Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cluster detection”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Preliminary Evidence of Gas-Phase Water Splitting on Holmium Nitrogen Oxide Clusters

Preliminary Evidence of Gas-Phase Water Splitting on Holmium Nitrogen Oxide Clusters Christopher A. Zarzana1, Makayla R. Baxter , Introduction: Molecular hydrogen is a potential energy carrier that could be used to help implement a clean energy economy if it is generated from splitting of water. Improvements in the efficiencies of water-splitting electrolyzers relies on development of novel materials with enhanced performance. However, research in this area is slowed due to underdeveloped understanding of the mechanisms of device performance due to challenges interrogating the fundamental chemical reactions at play in bulk materials. Studies of the intrinsic reactivity of clusters that are representative of the reactive sites of these materials can increase understanding of the fundamental reaction mechanisms involved in hydrogen production, allowing for more efficient development of new water-splitting materials. Methods: Holmium tetranitrato ([Ho(NO3)4]-) clusters were generated in gas-phase using the electrospray ionization source of a Bruker (Billerica, MA, USA) micrOTOF-Q II quadrupole time-of-flight mass spectrometer. Spray solutions consisted of aqueous holmium (Ho) nitrate solutions (at nominally 3 mM) diluted to 30 µM in acetonitrile. The holmium (Ho) tetranitrato clusters were isolated using the quadrupole and were subsequently activated and allowed to react with background water in the collision cell. High resolution, high mass accuracy spectra were recorded using the time-of-flight. Mass accuracy was ensured using external calibration with Agilent (Santa Clara, CA, USA) ESI-L Low Concentration tuning mix. Preliminary data: Collisional activation of the holmium tetranitrato complexes ([Ln(NO3)4]-) resulted in an expected series of ions resulting from the loss of ·NO and ·NO2. This included an ion at m/z = 382.885 assigned as [HoO2(NO3)3]- (theoretical m/z=382.884, error=-1.2 ppm), resulting from loss of ·NO, and an ion at m/z=366.890 assigned as [HoO(NO3)3]- (theorical m/z=366.889, error=-1.8 ppm), resulting from loss of ·NO2. Additional ions were detected that would result from more complicated losses from [Ho(NO3)4]-, including ions at m/z=320.898 assigned as [HoO2(NO3)2]- (theoretical m/z=320.896, error=-4.9 ppm), at m/z=304.904 assigned as [HoO(NO3)2]- (theoretical m/z=304.901, error=-8.1 ppm, very low signal), and at m/z=288.908 assigned as [Ho(NO3)2]- (theoretical m/z=288.907, error=-3.4 ppm). This ion series would arise from loss of some combination of ·NO, ·NO2, and ·NO3, although it is not known whether these losses occur sequentially (e.g. loss of ·NO and ·NO3 to yield [HoO2(NO3)2]-) or as a single species (e.g. direct loss of N2O4). These ions were accompanied by a complementary series representing addition of a single water molecule. This included an ion at m/z=338.908 assigned as [HoO2(NO3)2H2O]- (theoretical m/z=338.907, error=-4.1 ppm), an ion at m/z=322.912 assigned as [HoO(NO3)2H2O]- (theoretical m/z=322.912, error=1.2 ppm), and an ion at m/z=306.918 assigned as [Ho(NO3)2H2O]-, (theoretical m/z=306.918, error=-3.7 ppm). An additional hydrated ion was observed at m/z=276.921 assigned as [HoO2(NO3) H2O]- (theoretical m/z=276.919, error=-7.3 ppm), although corresponding dehydrated ion was not observed. An additional ion was observed at m/z=367.898 that has been assigned as [Ho(NO3)3OH]- (theoretical m/z=367.897, error=-2.5 ppm). It is hypothesized that this ion arises from addition of water to [Ho(NO3)3]- followed by elimination of a hydrogen radical. Neither [Ho(NO3)3]- nor [Ho(NO3)3H2O]- were detected, suggesting that, if the hypothesis is correct, addition of water to [Ho(NO3)3]- and its subsequent splitting is rapid. Elimination of HNO3 from [Ho(NO3)4H2O]- could also yield [Ho(NO3)3OH]-; however, no [Ho(NO3)4H2O]- ions were observed. Novelty: Potential evidence of water splitting on gas-phase lanthanide clusters offers a way to study the intrinsic reactivity of hydrogen-generation materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Removing imaging systematics from galaxy clustering measurements with Obiwan: application to the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxy sample

