UMAP_Projection_of_Populus_trichocarpa_Genotypes_Colored_by_DBSCAN_Cluster
Aninteractive UMAPplot derived from the top 8 PCs of LD-prunedPopulus trichocarpapangenome SNP data. Points are colored by DBSCAN clustering.
09 BIOMASS FUELS↗
SEARCH · Engineering Papers
Results for “data clustering”
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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Analysis of multispectral data using an unsupervised classification technique: Application to VAS
A statistical classification method based on clustering of multidimensional histograms was applied to several channels of the VAS multispectral imagery. The method automatically discriminates and classifies atmospheric ground features such as cloud types, atmospheric moisture patterns, ocean, or ground. Such a clustering method has the advantage of forming natural data groupings, without a priori classification. Clusters are not limited by straight lines or plane surfaces as is the case in threshold methods. The method was applied to simultaneous full resolution images from channels 8 (11.2 micron), 10 (6.7 micron), and 12 (3.9 micron). Twenty image segments of 64 by 64, 12 image segments of 128 by 128, and 4 image segments of 254 by 254 picture elements were analyzed. In addition, normal VISSR mode images at 1800, 1830, and 2000 GMT were used to identify the classes. The gray levels measured along a scan line and the result of the classification scheme (dashed curves) for the three channels investigated are shown. Each point of the image is affected to a class. Each class is identified by a center of gravity that is represented by a vector in the three dimensional space of gray levels.
Szejwach, G.↗
Impact of Using Audit Data to Improve the Evidence-Based Use of Single-Fraction Radiation Therapy for Bone Metastases in British Columbia
Purpose: To assess the impact of a population-based intervention to increase the consistency and use of single-fraction radiation therapy (SFRT) for bone metastases. Methods and Materials: In 2012, an audit of radiation therapy prescriptions for bone metastases in British Columbia identified significant interphysician and -center (26%-73%) variation in the use of SFRT. Anonymous physician-level and identifiable regional cancer center SFRT use data were presented to all radiation oncologists, together with published guidelines, meta-analyses, and recommendations from practice leaders. The use of SFRT for bone metastases from 2007 through 2011 was compared with use of SFRT in 2013, to assess the impact of the audit and educational intervention. Multilevel logistic regression was used to assess the relationship between the usage of SFRT and the timing of the radiation while controlling for potentially confounding variables. Physician and center were included as group effects to account for the clustered structure of the data. Results: A total of 16,898 courses of RT were delivered from 2007 through 2011, and 3200 courses were delivered in 2013. The rates of SFRT use in 2007, 2008, 2009, 2010, 2011, and 2013 were 50.5%, 50.9%, 48.3%, 48.5%, 48.0%, and 59.7%, respectively (P<.001). Use of SFRT increased in each of 5 regional centers: A: 26% to 32%; B: 36% to 56%; C: 39% to 57%; D: 49% to 56%; and E: 73% to 85.0%. Use of SFRT was more consistent; 3 of 5 centers used SFRT for 56% to 57% of bone metastases RT courses. The regression analysis showed strong evidence that the usage of SFRT increased after the 2012 intervention (odds ratio 2.27, 95% confidence interval 2.06-2.50, P<.0001). Conclusion: Assessed on a population basis, an audit-based intervention increased utilization of SFRT for bone metastases. The intervention reversed a trend to decreasing SFRT use, reduced costs, and improved patient convenience. This suggests that dissemination of programmatic quality indicators in oncology can lead to increased utilization of evidence-based practice.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
LARGE-SCALE FILAMENTARY STRUCTURES AROUND THE VIRGO CLUSTER REVISITED
We revisit the filamentary structures of galaxies around the Virgo cluster, exploiting a larger data set, based on the HyperLeda database, than previous studies. In particular, this includes a large number of low-luminosity galaxies, resulting in better sampled individual structures. We confirm seven known structures in the distance range 4 h {sup −1} Mpc < SGY < 16 h {sup −1} Mpc, now identified as filaments, where SGY is the axis of the supergalactic coordinate system roughly along the line of sight. The Hubble diagram of the filament galaxies suggests they are infalling toward the main body of the Virgo cluster. We propose that the collinear distribution of giant elliptical galaxies along the fundamental axis of the Virgo cluster is smoothly connected to two of these filaments (Leo II A and B). Behind the Virgo cluster (16 h {sup −1} Mpc < SGY < 27 h {sup −1} Mpc), we also identify a new filament elongated toward the NGC 5353/4 group (“NGC 5353/4 filament”) and confirm a sheet that includes galaxies from the W and M clouds of the Virgo cluster (“W–M sheet”). In the Hubble diagram, the NGC 5353/4 filament galaxies show infall toward the NGC 5353/4 group, whereas the W–M sheet galaxies do not show hints of gravitational influence from the Virgo cluster. The filamentary structures identified can now be used to better understand the generic role of filaments in the build-up of galaxy clusters at z ≈ 0.
