Generating Information-Diverse Microwave Speckle Patterns Inside a Room at a Single Frequency With a Dynamic Metasurface Aperture
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47 OTHER INSTRUMENTATION↗
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Results for “DIVERSITY OF INFORMATION”
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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Semantic Complex Event Processing over End-to-End Data Flows
Emerging Complex Event Processing (CEP) applications in cyber physical systems like SmartPower Grids present novel challenges for end-to-end analysis over events, flowing from heterogeneous information sources to persistent knowledge repositories. CEP for these applications must support two distinctive features - easy specification patterns over diverse information streams, and integrated pattern detection over realtime and historical events. Existing work on CEP has been limited to relational query patterns, and engines that match events arriving after the query has been registered. We propose SCEPter, a semantic complex event processing framework which uniformly processes queries over continuous and archived events. SCEPteris built around an existing CEP engine with innovative support for semantic event pattern specification and allows their seamless detection over past, present and future events. Specifically, we describe a unified semantic query model that can operate over data flowing through event streams to event repositories. Compile-time and runtime semantic patterns are distinguished and addressed separately for efficiency. Query rewriting is examined and analyzed in the context of temporal boundaries that exist between event streams and their repository to avoid duplicate or missing results. The design and prototype implementation of SCEPterare analyzed using latency and throughput metrics for scenarios from the Smart Grid domain.
Zhou, Qunzhi↗
Preparing Underrepresented Minorities for STEM Careers in Energy
The purpose of the grant was to increase the participation of underrepresented minorities and women in STEM disciplines that are critical for the energy industry. The grant was implemented by the Fort Valley State University’s (FVSU) Cooperative Developmental Energy Program (CDEP). FVSU-CDEP enrolled two cohorts of six academically talented freshmen students into its accelerated five-year 3+2 dual degree STEM program. In the 3+2 dual degree format, students enroll at FVSU for three years and earn bachelor’s degrees in biology, chemistry, or mathematics and transfer to a partnering university for two years to earn second bachelor’s degrees in engineering, geology/geophysics, or health physics. FVSU-CDEP partnering universities consist of Georgia Tech, Grand Valley State University, Penn State University, University of Alabama, University of Arkansas, University of Nevada-Las Vegas, and the University of Texas-Austin. Cohort I enrolled at FVSU fall semester of 2017 and cohort II enrolled fall semester of 2020. All six of the students in cohort I earned bachelor’s STEM degrees at FVSU and transferred to a partnering university. Five of the six who transferred to a partnering university earned the second bachelor’s degree. One student transferred from a partnering university to a non-partner university to continue pursuit of the second degree. All students in cohort I had internships, attended professional meetings, and made presentations. Cohort II enrolled the fall-semester of 2020 in the midst of COVID epidemic. Cohort II was only funded for one year, 2020/2021. The DOE Office of Economic Impact and Diversity informed the PI that there would not be any funding for the fifth and final year (2021/2022) of the project. All six of the students in cohort II earned bachelor’s STEM degrees from FVSU and transferred to one of CDEP’s partnering universities. One of the students in cohort II graduate with an MS degree spring of 2023. Currently, four are pursuing graduate degrees at partnering universities and one withdrew from a partnering school because health reasons. Students in cohort II opportunities to participate in internship were negatively impact by the COVID epidemic (2020-2022). Of the 12 scholarship participants, 8 were females. Fifty percent of the males pursued second degrees in Engineering while only twenty-five percent of the females chose Engineering as their second degree.
42 ENGINEERING↗
The global warming hiatus: Slowdown or redistribution?
Abstract Global mean surface temperatures ( GMST ) exhibited a smaller rate of warming during 1998–2013, compared to the warming in the latter half of the 20th Century. Although, not a “true” hiatus in the strict definition of the word, this has been termed the “global warming hiatus” by IPCC (2013). There have been other periods that have also been defined as the “hiatus” depending on the analysis. There are a number of uncertainties and knowledge gaps regarding the “hiatus.” This report reviews these issues and also posits insights from a collective set of diverse information that helps us understand what we do and do not know. One salient insight is that the GMST phenomenon is a surface characteristic that does not represent a slowdown in warming of the climate system but rather is an energy redistribution within the oceans. Improved understanding of the ocean distribution and redistribution of heat will help better monitor Earth's energy budget and its consequences. A review of recent scientific publications on the “hiatus” shows the difficulty and complexities in pinpointing the oceanic sink of the “missing heat” from the atmosphere and the upper layer of the oceans, which defines the “hiatus.” Advances in “hiatus” research and outlooks (recommendations) are given in this report.
