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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Rheological Properties of Small-Molecular Liquids at High Shear Strain Rates

Molecular-scale understanding of rheological properties of small-molecular liquids and polymers is critical to optimizing their performance in practical applications such as lubrication and hydraulic fracking. We combine nonequilibrium molecular dynamics simulations with two unsupervised machine learning methods: principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), to extract the correlation between the rheological properties and molecular structure of squalane sheared at high strain rates (10 6 –10 10 s -1 ) for which substantial shear thinning is observed under pressures P ϵ 0.1–955 MPa at 293 K. Intramolecular atom pair orientation tensors of 435 × 6 dimensions and the intermolecular atom pair orientation tensors of 61 × 6 dimensions are reduced and visualized using PCA and t-SNE to assess the changes in the orientation order during the shear thinning of squalane. Dimension reduction of intramolecular orientation tensors at low pressures P = 0.1,100 MPa reveals a strong correlation between changes in strain rate and the orientation of the side-backbone atom pairs, end-backbone atom pairs, short backbone-backbone atom pairs, and long backbone-backbone atom pairs associated with a squalane molecule. At high pressures P ≥ 400 MPa, the orientation tensors are better classified by these different pair types rather than strain rate, signaling an overall limited evolution of intramolecular orientation with changes in strain rate. Dimension reduction also finds no clear evidence of the link between shear thinning at high pressures and changes in the intermolecular orientation. The alignment of squalane molecules is found to be saturated over the entire range of rates during which squalane exhibits substantial shear thinning at high pressures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks

For turbulent reacting flow systems, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the computational cost of device-scale simulations. Principal component analysis (PCA), and its variants, are a widely employed class of methods. Recently, an alternative technique that focuses on higher-order statistical interactions, co-kurtosis PCA (CoK-PCA), has been shown to effectively provide a low-dimensional representation by capturing the stiff chemical dynamics associated with spatiotemporally localized reaction zones. While its effectiveness has only been demonstrated based on a priori analyses with linear reconstruction, in this work, we employ nonlinear techniques to reconstruct the full thermo-chemical state and evaluate the efficacy of CoK-PCA compared to PCA. Specifically, we combine a CoK-PCA-/PCA-based dimensionality reduction (encoding) with an artificial neural network (ANN) based reconstruction (decoding) and examine, a priori, the reconstruction errors of the thermo-chemical state. In addition, we evaluate the errors in species production rates and heat release rates, which are nonlinear functions of the reconstructed state, as a measure of the overall accuracy of the dimensionality reduction technique. We employ four datasets to assess CoK-PCA/PCA coupled with ANN-based reconstruction: zero-dimensional (homogeneous) reactor for autoignition of an ethylene/air mixture that has conventional single-stage ignition kinetics, a dimethyl ether (DME)/air mixture which has two-stage (low and high temperature) ignition kinetics, a one-dimensional freely propagating premixed ethylene/air laminar flame, and a two-dimensional dataset representing turbulent autoignition of ethanol in a homogeneous charge compression ignition (HCCI) engine. Finally, results from the analyses demonstrate the robustness of the CoK-PCA based low-dimensional manifold with ANN reconstruction in accurately capturing the data, specifically from the reaction zones.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2024

Development of a Data Fusion Methodology for Lineload Aerodynamic Databases for a Launch Vehicle during Liftoff and Transition