79 ASTRONOMY AND ASTROPHYSICS↗
Distributed non-negative matrix factorization with determination of the number of latent features
The holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for data-driven science, decision making and emergency response. Such exploratory analyses require devising unsupervised learning methods for data mining and extraction of the latent features, and non-negative matrix factorization (NMF) is one of the prominent such methods. NMF is based on compute-intense non-convex constrained minimization, which, for large datasets requires fast and distributed algorithms. However, current parallel implementations of NMF fail to estimate the number of latent features. In practice, identifying these features is both difficult and significant for pattern recognition and latent feature analysis, especially for large dense matrices. Here, we introduce a distributed NMF algorithm coupled with distributed custom clustering followed by a stability analysis on dense data, which we call DnMFk, to determine the number of latent variables. The results on synthetic data and the classical Swimmer data set demonstrate the accuracy of model determination while scaling nearly linearly across multiple processors for large data. Further, we employ DnMFk to determine the number of hidden features from a terabyte matrix.
97 MATHEMATICS AND COMPUTING↗
Observational evidence of preferred flow regimes in the Northern Hemisphere winter stratosphere
Ten years of stratospheric geopotential height data are analyzed in an attempt to determine whether there are preferred flow regimes in the Northern Hemisphere winter stratosphere. The data are taken from Stratospheric Sounding Units on board NOAA satellites. The probability density estimate of the amplitude of the wavenumber 1 10-mb height is found to be bimodal. The density distribution is composed of a dominant large-amplitude mode and a less frequent low-amplitude mode. When the wavenumber 1 10-mb height data are projected onto the phase plane defined by the 10-mb zonal-mean winds and wavenumber 1 100-mb heights, three preferred regimes are evident. The small-amplitude mode separates into a strong zonal wind-weak wave regime and a weak zonal wind-weak wave regime. The large-amplitude mode is an intermediate zonal wind-strong wave regime. Transitions between the large-amplitude regime and the weak zonal wind-weak wave regime are found to be associated with major stratospheric warmings. The clustering of the stratospheric data into the preferred flow regimes is interpreted in light of the bifurcation properties of the Holton and Mass model. The interannual variability of the Northern Hemisphere winter stratosphere is interpreted in terms of the relative frequency of the observed preferred regimes.
Pierce, R. B.↗
Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability
Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.
14 SOLAR ENERGY↗
Elemental Abundances in the Intracluster Gas and the Hot Galactic Coronae in Cluster A 194
The wealth of available data from ASCA and ROSAT have allowed new approaches to interesting and fundamental problems related to galaxy and cluster evolution, the synthesis of the elements, and the enrichment of the intracluster medium. This paper will present a detailed study of the dynamics of these merging systems.
Forman, William↗
Analysis of plasmaspheric plumes: CLUSTER and IMAGE observations and numerical simulations
Plasmaspheric plumes have been routinely observed by CLUSTER and IMAGE. The CLUSTER mission provides high time resolution four-point measurements of the plasmasphere near perigee. Total electron density profiles can be derived from the plasma frequency and/or from the spacecraft potential (note that the electron spectrometer is usually not operating inside the plasmasphere); ion velocity is also measured onboard these satellites (but ion density is not reliable because of instrumental limitations). The EUV imager onboard the IMAGE spacecraft provides global images of the plasmasphere with a spatial resolution of 0.1 RE every 10 minutes; such images acquired near apogee from high above the pole show the geometry of plasmaspheric plumes, their evolution and motion. We present coordinated observations for 3 plume events and compare CLUSTER in-situ data (panel A) with global images of the plasmasphere obtained from IMAGE (panel B), and with numerical simulations for the formation of plumes based on a model that includes the interchange instability mechanism (panel C). In particular, we study the geometry and the orientation of plasmaspheric plumes by using a four-point analysis method, the spatial gradient. We also compare several aspects of their motion as determined by different methods: (i) inner and outer plume boundary velocity calculated from time delays of this boundary observed by the wave experiment WHISPER on the four spacecraft, (ii) ion velocity derived from the ion spectrometer CIS onboard CLUSTER, (iii) drift velocity measured by the electron drift instrument ED1 onboard CLUSTER and (iv) global velocity determined from successive EUV images. These different techniques consistently indicate that plasmaspheric plumes rotate around the Earth, with their foot fully co-rotating, but with their tip rotating slower and moving farther out.