This article presents the application of a new tool, Obiwan, which uses image simulations to determine the selection function of a galaxy redshift survey and calculate three-dimensional (3D) clustering statistics. Obiwan relies on a forward model of the process by which images of the night sky are transformed into a 3D large-scale structure catalogue, and offers several advantages over more traditional map-based techniques – such as operating on individual exposures and adopting a maximum likelihood approach. The photometric pipeline automatically detects and models galaxies and then generates a catalogue of such galaxies with detailed information for each one of them, including their location, redshift, and so on. Systematic biases in the imaging data are therefore imparted into the catalogues and must be accounted for in any scientific analysis of their information content. Obiwan simulates this process for samples selected from the Legacy Surveys imaging data. This imaging data will be used to select target samples for the next-generation Dark Energy Spectroscopic Instrument (DESI) experiment. Here, we apply Obiwan to a portion of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxies (ELGs). Systematic biases in the data are clearly identified and removed. We compare the 3D clustering results to those obtained by the map-based approach applied to the complete eBOSS Data Release 16 (DR16) sample. We find the results are consistent, thereby validating the eBOSS DR16 ELG catalogues, which is used to obtain cosmological results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The Sagittarius stream in Gaia Early Data Release 3 and the origin of the bifurcations

The Sagittarius dwarf spheroidal (Sgr) is a dissolving galaxy being tidally disrupted by the Milky Way (MW). Its stellar stream still poses serious modelling challenges, which hinders our ability to use it effectively as a prospective probe of the MW gravitational potential at large radii. Our goal is to construct the largest and most stringent sample of stars in the stream with which we can advance our understanding of the Sgr-MW interaction, focusing on the characterization of the bifurcations. We improved on previous methods based on the use of the wavelet transform to systematically search for the kinematic signature of the Sgr stream throughout the whole sky in the Gaia data. We then refined our selection via the use of a clustering algorithm on the statistical properties of the colour-magnitude diagrams. Our final sample contains more than 700 000 candidate stars and is three times larger than previous Gaia samples. With it, we have been able to detect the bifurcation of the stream in both the northern and southern hemispheres, which requires four branches (two bright and two faint) to fully describe the system. We present the detailed proper motion distribution of the trailing arm as a function of the angular coordinate along the stream, showing, for the first time, the presence of a sharp edge (on the side of the small proper motions) beyond which there are no Sgr stars. We also characterize the correlation between kinematics and distance. Finally, the chemical analysis of our sample shows that the faint branch of the bifurcation is more metal poor than the bright. We provide analytical descriptions for the proper motion trends as well as for the sky distribution of the four branches of the stream. Based on our analysis, we interpret the bifurcations as a misaligned overlap of the material stripped at the antepenultimate pericentre (faint branches) with the stars ejected at the penultimate pericentre (bright branch), given that Sgr just went through its perigalacticon. The source of this misalignment is still unknown, but we argue that models with some internal rotation in the progenitor – at least during the time of stripping of the stars that are now in the faint branches – are worth exploring.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Whole Genome Sequencing Links Mycobacterium bovis From Cattle, Cheese and Humans in Baja California, Mexico

Mycobacterium bovis causes tuberculosis (TB) in cattle, which in turn can transmit the pathogen to humans. Tuberculosis in dairy cattle is of particular concern where the consumption of raw milk and dairy products is customary. Baja California (BCA), Mexico, presents high prevalence of TB in both cattle and humans, making it important to investigate the molecular epidemiology of the disease in the region. A long-term study was undertaken to fully characterize the diversity of M. bovis genotypes circulating in dairy cattle, cheese and humans in BCA by whole-genome sequencing (WGS). During a 2-year period, 412 granulomatous tissue samples were collected from local abattoirs and 314 cheese samples were purchased from local stores and vendors in BCA and sent to the laboratory for mycobacterial culture, histology, direct PCR and WGS. For tissue samples M. bovis was recovered from 86.8%, direct PCR detected 90% and histology confirmed 85.9% as mycobacteriosis-compatible. For cheese, M. bovis was recovered from 2.5% and direct PCR detected 6% of the samples. There was good agreement between diagnostic tests. Subsequently, a total of 345 whole-genome SNP sequences were obtained. Phylogenetic analysis grouped these isolates into 10 major clades. SNP analysis revealed putative transmission clusters where the pairwise SNP distance between isolates from different dairies was ≤3 SNP. Also, human and/or cheese isolates were within 8.45 (range 0–17) and 5.8 SNP (range 0–15), respectively, from cattle isolates. Finally, a comparison between the genotypes obtained in this study and those reported previously suggests that the genetic diversity of M. bovis in BCA is well-characterized, and can be used to determine if BCA is the likely source of M. bovis in humans and cattle in routine epidemiologic investigations and future studies. In conclusion, WGS provided evidence of ongoing local transmission of M. bovis among the dairies in this high-TB burden region of BCA, as well as show close relationships between isolates recovered from humans, cheese, and cattle. This confirms the need for a coordinated One Health approach in addressing the elimination of TB in animals and humans. Overall, the study contributes to the knowledge of the molecular epidemiology of M. bovis in BCA, providing insight into the pathogen's dynamics in a high prevalence setting.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