Yan, Xiao‐Hai↗
Developing a strategy for the national coordinated soil moisture monitoring network
Soil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA's National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national-scale coordination for soil moisture monitoring in the United States.
54 ENVIRONMENTAL SCIENCES↗
Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data
High-resolution (HR) MS instruments recording HR-full scan allow analysts to go further beyond pre-acquisition choices. Untargeted acquisition can reveal unexpected compounds or concentrations and can be performed for preliminary diagnosis attempt. Then, revealed compounds will have to be identified for interpretations. Whereas the need of reference standards is mandatory to confirm identification, the diverse information collected from HRMS allows identifying unknown compounds with relatively high degree of confidence without reference standards injected in the same analytical sequence. However, there is a necessity to evaluate the degree of confidence in putative identifications, possibly before further targeted analyses. This is why a confidence scale and a score in the identification of (non-peptidic) known-unknown, defined as compounds with entries in database, is proposed for (LC-) HRMS data. The scale is based on two representative documents edited by the European Commission (2007/657/EC) and the Metabolomics Standard Initiative (MSI), in an attempt to build a bridge between the communities of metabolomics and screening labs. With this confidence scale, an identification (ID) score is determined as [a number, a letter, and a number] (e.g., 2D3), from the following three criteria: I, a General Identification Category (1, confirmed, 2, putatively identified, 3, annotated compounds/classes, and 4, unknown); II, a Chromatography Class based on the relative retention time (from the narrowest tolerance, A, to no chromatographic references, D); and III, an Identification Point Level (1, very high, 2, high, and 3, normal level) based on the number of identification points collected. Three putative identification examples of known-unknown will be presented. .
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Effect of image resolution on automated classification of chest X-rays
Deep learning (DL) models have received much attention lately for their ability to achieve expert-level performance on the accurate automated analysis of chest X-rays (CXRs). Recently available public CXR datasets include high resolution images, but state-of-the-art models are trained on reduced size images due to limitations on graphics processing unit memory and training time. As computing hardware continues to advance, it has become feasible to train deep convolutional neural networks on high-resolution images without sacrificing detail by downscaling. This study examines the effect of increased resolution on CXR classification performance. We used the publicly available MIMIC-CXR-JPG dataset, comprising 377,110 high resolution CXR images for this study. We applied image downscaling from native resolution to 2048 × 2048 pixels, 1024 × 1024 pixels, 512 × 512 pixels, and 256 × 256 pixels and then we used the DenseNet121 and EfficientNet-B4 DL models to evaluate clinical task performance using these four downscaled image resolutions. We find that while some clinical findings are more reliably labeled using high resolutions, many other findings are actually labeled better using downscaled inputs. We qualitatively verify that tasks requiring a large receptive field are better suited to downscaled low resolution input images, by inspecting effective receptive fields and class activation maps of trained models. Lastly, we show that stacking an ensemble across resolutions outperforms each individual learner at all input resolutions while providing interpretable scale weights, indicating that diverse information is extracted across resolutions.
47 OTHER INSTRUMENTATION↗
The Second Flight of the Sunrise Balloon-borne Solar Observatory: Overview of Instrument Updates, the Flight, the Data, and First Results
The Sunrise balloon-borne solar observatory, consisting of a 1 m aperture telescope that provides a stabilized image to a UV filter imager and an imaging vector polarimeter, carried out its second science flight in 2013 June. It provided observations of parts of active regions at high spatial resolution, including the first high-resolution images in the Mg ii k line. The obtained data are of very high quality, with the best UV images reaching the diffraction limit of the telescope at 3000 Å after Multi-Frame Blind Deconvolution reconstruction accounting for phase-diversity information. Here a brief update is given of the instruments and the data reduction techniques, which includes an inversion of the polarimetric data. Mainly those aspects that evolved compared with the first flight are described. A tabular overview of the observations is given. In addition, an example time series of a part of the emerging active region NOAA AR 11768 observed relatively close to disk center is described and discussed in some detail. The observations cover the pores in the trailing polarity of the active region, as well as the polarity inversion line where flux emergence was ongoing and a small flare-like brightening occurred in the course of the time series. The pores are found to contain magnetic field strengths ranging up to 2500 G, and while large pores are clearly darker and cooler than the quiet Sun in all layers of the photosphere, the temperature and brightness of small pores approach or even exceed those of the quiet Sun in the upper photosphere.