Darouzet, Fabien↗
Electric load shape benchmarking for small- and medium-sized commercial buildings
Small- and medium-sized commercial buildings owners and utility managers often look for opportunities for energy cost savings through energy efficiency and energy waste minimization. However, they currently lack easy access to low-cost tools that help interpret the massive amount of data needed to improve understanding of their energy use behaviors. Benchmarking is one of the techniques used in energy audits to identify which buildings are priorities for an energy analysis. Traditional energy performance indicators, such as the energy use intensity (annual energy per unit of floor area), consider only the total annual energy consumption, lacking consideration of the fluctuation of energy use behavior over time, which reveals the time of use information and represents distinct energy use behaviors during different time spans. To fill the gap, this study developed a general statistical method using 24-hour electric load shape benchmarking to compare a building or business/tenant space against peers. Specifically, the study developed new forms of benchmarking metrics and data analysis methods to infer the energy performance of a building based on its load shape. We first performed a data experiment with collected smart meter data using over 2,000 small- and medium-sized businesses in California. We then conducted a cluster analysis of the source data, and determined and interpreted the load shape features and parameters with peer group analysis. Finally, we implemented the load shape benchmarking feature in an open-access web-based toolkit (the Commercial Building Energy Saver) to provide straightforward and practical recommendations to users. The analysis techniques were generic and flexible for future datasets of other building types and in other utility territories.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Concept and simulation study of a novel localization method for robotic endoscopic capsules using multiple positron emission markers
Purpose: Over the last decade, wireless capsule endoscope has been the tool of choice for noninvasive inspection of the gastrointestinal tract, especially in the small intestine. However, the latest clinical products have not been equipped with a sufficiently accurate localization system which makes it difficult to determine the location of intestinal abnormalities, and to apply follow-up interventions such as biopsy or drug delivery. In this paper, the authors present a novel localization method based on tracking three positron emission markers embedded inside an endoscopic capsule. Methods: Three spherical {sup 22}Na markers with diameters of less than 1 mm are embedded in the cover of the capsule. Gamma ray detectors are arranged around a patient body to detect coincidence gamma rays emitted from the three markers. The position of each marker can then be estimated using the collected data by the authors’ tracking algorithm which consists of four consecutive steps: a method to remove corrupted data, an initialization method, a clustering method based on the Fuzzy C-means clustering algorithm, and a failure prediction method. Results: The tracking algorithm has been implemented inMATLAB utilizing simulation data generated from the Geant4 Application for Emission Tomography toolkit. The results show that this localization method can achieve real-time tracking with an average position error of less than 0.4 mm and an average orientation error of less than 2°. Conclusions: The authors conclude that this study has proven the feasibility and potential of the proposed technique in effectively determining the position and orientation of a robotic endoscopic capsule.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Cluster-Induced Fluctuations in the Microwave Background Radiation
The research proposed was to detect, map and interpret the Sunyaev-Zel dovich (SZ) effects in two samples of distant clusters of galaxies with the OVRO 40-m telescope: an optically selected sample of 26 clusters at the North Ecliptic Pole, and an X-ray selected sample of clusters based on the Einstein Medium Sensitivity Survey, to make small maps of the strongest cluster SZ effects using the OVRO 40-m telescope, to combine the SZ and X-ray data for well-detected clusters to determine the value of the Hubble constant and set limits to the value of the deceleration parameter, and to study the properties of cluster atmospheres using the SZ effect.