β Cephei Pulsations in the High-mass Eclipsing System CW Cephei

CW Cep is an early B-type eclipsing binary with mass measurement precisions better than 1%. We report the discovery of pulsation signatures in the Transiting Exoplanet Survey Satellite time-series data of the system observed during Sectors 17 and 18. Our binary modeling indicates that the target star is a partially eclipsing detached system with masses of 12.95 M {sub ⊙} and 11.88 M {sub ⊙} and radii of 5.52 R {sub ⊙} and 5.09 R {sub ⊙} in an eccentric orbit of e = 0.0305. The distance to the eclipsing system, 928 ± 36 pc, is much more precise than the Gaia distance of 962 ± 453 pc. Applying multifrequency analyses to the residual light curve in the outside-eclipse part, we detected 13 significant signals in two frequency regions. Six frequencies below 1 day{sup −1} appeared to be mostly orbital harmonic and combination terms, or sidelobes due to insufficient removal of the binary effects. In contrast, seven frequencies clustered around 2.73 day{sup −1} and 5.34 day{sup −1} could be considered β Cep–type pulsations. Our results represent the second discovery of β Cep pulsations present in double-lined eclipsing binaries with precise masses and, hence, CW Cep serves as an important test bed for the asteroseismic modeling of high-mass stars.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

The HST See Change Program. I. Survey Design, Pipeline, and Supernova Discoveries*

The See Change survey was designed to make z > 1 cosmological measurements by efficiently discovering high-redshift Type Ia supernovae (SNe Ia) and improving cluster mass measurements through weak lensing. This survey observed twelve galaxy clusters with the Hubble Space Telescope (HST) spanning the redshift range z = 1.13–1.75, discovering 57 likely transients and 27 likely SNe Ia at z ~ 0.8–2.3. As in similar previous surveys, this proved to be a highly efficient use of HST for supernova observations; the See Change survey additionally tested the feasibility of maintaining, or further increasing, the efficiency at yet higher redshifts, where we have less detailed information on the expected cluster masses and star formation rates. Furthermore, we find that the resulting number of SNe Ia per orbit is a factor of ~8 higher than for a field search, and 45% of our orbits contained an active SN Ia within 22 rest-frame days of peak, with one of the clusters by itself yielding 6 of the SNe Ia. We present the survey design, pipeline, and supernova discoveries. Novel features include fully blinded supernova searches, the first random forest candidate classifier for undersampled IR data (with a 50% detection threshold within 0.05 mag of human searchers), real-time forward-modeling photometry of candidates, and semi-automated photometric classifications and follow-up forecasts. We also describe the spectroscopic follow-up, instrumental in measuring host galaxy redshifts. The cosmology analysis of our sample will be presented in a companion paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Machine Learning Approaches for Nuclear Material Accounting Data from Irradiation and Reprocessing

We are currently exploring data analysis methods for their ability to strengthen the synthesis and evaluation of information generated within domestic and international safeguard regimes. Safeguard data typically includes rich heterogenous datasets amenable to advanced data analytics methods, such as machine learning. We have converted transactional data containing both numerical and categorical attributes from a domestic nuclear material control and accountability (NMC&A) system into a low-dimensional numerical structure via linear principal component analysis. This data representation allows for global structure discovery via cluster analysis, which can characterize the typical behavior of each of the primary types of transaction events. Furthermore, the structure of the top principal components captures the “typical behavior” of the data and is thus amenable to anomaly detection through statistical hypothesis testing. We explored this capability by generating erroneous permutations of the data and computing the Q-residual quantity increase associated with the information loss when these data are projected into the low-dimensional principal component analysis space representative of typical transactions.Future work will focus on identifying data transformations (e.g., graph networks) that more closely align with the inherent structure of these transactions to explain more salient information and disambiguate the underlying nuclear process—in this case, irradiation and reprocessing—from artifacts of NMC&A system transactional record keeping data.