The need for databases for the distributed loading on launch vehicles during the early portion of flight necessitates the use of expensive computational flows in regimes where wake effects dominate. While also being expensive, this is a regime that computational tools tend to historically have problems simulating accurately. To help tackle this problem, a method of data fusion to combine computational results to wind tunnel derived force and moment data is developed. Using this method, significant reduction in computational costs and increases in confidence of the final product is possible and has been used to generate several databases for the Space Launch System (SLS) at NASA. While the full details of database generation are not part of this work, the crucial method at its core is developed here. Two SLS geometries are used throughout the work to demonstrate the techniques. These are two of the larger geometries and represent both planned crewed missions to the Moon as well as potential cargo missions to deep space. The method uses principal component analysis (PCA) to generate a reduced ordered model (ROM) to help fill in the full parameter space. Other similar techniques are explored, but were not found to have a significant result on the predictions of the ROM. Because the full number of components are kept to generate the model, this lack of difference is expected. This method is then extended to ensure that predicted surfaces match trusted force and moment data derived from wind tunnel testing. This extension is done by setting up a constrained optimization problem in order to minimize the deviation from the surface resolved computational data while still integrating to the desired values. When generating the constrained optimization problem, a weighting factor to balance these competing needs is introduced. The work compares previously introduced weighting terms from similar work to the proposed terms and shows that the previously used terms do not have as desirable behavior in this flow regime. This method is then expanded by developing a technique to incorporate uncertainty quantification into the developed data fusion methodology. This expansion takes a two pronged approach. One examines transferring the uncertainties in the force and moment database and characterizes how those adjustments change the predicted lineloads. The second looks at model form error and looks how rebuilding the model using slightly different data changes the predictions. These two terms are then combined in order to create an uncertainty model that takes both effects into account. The limitations of the proposed methods is then discussed as well as possible techniques to address these shortcomings.

Launch Vehicles↗
DOE OSTI2024

Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder

Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.

36 MATERIALS SCIENCE↗
DOE OSTI2023

VEESA R package

SAND2024-04584O R package for applying the VEESA pipeline method is a technique used for explainable machine learning with functional data. The VEESA pipeline makes use of the elastic-shape analysis framework for functional data. It also implements functional principal component analysis and permutation feature importance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Tucker, James↗
DOE OSTI2024

A flow cytometric assay to detect viability and persistence of Salmonella enterica subsp. enterica serotypes in nuclease-free water at 4 and 25°C

Salmonella spp. is one of the most isolated microorganisms reported to be responsible for human foodborne diseases and death. Water constitutes a major reservoir where the Salmonella spp. can persist and go undetected when present in low numbers. In this study, we assessed the viability of 12 serotypes of Salmonella enterica subsp. enterica for 160 days in nuclease-free water at 4 and 25°C using flow cytometry and Tryptic Soy Agar (TSA) plate counts. The results show that all 12 serotypes remain viable after 160 days in distilled water using flow cytometry, whereas traditional plate counts failed to detect ten serotypes incubated at 25°C. Moreover, the findings demonstrate that 4°C constitutes a more favorable environment where Salmonella can remain viable for prolonged periods without nutrients. Under such conditions, however, Salmonella exhibits a higher susceptibility to all tested antibiotics and benzalkonium chloride (BZK). The pre-enrichment with Universal Pre-enrichment Broth (UP) and 1/10 × Tryptic Soy broth (1/10 × TSB) resuscitated all tested serotypes on TSA plates, nevertheless cell size decreased after 160 days. Furthermore, phenotype microarray (PM) analysis of S. Inverness and S. Enteritidis combined with principal component analysis (PCA) revealed an inter-individual variability in serotypes with their phenotype characteristics, and the impact of long-term storage at 4 and 25°C for 160 days in nuclease-free water. This study provides an insight to Salmonella spp. long-term survivability at different temperatures and highlights the need for powerful tools to detect this microorganism to reduce the risk of disease transmission of foodborne pathogens via nuclease-free water.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
DOE OSTI2025

Multivariate Analysis as a Tool for Validating Tester Matching

A method of applying Principal Component Analysis, Soft Independent Modeling of Class Analysis, and statistical analysis is described that can be applied to many types of testers to ascertain how well matched the performance of the testers in the analysis are to one another or how well matched a tester is to itself at a later time. This method is most useful for situations for which the same units have not been run across the testers being analyzed for matched performance.