Birkinshaw, Mark↗
Droplet Vaporization In A Levitating Acoustic Field
Combustion experiments using arrays of droplets seek to provide a link between single droplet combustion phenomena and the behavior of complex spray combustion systems. Both single droplet and droplet array studies have been conducted in microgravity to better isolate the droplet interaction phenomena and eliminate or reduce the effects of buoyancy-induced convection. In most experiments involving droplet arrays, the droplets are supported on fibers to keep them stationary and close together before the combustion event. The presence of the fiber, however, disturbs the combustion process by introducing a source of heat transfer and asymmetry into the configuration. As the number of drops in a droplet array increases, supporting the drops on fibers becomes less practical because of the cumulative effect of the fibers on the combustion process. To eliminate the effect of the fiber, several researchers have conducted microgravity experiments using unsupported droplets. Jackson and Avedisian investigated single, unsupported drops while Nomura et al. studied droplet clouds formed by a condensation technique. The overall objective of this research is to extend the study of unsupported drops by investigating the combustion of well-characterized drop clusters in a microgravity environment. Direct experimental observations and measurements of the combustion of droplet clusters would provide unique experimental data for the verification and improvement of spray combustion models. In this work, the formation of drop clusters is precisely controlled using an acoustic levitation system so that dilute, as well as dense clusters can be created and stabilized before combustion in microgravity is begun. While the low-gravity test facility is being completed, tests have been conducted in 1-g to characterize the effect of the acoustic field on the vaporization of single and multiple droplets. This is important because in the combustion experiment, the droplets will be formed and levitated prior to ignition. Therefore, the droplets will begin to vaporize in the acoustic field thus forming the "initial conditions" for the combustion process. Understanding droplet vaporization in the acoustic field of this levitator is a necessary step that will help to interpret the experimental results obtained in low-gravity.
Ruff, G. A.↗
Training data selection for event classification in a highly variable environment
A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.
Iyer, A↗
The Atacama Cosmology Telescope: DR6 Sunyaev-Zel'dovich Selected Galaxy Clusters Catalog
We present the results of a search for galaxy clusters in the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) microwave sky maps covering 16293 square degrees in three frequency bands, using data obtained over the lifetime of the project (2008-2022). We report redshifts and mass estimates for 10040 clusters detected via their Sunyaev-Zel'dovich (SZ) effect with signal-to-noise greater than 4 at a 2.4 arcminute filter scale. The catalog includes 1180 clusters at redshifts greater than 1, and 124 clusters at redshifts greater than 1.5. Using a relation between cluster SZ signal and mass that is consistent with recent weak-lensing measurements, we estimate that clusters detected with signal-to-noise greater than 5 form a sample which is 90% complete for clusters with masses greater than $5 \times 10^{14}$ MSun (measured within a spherical volume with mean density 500 times the critical density). El Gordo, a cluster found in an initial ACT survey of 755 square degrees, remains the most extreme cluster in mass and redshift; we find no cluster with a mass and redshift combination high enough to falsify the standard LCDM cosmology with Gaussian initial perturbations. We make public a variety of data products, including the full cluster candidate list, noise maps, and sky masks, along with our software for cluster detection and instructions for reproducing our cluster catalogs from the public ACT maps.
Aguena, M. [Trieste Observ.; LIneA, Rio de Janeiro↗
From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation Among Gene Classes from Large-Scale Expression Data
We provide preliminary evidence that existing algorithms for inferring small-scale gene regulation networks from gene expression data can be adapted to large-scale gene expression data coming from hybridization microarrays. The essential steps are (I) clustering many genes by their expression time-course data into a minimal set of clusters of co-expressed genes, (2) theoretically modeling the various conditions under which the time-courses are measured using a continuous-time analog recurrent neural network for the cluster mean time-courses, (3) fitting such a regulatory model to the cluster mean time courses by simulated annealing with weight decay, and (4) analysing several such fits for commonalities in the circuit parameter sets including the connection matrices. This procedure can be used to assess the adequacy of existing and future gene expression time-course data sets for determining transcriptional regulatory relationships such as coregulation.
Mjolsness, Eric↗
Many-body forces and nucleon clustering near the QCD critical point
It has been proposed that one can look for the QCD critical point (CP) by the Beam Energy Scan accurately monitoring event-by-event fluctuations. This experimental program is under way at the BNL RHIC collider. Separately, it has been studied how clustering of nucleons at freeze out affects proton multiplicity distribution and light nuclei production. It was found that even a minor increase of the range of nuclear forces dramatically increases clustering, while large correlation length ξ near CP makes attraction due to binary forces unrealistically large. Here, in this paper, we show that repulsive many-body forces near CP should overcome the binary ones and effectively suppress clustering. We also discuss current experimental data and point out locations at which a certain drop in clustering may already be observed.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
The revised HEAO A-2 X-ray luminosity function for Abell clusters
Spectroscopic redshifts are determined for Abell clusters in the HEAO A-2 X-ray sample. These data are used to correct the cluster X-ray luminosity function of McKee et al.
Hintzen, P.↗