Drescher, Adam↗
DOE OSTI2021

Accelerated, scalable and reproducible AI-driven gravitational wave detection

The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware-Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month's worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery. By combining a repository for artificial intelligence models and a supercomputing cluster, an entire month's worth of advanced LIGO data is analysed in just 7 min, finding all binary black hole mergers previously identified in this dataset and reporting no misclassifications.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Laser spectroscopy of indium Rydberg atom bunches by electric field ionization

This work reports on the application of a novel electric field-ionization setup for high-resolution laser spectroscopy measurements on bunched fast atomic beams in a collinear geometry. In combination with multi-step resonant excitation to Rydberg states using pulsed lasers, the field ionization technique demonstrates increased sensitivity for isotope separation and measurement of atomic parameters over previous non-resonant laser ionization methods. The setup was tested at the Collinear Resonance Ionization Spectroscopy experiment at ISOLDE-CERN to perform high-resolution measurements of transitions in the indium atom from the 5s 2 5d 2 D 5/2 and 5s 2 5d 2 D 3/2 states to 5s 2 np 2 P and 5s 2 nf 2 F Rydberg states, up to a principal quantum number of n=72 . The extracted Rydberg level energies were used to re-evaluate the ionization potential of the indium atom to be 46,670.107(4)cm -1 . The nuclear magnetic dipole and nuclear electric quadrupole hyperfine structure constants and level isotope shifts of the 5s 2 5d 2 D 5/2 and 5s 2 5d 2 D 3/2 states were determined for 113,115 In. The results are compared to calculations using relativistic coupled-cluster theory. A good agreement is found with the ionization potential and isotope shifts, while disagreement of hyperfine structure constants indicates an increased importance of electron correlations in these excited atomic states. With the aim of further increasing the detection sensitivity for measurements on exotic isotopes, a systematic study of the field-ionization arrangement implemented in the work was performed at the same time and an improved design was simulated and is presented. The improved design offers increased background suppression independent of the distance from field ionization to ion detection.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

dSph-obic dark matter

We present a mechanism that allows thermal relic dark matter to annihilate efficiently in the Galactic Halo and in galaxy clusters, but not in the lower-velocity environments of dwarf spheroidal (dSph) galaxies. We realize this within a complete model in which the dark matter consists of two distinct states separated by a small mass splitting. An indirect detection signal is generated only through the coannihilations of these two states, requiring both to be present. In the halo of the Milky Way, the dark matter particles in the lighter state can be excited into the long-lived heavier state through scattering. Once excited, these heavier particles can coannihilate with those in the lighter state, yielding a gamma-ray signal with little or no suppression. By contrast, the dark matter particles in dwarf galaxies do not possess enough kinetic energy to be excited, thereby suppressing the coannihilation rate and corresponding indirect detection signals from those systems. This framework breaks the predictive relationship that ordinarily exists between these respective gamma-ray signals and complicates our ability to interpret the results of indirect detection searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Activated KCNQ1 channel promotes fibrogenic response in hereditary gingival fibromatosis via clustering and activation of Ras

Abstract Background and Objective Activated potassium channels were found to be strongly correlated with gingival overgrowth (GO) phenotype as we reviewed syndromic hereditary gingival fibromatosis (HGF). Nevertheless, the functional roles of potassium channels in gingival fibrosis or gingival overgrowth remained uncovered. The aim of the present study was to explore the pathogenic role of aberrantly activated potassium channel in Hereditary Gingival Fibromatosis (HGF). Methods Gingival tissues were collected from 9 HGF patients and 15 normal controls. Expression of KCNQ1 was detected by immunohistochemistry. Gingival fibroblasts were isolated, and outward K + currents were detected by whole‐cell patch‐clamp analysis, transmembrane potential was determined by flow cytometry. Normal human gingival fibroblasts (NHGFs) were transfected with KCNQ1 adenovirus or treated with KCNQ1 selective agonist ML277 and antagonist chromanol 293B. Accumulation of Extracellular Matrix (ECM) was measured by Western blotting and Sircol Soluble Collagen Assay. Content of secreted TGF‐β1 was measured by ELISA. Active RAS pull‐down assay and cell immunofluorescence were utilized to verify RAS activation. Results KCNQ1 was upregulated in gingival tissues derived from HGF patients and HGF gingival fibroblasts presented increased outward K + currents than NHGFs. Overexpression of KCNQ1, or KCNQ1 agonist ML277, promoted fibrotic responses of NHGFs. TGF‐β1 and KCNQ1 channels formed a positive feed‐back loop. ML277 generated lateral clustering and activation of Ras on plasma membrane, followed by augmented MAPK/AP‐1 signaling pathway output. JNK or ERK1/2 inhibitors suppressed ML277‐induced AP‐1 and ECM upregulation. Conclusion Activation of KCNQ1 potassium channel promoted fibrogenic responses in NHGFs via Ras/MAPK/AP‐1 signaling.

