range of pre-built color palettes but users can also provide any `R` color palette or create their own as needed. Four different legend types are available to highlight different kinds of data distributions. The input spatial data can be both gridded or polygon data. `rmap` is desgined in particular for comparing spatial data across scenarios and time periods and comes preloaded with standard country, state, and basin maps as well as custom maps compatible with the Global Change Analysis Model (GCAM) spatial boundaries. `rmap` has a growing number of users and its products have been used in multiple multisector dynamics publications as well as a required dependency in other R packages such as `rfasst` and `metis`. `rmap's` automatic processing of tabular data using pre-built map selection, difference map calculations, faceting, and animations offers unique functionality which makes it a powerful and yet simple tool for users looking to explore multi-sector, multi-scenario data across space and time.
58 GEOSCIENCES↗
Daylight-driven carbon exchange through a vertically structured microbial community
Interactions between autotrophs and heterotrophs are central to carbon (C) exchange across trophic levels in essentially all ecosystems and metabolite exchange is a frequent mechanism for distributing C within spatially structured ecosystems. Yet, despite the importance of C exchange, the timescales at which fixed C is transferred in microbial communities is poorly understood. We employed a stable isotope tracer combined with spatially resolved isotope analysis to quantify photoautotrophic uptake of bicarbonate and track subsequent exchanges across a vertical depth gradient in a stratified microbial mat over a light-driven diel cycle. We observed that C mobility, both across the vertical strata and between taxa, was highest during periods of active photoautotrophy. Parallel experiments with 13 C-labeled organic substrates (acetate and glucose) showed comparably less exchange of C within the mat. Metabolite analysis showed rapid incorporation of 13 C into molecules that can both comprise a portion of the extracellular polymeric substances in the system and serve to transport C between photoautotrophs and heterotrophs. Stable isotope proteomic analysis revealed rapid C exchange between cyanobacterial and associated heterotrophic community members during the day with decreased exchange at night. We observed strong diel control on the spatial exchange of freshly fixed C within tightly interacting mat communities suggesting a rapid redistribution, both spatially and taxonomically, primarily during daylight periods.
59 BASIC BIOLOGICAL SCIENCES↗
Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”
The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.
54 ENVIRONMENTAL SCIENCES↗
Three-Dimensional Imaging Lidar for Characterizing Particle Fields and Organisms in the Mesopelagic Zone
The ocean’s mesopelagic zone is largely uncharacterized despite its vital role in sustaining ocean ecosystems. The composition, cycling, and fate of particle fields in the mesopelagic lacks an integrative multi-scale understanding of organism migration patterns, distribution, and diversity. This problem is addressed by combining complementary technologies with overlapping size spectra, including profiler mounted optical scattering sensors, profiler, and ship mounted acoustic devices, and a custom Unobtrusive Multi-Static Lidar Imager (UMSLI). This unique sensor suite can observe distributions of particles including organisms over a six order of magnitude dynamic size range, from microns to meters. Overlapping size ranges between different methods allows for cross-validation. This work focuses on the lidar imaging measurements and optical backscattering and attenuation, covering a combined particle size range of 0.1 mm to several cm. Particles at the small end of this range are sized using an existing backscattering time series inversion method after Briggs et al. (2013). Larger particles are resolved with UMSLI over an expanding volume using three-dimensional photo-realistic laser serial imaging. UMSLI’s image rectifying ability over time allows for derivation of particle concentration, size, and spatial distribution. Technical details on the development and post-processing methods for the novel UMSLI system are provided. Image resolved particle size distributions (PSDs) revealed a size shift from smaller to larger particles (>0.5 mm) as indicated by flatter slopes from dawn (slope = 2.6) to dusk (slope = 3.0). PSD trends are supported by an optical backscatter and transmissometer time series inversion analysis. Size shifts in the particle field are largely attributed to aggregation effects. Images support evidence of temporal variation between dusk and dawn stations through statistical analysis of particle concentrations for particle sizes 0.50–5.41 mm. Spatial analysis of the particle field revealed a dominantly uniform distributed marine snow background. The importance and potential of integrated approaches to studying particle and organism dynamics in ocean environments are discussed.