79 ASTRONOMY AND ASTROPHYSICS↗
WEST Diversity Panel GBS information (Ferguson et al., 2020)
Genotyping by sequencing information (5,512,653 SNPs on 850 individuals [842 unique]) used in the manuscript "Machine learning enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions" (Ferguson et al., 2020) DOI:10.1101/2020.11.02.365213.
09 BIOMASS FUELS↗
Mycobacterium Phage Butters-Encoded Proteins Contribute to Host Defense against Viral Attack [plus supplemental information]
A diverse set of prophage-mediated mechanisms protecting bacterial hosts from infection has been recently uncovered within cluster N mycobacteriophages isolated on the host, Mycobacterium smegmatis mc 2 155. In that context, we unveil a novel defense mechanism in cluster N prophage Butters. By using bioinformatics analyses, phage plating efficiency experiments, microscopy, and immunoprecipitation assays, we show that Butters genes located in the central region of the genome play a key role in the defense against heterotypic viral attack. Our study suggests that a two-component system, articulated by interactions between protein products of genes 30 and 31, confers defense against heterotypic phage infection by PurpleHaze (cluster A/subcluster A3) or Alma (cluster A/subcluster A9) but is insufficient to confer defense against attack by the heterotypic phage Island3 (cluster I/subcluster I1). Therefore, based on heterotypic phage plating efficiencies on the Butters lysogen, additional prophage genes required for defense are implicated and further show specificity of prophage-encoded defense systems. IMPORTANCE: Many sequenced bacterial genomes, including those of pathogenic bacteria, contain prophages. Some prophages encode defense systems that protect their bacterial host against heterotypic viral attack. Understanding the mechanisms undergirding these defense systems is crucial to appreciate the scope of bacterial immunity against viral infections and will be critical for better implementation of phage therapy that would require evasion of these defenses. Furthermore, such knowledge of prophage-encoded defense mechanisms may be useful for developing novel genetic tools for engineering phage-resistant bacteria of industrial importance.
59 BASIC BIOLOGICAL SCIENCES↗
Comprehensive, Quantitative Risk Assessment of CO 2 Geologic Sequestration (Final Technical Report)
A Quantitative Failure Modes and Effects Analysis (QFMEA) was developed to conduct comprehensive, quantitative risk assessments on CO 2 capture, transportation, and sequestration or use in deep saline aquifers, enhanced oil recovery operations, or enhanced coal bed methane operations. The model identifies and characterizes potential risks; identifies the likely failure modes, causes, effects and methods of detection; lists possible risk prevention and risk mitigation steps; estimates potential damage recovery costs, mitigation costs and costs savings resulting from mitigation; and ranks (prioritizes) risks according to the probability of failure, the severity of failure, the difficulty of early failure detection and the potential for fatalities. The QFMEA model generates the necessary information needed for effective project risk management. Diverse project information can be integrated into a concise, common format that allows comprehensive, quantitative analysis, by a cross-functional team of experts, to determine: What can possibly go wrong? How much will damage recovery cost? How can it be prevented or mitigated? What is the cost savings or benefit of prevention or mitigation? Which risks should be given highest priority for resolution? The QFMEA model can be tailored to specific projects and is applicable to new projects as well as mature projects. The model can be revised and updated as new information comes available. It accepts input from multiple sources, such as literature searches, site characterization, field data, computer simulations, analogues, process influence diagrams, probability density functions, financial analysis models, cost factors, and heuristic best practices manuals, and converts the information into a standardized format in an Excel spreadsheet. Process influence diagrams, geologic models, financial models, cost factors and an insurance schedule were developed to support the QFMEA model. Comprehensive, quantitative risk assessments were conducted on three (3) sites using the QFMEA model: (1) SACROC Northern Platform CO 2 -EOR Site in the Permian Basin, Scurry County, TX, (2) Pump Canyon CO 2 -ECBM Site in the San Juan Basin, San Juan County, NM, and (3) Farnsworth Unit CO 2 -EOR Site in the Anadarko Basin, Ochiltree County, TX. The sites were sufficiently different from each other to test the robustness of the QFMEA model.