Gao, Qian↗
DOE OSTI2021

Correlative STEM-APT characterization of radiation-induced segregation and precipitation of in-service BWR 304 stainless steel

In this study, radiation induced segregation and precipitation phenomena in an in-service boiling water reactor 304 stainless steel component were investigated using directly correlated 3D-atom probe tomography and scanning transmission electron microscopy. Significant quantitative differences in measured segregation at grain boundaries were found between the atom probe and energy dispersive spectroscopy measurements of the exact same locations. In particular, a much stronger Si segregation (~10 atomic% via atom probe versus ~4 atomic% via electron microscopy) and different Cr profile shapes were detected that are critical to models of radiation induced segregation and stress corrosion cracking behavior. These quantitative differences highlight the need for comparative microscopy and critical evaluation of limitations in each analytical method. Elemental segregation to dislocations and conjoined-clusters were also highlighted by atom probe; confirming and expanding upon what has been observed in test reactor neutron and accelerator-based ion irradiations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantitative Imaging of Cobalt Phthalocyanine Distribution on Carbon Nanotubes: A Deep Learning Approach to Catalyst Characterization

Electrochemical reduction of carbon dioxide (CO 2 ) offers a pathway to valuable products, with catalysts playing a crucial role. This study investigates the distribution of cobalt tetraaminophthalocyanine (CoPc-NH 2 ) immobilized on carbon nanotubes (CNTs), utilizing high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) to characterize CoPc-NH 2 distribution. A challenge in the quantitative HAADF-STEM analysis is the introduction of bias from manual Co atom identification. To address this, we developed and trained a convolutional neural network (CNN) using a data set generated from images of CoPc-NH 2 /CNT samples with varying Co loadings. The CNN, implemented in TensorFlow and Keras, facilitated Co atom detections. Analysis of the CNN-generated data confirmed a correlation between Co loading and surface density, consistent with findings from UV–vis spectroscopy. Furthermore, the application of Ripley’s L(d) function highlighted the presence of slight Co atom clustering. Furthermore, this work demonstrates the utility of the combined HAADF-STEM and CNN approach for providing spatially resolved information about catalyst distribution on nonplanar supports, revealing structural details that are typically lost through other characterization methods.

HAADF-STEM↗
DOE OSTI2024

Repurposing of the Run 2 CMS High Level Trigger Infrastructure as a Cloud Resource for Offline Computing

The former CMS Run 2 High Level Trigger (HLT) farm is one of the largest contributors to CMS compute resources, providing about 25k job slots for offline computing. This CPU farm was initially employed as an opportunistic resource, exploited during inter-fill periods, in the LHC Run 2. Since then, it has become a nearly transparent extension of the CMS capacity at CERN, being located on-site at the LHC interaction point 5 (P5), where the CMS detector is installed. This resource has been configured to support the execution of critical CMS tasks, such as prompt detector data reconstruction. It can therefore be used in combination with the dedicated Tier 0 capacity at CERN, in order to process and absorb peaks in the stream of data coming from the CMS detector. The initial configuration for this resource, based on statically configured VMs, provided the required level of functionality. However, regular operations of this cluster revealed certain limitations compared to the resource provisioning and use model employed in the case of WLCG sites. A new configuration, based on a vacuum-like model, has been implemented for this resource in order to solve the detected shortcomings. This paper reports about this redeployment work on the permanent cloud for an enhanced support to CMS offline computing, comparing the former and new models’ respective functionalities, along with the commissioning effort for the new setup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Bacterial xylan utilization regulons: systems for coupling depolymerization of methylglucuronoxylans with assimilation and metabolism