54 ENVIRONMENTAL SCIENCES↗
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information
Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
60 APPLIED LIFE SCIENCES↗
How much information on precipitation is contained in satellite infrared imagery?
Satellite infrared (IR) brightness temperature providing information on the characteristics of cloud tops is one of the primary input data used for estimating precipitation from satellites. Thus, the natural questions to ask are: Does IR imagery contain more information about precipitation in some regions more than others? What are the factors that lead to such differences? Should we expect the amount of information provided by IR imagery to change with respect to temporal or spatial aggregation? Comprehensive analysis of the accuracy of satellite-based precipitation estimates obtained from any given algorithm may provide qualitative answers to the above questions. However, such an approach only offers conclusions within the limited context of the algorithm used to obtain precipitation estimates; that is, patterns caused by the inherent information content of IR imagery are inextricable from those induced by assumptions embedded in the precipitation estimation algorithm. Here, we address the abovementioned questions from a different perspective using an information-theoretic measure, free of structural assumptions and general to a wide range of relationships, to characterize the average and seasonal dependence between IR imagery provided by satellite sensors in the spectral range (10.7–11.5 μm) and precipitation rates in the contiguous United States (CONUS) across distinct temporal and spatial scales. We analyze a total of more than 1.3 billion pairs of IR and precipitation observations over CONUS, and we observe interesting patterns. First, we show that there is a strong inverse relationship (ρ = - 0.73) between the information content of IR data and the number of no-rain observations; however, its robustness varies regionally and seasonally with less significant correlation during the warm season. Second, we demonstrate that the intuitive relationship of increasing dependence between IR and precipitation as a result of temporal or spatial aggregation exhibits a diminishing returns behavior. For instance, temporal aggregation from 1 to 3 h increases the dependence approximately 7 times as much as temporal aggregation from 12 to 24 h. Furthermore, we quantitatively examine and re-confirm several statements previously reported in the literature such as the strong association between IR and precipitation in convective storms and the low association in orographic rainfall. Finally, we conclude by illustrating the potential of the analysis in diagnosis of operational algorithms for estimating precipitation from IR brightness temperature. The findings of this study pinpoint spatial domains and time scales at which IR is not an adequate proxy for estimating precipitation. Thus, they can potentially guide the development of operational algorithms that utilize satellite Infrared imagery for estimating, downscaling and data fusion of precipitation. Finally, the analysis presented here opens up the possibility of developing new methodologies for diagnosis of satellite-based precipitation estimation algorithms.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem
Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. A systematic evaluation of the interaction between coastal catchment properties and carbon dioxide (CO2) emission by soil respiration is significant for assessing carbon dynamics and predicting the future trajectory of atmospheric CO2 concentrations in coastal TAIs. The soil CO2 efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation index (kNDVI), climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, there is no one-size-fits-all approach to modeling carbon fluxes in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.
54 ENVIRONMENTAL SCIENCES↗
Multiscale analysis of remotely sensed imagery to quantify spatial and temporal patterns of river bank erosion in floodplains with permafrost
Explore the source record for details and available documents.