Multari, Rosalie A [Sandia National Laboratories (↗
NASA NTRS2014

Next Generation Aura-OMI SO2 Retrieval Algorithm: Introduction and Implementation Status

We introduce our next generation algorithm to retrieve SO2 using radiance measurements from the Aura Ozone Monitoring Instrument (OMI). We employ a principal component analysis technique to analyze OMI radiance spectral in 310.5-340 nm acquired over regions with no significant SO2. The resulting principal components (PCs) capture radiance variability caused by both physical processes (e.g., Rayleigh and Raman scattering, and ozone absorption) and measurement artifacts, enabling us to account for these various interferences in SO2 retrievals. By fitting these PCs along with SO2 Jacobians calculated with a radiative transfer model to OMI-measured radiance spectra, we directly estimate SO2 vertical column density in one step. As compared with the previous generation operational OMSO2 PBL (Planetary Boundary Layer) SO2 product, our new algorithm greatly reduces unphysical biases and decreases the noise by a factor of two, providing greater sensitivity to anthropogenic emissions. The new algorithm is fast, eliminates the need for instrument-specific radiance correction schemes, and can be easily adapted to other sensors. These attributes make it a promising technique for producing long-term, consistent SO2 records for air quality and climate research. We have operationally implemented this new algorithm on OMI SIPS for producing the new generation standard OMI SO2 products.

Sulfur dioxide↗
NASA NTRS2006

Principle Component Analysis of the Evolution of the Saharan Air Layer and Dust Transport: Comparisons between a Model Simulation and MODIS Retrievals

The onset and evolution of Saharan Air Layer (SAL) episodes during June-September 2002 are diagnosed by applying principal component analysis to the NCEP reanalysis temperature anomalies at 850 hPa, where the largest SAL-induced temperature anomalies are located. The first principal component (PC) represents the onset of SAL episodes, which are associated with large warm anomalies located at the west coast of Africa. The second PC represents two opposite phases of the evolution of the SAL. The positive phase of the second PC corresponds to the southwestward extension of the warm anomalies into the tropical-subtropical North Atlantic Ocean, and the negative phase corresponds to the northwestward extension into the subtropical to mid-latitude North Atlantic Ocean and the southwest Europe. A dust transport model (CARMA) and the MODIS retrievals are used to study the associated effects on dust distribution and deposition. The positive (negative) phase of the second PC corresponds to a strengthening (weakening) of the offshore flows in the lower troposphere around 10deg - 20degN, causing more (less) dust being transported along the tropical to subtropical North Atlantic Ocean. The variation of the offshore flow indicates that the subseasonal variation of African Easterly Jet is associated with the evolution of the SAL. Significant correlation is found between the second PC time series and the daily West African monsoon index, implying a dynamical linkage between West African monsoon and the evolution of the SAL and Saharan dust transport.

Wong, S.↗
DOE OSTI2025

Explainable Machine Learning for Functional Data

Black-box machine learning models are recognized as useful tools for prediction applications, but the algorithmic complexity of some models causes interpretation challenges. Explainability methods have been proposed to provide insight into these models, but there is little research focused on supervised modeling with functional data inputs. We argue that, especially in applications of high consequence, it is important to explicitly model the functional dependence in a black-box analysis to not obscure or misrepresent patterns in explanations. As such, we propose the V ariable importance E xplainable E lastic S hape A nalysis (VEESA) pipeline for training supervised machine learning models with functional inputs. The pipeline is an analysis process that includes the data preprocessing, modeling, and post-hoc explanations. The preprocessing is done using elastic functional principal components analysis, which accounts for vertical and horizontal variability in functional data and, ultimately, allows for explanations in the original data space that identify the important functional variability without bias due to correlated variables. Here, we demonstrate the pipeline on two high-consequence applications: explosives classification for national security and inkjet printer identification in forensic science. The applications exhibit the VEESA pipeline’s ability to provide an understanding of the characteristics of the functional data useful for prediction. Code for implementing the pipeline is available in the veesa R package (and supplemental python code).