Abstract Bioconversion of lignocellulosic resources offers an economically promising path to renewable energy. Technological challenges to achieving bioconversion include the development of cost-effective processes that render the cellulose and hemicellulose components of these resources to fermentable hexoses and pentoses. Natural bioprocessing of the hemicellulose fraction of lignocellulosic biomass requires depolymerization of methylglucuronoxylans. This requires secretion of endoxylanases that release xylooligosaccharides and aldouronates. Physiological, biochemical, and genetic studies with selected bacteria support a process in which a cell-anchored multimodular GH10 endoxylanase catalyzes release of the hydrolysis products, aldotetrauronate, xylotriose, and xylobiose, which are directly assimilated and metabolized. Gene clusters encoding intracellular enzymes, including α-glucuronidase, endoxylanase, β-xylosidase, ABC transporter proteins, and transcriptional regulators, are coordinately responsive to substrate induction or repression. The rapid rates of glucuronoxylan utilization and microbial growth, along with the absence of detectable products of depolymerization in the medium, indicate that assimilation and depolymerization are coupled processes. Genomic comparisons provide evidence that such systems occur in xylanolytic species in several genera, including Clostridium, Geobacillus, Paenibacillus, and Thermotoga. These systems offer promise, either in their native configurations or through gene transfer to other organisms, to develop biocatalysts for efficient production of fuels and chemicals from the hemicellulose fractions of lignocellulosic resources.

Biotechnology & Applied Microbiology↗
DOE OSTI2023

Beyond the 3rd moment: a practical study of using lensing convergence CDFs for cosmology with DES Y3

ABSTRACT Widefield surveys probe clustered scalar fields – such as galaxy counts, lensing potential, etc. – which are sensitive to different cosmological and astrophysical processes. Constraining such processes depends on the statistics that summarize the field. We explore the cumulative distribution function (CDF) as a summary of the galaxy lensing convergence field. Using a suite of N-body light-cone simulations, we show the CDFs’ constraining power is modestly better than the second and third moments, as CDFs approximately capture information from all moments. We study the practical aspects of applying CDFs to data, using the Dark Energy Survey (DES Y3) data as an example, and compute the impact of different systematics on the CDFs. The contributions from the point spread function and reduced shear approximation are $\lesssim 1~{{\ \rm per\ cent}}$ of the total signal. Source clustering effects and baryon imprints contribute 1–10 per cent. Enforcing scale cuts to limit systematics-driven biases in parameter constraints degrade these constraints a noticeable amount, and this degradation is similar for the CDFs and the moments. We detect correlations between the observed convergence field and the shape noise field at 13σ. The non-Gaussian correlations in the noise field must be modelled accurately to use the CDFs, or other statistics sensitive to all moments, as a rigorous cosmology tool.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Data Analytics Applied to Coal Fired Boilers for Detecting Leaks

Data analytics were used to detect boiler leaks from five different coal-fired boilers including both subcritical and supercritical systems. Discriminant functions were developed that detected leaks up to two weeks prior to forced plant shutdowns for repairs. The leaks were identified to occur at different sections of the boiler for each plant, including waterwalls, economizer and superheater using conventional process measurement data. Leaking conditions were detected with a high degree of confidence (≪ 1% misclassified observations) and were able to distinguish normal operations from those time periods with steam leaks even while operating the power plants in power cycling mode.Multivariable statistical analyses, including Principal Component (PCA), cluster, and Fischer Discriminant Analysis (FDA) were used to characterize the leak occurrence. Normal and operational states with steam leaks were provided in the original process datasets. These datasets were split into two different groups for training and validation purposes. The data were sorted chronologically, and every third observation was assigned to training the Discriminant Function Model (DFM) while the rest were reserved for validation. PCA was used to reduce dimensionality of the original datasets. Canonical and FDA analyses were used to investigate the relationship between process variables. The outcome of the analyses revealed that nearly 35,000 observations were classified correctly; less than 0.05% of total observations were misclassified to be leaking, i.e. both false positives and false negatives.

Indrawan, Natarianto↗
DOE OSTI2021

Identifying Genomic Islands with Deep Neural Networks

Background Horizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, and are often classified by the content of their cargo genes or their means of integration and mobility. While various computational methods have been devised to detect different types of GIs, no single method is capable of detecting all types. Results We propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools. Conclusions We used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was re-trained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.

Computer Vision↗