Earth Sciences↗
Correlated mechanochemical maps of Arabidopsis thaliana primary cell walls using atomic force microscope infrared spectroscopy
Spatial heterogeneity in composition and organisation of the primary cell wall affects the mechanics of cellular morphogenesis. However, directly correlating cell wall composition, organisation and mechanics has been challenging. To overcome this barrier, we applied atomic force microscopy coupled with infrared (AFM-IR) spectroscopy to generate spatially correlated maps of chemical and mechanical properties for paraformaldehyde-fixed, intact Arabidopsis thaliana epidermal cell walls. AFM-IR spectra were deconvoluted by non-negative matrix factorisation (NMF) into a linear combination of IR spectral factors representing sets of chemical groups comprising different cell wall components. This approach enables quantification of chemical composition from IR spectral signatures and visualisation of chemical heterogeneity at nanometer resolution. Cross-correlation analysis of the spatial distribution of NMFs and mechanical properties suggests that the carbohydrate composition of cell wall junctions correlates with increased local stiffness. Together, our work establishes new methodology to use AFM-IR for the mechanochemical analysis of intact plant primary cell walls.
59 BASIC BIOLOGICAL SCIENCES↗
Neutron imaging the spatial progression of chemical oxidation of lithium-ion cathode material in a packed bed reactor
Emerging electrochemical systems relevant to energy applications including redox targeted flow batteries rely on chemical redox of solid electroactive materials using dissolved redox couples. One configuration to facilitate contact between the redox shuttles and solid material which is volume efficient is a packed bed reactor. While methods have been reported to assess the overall progression by analysis of the packed bed reactor effluent, herein analysis of the spatial progression of the chemical redox will be reported. Combination of neutron and x-ray tomography enabled assessing the pore and particle structure in the packed bed reactor and the spatial homogeneity of the reaction at different overall extents of conversion of the reactor bed. Finally, these characterization tools provide methods to probe the chemical redox processes occurring within the reactor environment.
25 ENERGY STORAGE↗
Designing a GIS-based supply chain for producing carinata-based sustainable aviation fuel in Georgia, USA
Carinata is a potential crop for sustainable aviation fuel (SAF) production in the southern USA. However, as a novel crop, the cost-effectiveness and environmental feasibility of carinata feedstock are unknown, and there are questions about the optimal supply chain configuration for carinata-based SAF production. This study aims to design a supply chain model for carinata-based SAF production by optimizing the location of farms and facilities (e.g. storage units, crushing mills, biorefineries) for a minimum transportation cost under a set of supply and demand conditions. An integrated mixed-integer linear programming (MILP) model was combined with geographical information system (GIS) analysis to design a spatially explicit supply chain configuration. The GIS-based network analysis considered all of the counties in Georgia to set the candidate locations of carinata farms and facilities, and determined minimum cost and emission routes between those counties and the airport using existing transportation networks and modes (e.g. road, rail and pipeline). The MILP model determined the final selection of the farms and the number of facilities and their locations over those minimum-cost routes. With this supply chain configuration, the minimum price of SAF was $\$$0.92 L –1 , which is $\$$0.44 higher than conventional aviation fuel (CAF). The associated carbon intensity of SAF was estimated at 940.7 g CO 2 e L –1 , a reduction of 66% relative to the carbon intensity of equivalent CAF. The study found that a carbon tax (or subsidy) of $\$$230.48 t CO 2 e –1 would be needed to overcome the cost differential with CAF and promote carinata-based SAF in Georgia.