54 ENVIRONMENTAL SCIENCES↗
Interactive visual comparison of multimedia data through type-specific views
Analysts who work with collections of multimedia to perform information foraging understand how difficult it is to connect information across diverse sets of mixed media. The wealth of information from blogs, social media, and news sites often can provide actionable intelligence; however, many of the tools used on these sources of content are not capable of multimedia analysis because they only analyze a single media type. As such, analysts are taxed to keep a mental model of the relationships among each of the media types when generating the broader content picture. To address this need, we have developed Canopy, a novel visual analytic tool for analyzing multimedia. Canopy provides insight into the multimedia data relationships by exploiting the linkages found in text, images, and video co-occurring in the same document and across the collection. Canopy connects derived and explicit linkages and relationships through multiple connected visualizations to aid analysts in quickly summarizing, searching, and browsing collected information to explore relationships and align content. In this paper, we will discuss the features and capabilities of the Canopy system and walk through a scenario illustrating how this system might be used in an operational environment. Keywords: Multimedia (Image/Video/Music) Visualization.
Burtner, Edwin R.↗
Antiviral Strategies Against SARS-CoV-2: A Systems Biology Approach
The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions.
Teixeira Prates, Erica↗
Characterizing Time Series Data Diversity for Wind Forecasting
Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the important information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.
big data visualization↗
Characterizing Time Series Data Diversity for Wind Forecasting: Preprint
Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the important information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.
17 WIND ENERGY↗
From microbial diversity to functional potential using dimensionality reduction
The high dimensionality of microbial diversity data from ‘omics observations can be reduced using Machine Learning, with many recent studies showcasing ML utility for exploratory ecological feature finding and process prediction. Here, we compare the Self Organizing Map (SOM) dimensionality reduction method to the well-documented sample-based Principal Coordinate Analysis (PCoA) and taxa-based Weighted Gene Correlation Network Analysis (WGCNA) using near daily 16S rRNA gene amplicon sequencing data from the 2019 to 2020 MOSAiC International Arctic Drift Expedition. We then map k-means clustering outputs from each method to available metagenomes, extracting functionally distinct seasonal microbial ecotypes in the surface Arctic Ocean. Our results indicate the SOM method better represented expected seasonal transitions and identified a greater number of metabolically distinct functional groups than the more traditional PCoA ordination. Ultimately, we identified four community ecotypes with distinct taxonomic and functional cut-offs driven by seasonality, water mass, and substrate turnover, highlighting the importance of succession in functional diversity for the central Arctic Ocean. These results reinforce ML dimensionality reduction as a meaningful translator in the mining of historical amplicon datasets to address modern mechanistic questions and potentially provide ’omics informed ecotype diversity to leverage in mechanistic biogeochemical models.
Arctic Ocean↗
Accelerating the Structure Exploration of Diverse Bi–Pt Nanoclusters via Physics‐Informed Machine Learning Potential and Particle Swarm Optimization
Bimetallic Bi–Pt nanoclusters exhibit diverse structural motifs, including core-shell, Janus, and mixed alloy configurations, due to the unique bonding characteristics between Bi and Pt atoms. Using density functional theory refinements from ChIMES physically machine-learned potential and CALYPSO particle swarm optimization global searches, 34 Bi20-Pt20 nanoclusters are systematically classified. The results reveal that Bi atoms predominantly occupy surface sites, driven by charge transfer effects. Cohesive energy trends alone prove insufficient for structure differentiation, necessitating a data-driven approach employing principal component analysis and K-means clustering. Furthermore, vibrational, electronic, and infrared spectral analyses provide additional insights into structure-property relationships. The findings offer an original framework for the automated classification and analysis of bimetallic nanoclusters, enhancing the understanding of their stability and functional properties.
bimetallic nanoparticles↗
Empirical Performance Analysis of HPC Applications with Portable Hardware Counter Metrics [Thesis]
In this dissertation, we demonstrate that it is possible to develop methods of empirical hardware-counter-based performance analysis for scientific applications running on diverse types of CPUs. Although hardware counters have been used in performance analysis for at least 30 years, the methods used are still limited to particular CPU vendors or even particular generations of CPUs from the same vendor. Our motivating hypothesis is that hardware counter-based measurements could be developed to provide consistent performance information on diverse CPU types. This dissertation proves the hypothesis was correct by demonstrating one such set of metrics.
97 MATHEMATICS AND COMPUTING↗