Elastic Shape Analysis↗
NASA NTRS1998

Baseline Observations of Hemispheric Sea Ice with the Nimbus 7 Scanning Multichannel Microwave Radiometer

The Scanning Multichannel Microwave Radiometer (SMMR) on board the NASA Nimbus 7 satellite was designed to obtain data for sea surface temperatures (SSTs), near-surface wind speeds, sea ice coverage and type, rainfall rates over the oceans, cloud water content, snow water equivalent, and soil moisture. In this paper, I shall emphasize the sea ice observations and mention briefly some important SST observations. A prime factor contributing to the importance of SMMR sea ice observations lies in their successful integration into a long-term time series, presently being extended by observations from the series of Special Sensor Microwave/Imager (SSMI) on board the DOD/DMSP F8, Fl1, and F12 satellites. This currently constitutes a 19-year data set. Almost half of this was provided by the SMMR. Unfortunately, the 4-year data set produced earlier by the single-channel Electrically Scanned Microwave Radiometer (ESMR) was not successfully integrated into the SMMR/SSMI data set. This resulted primarily from the lack of an overlap period to provide intersensor adjustment, but also because of the large difference between the algorithms to produce ice concentrations and large temporal gaps in the ESMR data. The lack of overlap between the SeaSat and Nimbus 7 SMMR data sets was an important consideration for also excluding the SeatSat one, but the spatial gaps especially in the Southern Hemisphere daily SeaSat observations was another. The sea ice observations will continue into the future by means of the Advanced Microwave Scanning Radiometer (AMSR) on board the ADEOS II and EOS satellites due to be launched in mid- and late-2000, respectively. Analysis of the sea ice data has been carried out by a number of different techniques. Long-term trends have been examined by means of ordinary least squares and band-limited regression. Oscillations in the data have been examined by band-limited Fourier analysis. Here, I shall present results from a novel combination of Principal Component analysis and the recently-developed Empirical Mode Decomposition (EMD). In this method, the data are first separated into spatial and temporal parts, and then the temporal parts of the first few PCs are broken into intrinsic modes by the EMD method.

Gloersen, Per↗
NASA NTRS2003

Kernel PLS-SVC for Linear and Nonlinear Discrimination

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

Rosipal, Roman↗
NASA NTRS2024

Bridgeport Urban Development: Leveraging NASA Earth Observations and Sociodemographic Data to Assess Urban Heat Vulnerability and Inform Cool Corridors in Bridgeport, Connecticut

Urban environments face hotter temperatures than suburban and rural areas due to higher concentrations of impervious surfaces, heat-retaining buildings, and lack of green space. Bridgeport, Connecticut, which was formerly a national manufacturing hub, is now the densest and most populous city in the state. Bridgeport experiences hotter temperatures, exposing its residents to more extreme temperatures than the surrounding affluent suburbs. Extreme heat affects the health of those exposed to it and intensifies energy demands. Understanding temperature differences is the first step in effectively directing mitigation efforts. Our partner, Groundwork Bridgeport, along with the Yale Urban Design Workshop, are planning a “cool corridors” project, implementing cooling infrastructure to combat urban heat. We used Landsat 8 Thermal Infrared Sensor and Landsat 9 Thermal Infrared Sensor-2 data to conduct a Land Surface Temperature analysis in Google Earth Engine for the county of Fairfield. A Principal Component Analysis was performed to identify indicators of social vulnerability in Bridgeport. We used the SOlar and LongWave Environmental Irradiance Geometry model to identify felt heat on the block level to inform where the partner should locate their cooling interventions to ensure they are most effective and equitable. We focused on the East Side of Bridgeport, which we found was 10 degrees hotter than other areas of Bridgeport and the neighboring town of Fairfield. We integrated our findings using Earth observations and additional sociodemographic and climate data into final communication products for our partners which will facilitate their selection of candidate locations for their Cool Corridors project.