09 BIOMASS FUELS↗
Exploring constituent redistribution in irradiated U-19Pu-14Zr fuel via electron probe microanalysis
Here, the phenomena of constituent redistribution, wherein a previously homogeneous metallic fuel forms discrete, radially concentric compositional zones upon irradiation was investigated by examining an irradiated U-19Pu-14Zr fuel (where numbers represent wt. %) with a burnup of 11.5 at.% with electron probe microanalysis (EPMA) and quadruple inductively coupled plasma mass spectroscopy (Q-ICP-MS). EPMA-generated U, Pu, and Zr compositional data obtained from a diameter traverse of the sample was converted to mass and was used to: 1) compare the overall fuel element analysis results between the two methods, 2) determine the number of compositionally distinct zones forming as a result of constituent redistribution; and 3) quantify the post-irradiation loss or gain of U, Pu, and Zr atoms in each distinct compositional zone. Weight percent concentrations of U, Pu, and Zr for the overall cross section compare favorably between the two analytical methods, suggesting that the spatially resolved EPMA analysis complements bulk chemical analysis. Among the four identified compositional zones, post-irradiation quantification of U, Pu, and Zr elemental atom content changes shows that the quantity of U atoms lost from the innermost zone is slightly less than the quantity of U atoms gained by the middle two zones, and the quantity of Zr atoms lost from the high-U third zone is slightly less than is gained by the two innermost zones. Pu is lost from all four zones, although the innermost zone and the high-U third zone lose a significantly higher percentage (> 22 %) of their initial Pu atoms than the other two zones. For all three elements, EPMA cannot distinguish between atoms lost due to transport to a different zone from atoms lost due to nuclear processes; however, the insight gained from using this process can be used to experiment with new modeling techniques to predict constituent redistribution in U-Pu-Zr fuels.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
MaNGA galaxy properties – II. A detailed comparison of observed and simulated spiral galaxy scaling relations
ABSTRACT We present a catalogue of dynamical properties for 2368 late-type galaxies from the MaNGA survey. The latter complements the catalogue of photometric properties for the same sample based on deep optical dark energy sky instrument legacy imaging survey (DESI) photometry processed with autoprof . Rotation curves (RCs), extracted by model-fitting H α velocity maps from the MaNGA Data Analysis Pipeline, extend out to 1.4 (1.9) Re for the primary (secondary) MaNGA samples, respectively. The RCs and ancillary MaNGA Pipe3D data products were used to construct various fundamental galaxy scaling relations that are also compared uniformly with similar relations from numerical investigation of a hundred astrophysical object (NIHAO) zoom-in simulations. Simulated NIHAO galaxies were found to broadly reproduce the observed MaNGA galaxy population for $\log (M_*/{\rm {\rm M}_{\odot })\, \gt\, 8.5}$. Some discrepancies remain, such as those pertaining to central stellar densities and the diversity of RCs due to strong feedback schemes. Also presented are spatially resolved scatters for the velocity-size-stellar mass (VRM*) structural relations using MaNGA and NIHAO samples. The scatter for these relations in the galaxian interiors is a consequence of the diversity of inner RC shapes, while scatter in the outskirts is dictated by the large range of stellar surface densities, which is itself driven by sporadic star formation. The detailed, spatially resolved scatter analysis highlights the complex interplay between local and global astrophysical processes and provides a strong constraint to numerical simulations.
Astronomy & Astrophysics↗
Influence of landscape attributes on Virginia opossum density
Abstract The Virginia opossum ( Didelphis virginiana ), North America's only marsupial, has a range extending from southern Ontario, Canada, to the Yucatan Peninsula, Mexico, and from the Atlantic seaboard to the Pacific. Despite the Virginia opossum's taxonomic uniqueness in relation to other mammals in North America and rapidly expanding distribution, its ecology remains relatively understudied. Our poor understanding of the ecology of this important mesopredator is especially pronounced in the rural southeastern United States. Our goal was to estimate effects of habitat on opossum density within an extensive multi‐year spatial capture‐recapture study. Additionally, we compared the results of this spatial capture‐recapture analysis with a simple relative abundance index. Opossum densities in the relatively underdeveloped regions of the southeastern United States were lower compared to the more human‐dominated landscapes of the Northeast and Midwest. In the southeastern United States, Virginia opossums occurred at a higher density in bottomland swamp and riparian hardwood forest compared to upland pine ( Pinus spp.) plantations and isolated wetlands. These results reinforce the notion that the Virginia opossum is commonly associated with land cover types adjacent to permanent water (bottomland swamps, riparian hardwood). The relatively low density of opossums at isolated wetland sites suggests that the large spatial scale of selection demonstrated by opossums gives the species access to preferable cover types within the same landscape.
54 ENVIRONMENTAL SCIENCES↗