Silas Kirsch↗
NASA NTRS1995

The luminosity structure and objective classification of galaxies

The luminosity structure of spiral galaxies is studied using the technique of principal component analysis. It is found that approximately 94% of the variation in the luminosity distribution of galaxies can be accounted for by just two principal components. The principal luminosity components may contain valuable information about star formation history or whatever luminosity-regulating process occurs in galaxies. Practically, these principal components provide a new approach for the investigation of the luminosity structures of galaxies and their dependence on other properties. They also serve as an excellent objective classification system for galaxies. We introduce in this paper such a classification scheme and explore its various properties. The new system shows a number of very impressive characteristics. Most important, it can well segregate virtually all the important galactic properties we tested and does so much better than the conventional morphological classification systems. Of particular interest is that some distance-dependent parameters can also be determined to a surprisingly good accuracy; for example, absolute magnitude may be determined to an accuracy of approximately 0.6 mag (yet further improvement is believed to be highly possible). Second, the system is objective, and the classification procedure can be automated to a large degree; also the new system can apply to much smaller and fainter images than do eye-based clasification systems. These properties make the new system suitable for practical application, especially on very large (and deeper) digital image catalogs. Third, the classification is expressed in dimensionless numbers, yet the simple notation bears significant and easily understandable meaning, making it easy and convenient to use. Finally, the new system has another extremely useful feature: it provides a very powerful and convenient platform not only for classification, but also for easily recording, examining, and studying the variations and correlations of galaxy properties-all these may be carried out graphically by using the C-vectors and the C-diagrams introduced in the paper. We wil also give an example to demonstrate the use of the classification system for the study of the internal extinction problem in spiral galaxies.

Han, Mingshen↗
NASA NTRS1994

The intermediate line region of QSOs

Our recent statistical investigations of broad UV lines of luminous quasars (QSOs) suggest that the traditional broadline region (BLR) consists of two components-one of width approximately 2000 km/s FWHM with the peak within a few hundred kilometers per second of the systemic redshift, and another very broad component of width greater than or equal to 7000 km/s FWHM and blueshifted by greater than or equal to 1000 km/s. Differences in the relative strengths of these components account for much of the diversity of broad-line profiles, as well as relations among line strength, line width, asymmetry, and peak blueshift. We have suggested that the narrower component arises in a distinct intermediate-line region (ILR) that is an inner extension of the narrow-line region (NLR). We form spectra of the ILR and very broad line region (VBLR) in two complementary ways. First, using a small sample of high-quality spectra, we difference two composite spectra, one with FWHM (sub C IV) approximately 3000 km/s, the other with FWHM (sub C IV) approximately 7000 km/s (essentially a VBLR spectrum)-revealing a narrower line spectrum with FWHM approximately 2000 km/s. Second, we use a principal component analysis of 200 low signal-to-noise ratio spectra from the Large Bright Quasar Survey. The ILR is identified with the first principal component-that component accounting for most of the spectrum-to-spectrum variation. The VBLR spectrum is derived by subtracting this ILR from the mean spectrum. The two methods yield similar results, and the spectra of the ILR and VBLR are very different. Additional support for the existence of two component is the lack of a correlation between the equivalent widths of the ILR and VBLR. We discuss the relationships between the VBLR, the ILR, and the traditional NLR. The ILR line intensity ratios are distinctly different from those of the VBLR, with (relative to C IV lambda 1549) stronger Lyman alpha and weaker N v lambda 1240, lambda 1400 feature, He II lambda 1640, O III lambda 1663, and Al III lambda 1860. Comparison with other active galactic nuclei (AGN) emission-line regions shows that the ILR spectrum tends to be intermediate between that of the VBLR and that of gas more distant from the ionizing continuum, such as the NLR and extended Lyman alpha nebulosity. Comparison of the spectra with results from photoionization models suggests that, compared with the VBLR, the ILR is about 10 times more distant from the ionizing continuum (approximately 1 pc), 100 to 1000 times less dense (10(exp 10)/cu cm), and has a smaller covering factor (less than or approximately 3%, compared with approximately 24% for the VBLR).

Brotherton, M. S.↗