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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

A distributed computing model for telemetry data processing

We present a new approach to distributing processed telemetry data among spacecraft flight controllers within the control centers at NASA's Johnson Space Center. This approach facilitates the development of application programs which integrate spacecraft-telemetered data and ground-based synthesized data, then distributes this information to flight controllers for analysis and decision-making. The new approach combines various distributed computing models into one hybrid distributed computing model. The model employs both client-server and peer-to-peer distributed computing models cooperating to provide users with information throughout a diverse operations environment. Specifically, it provides an attractive foundation upon which we are building critical real-time monitoring and control applications, while simultaneously lending itself to peripheral applications in playback operations, mission preparations, flight controller training, and program development and verification. We have realized the hybrid distributed computing model through an information sharing protocol. We shall describe the motivations that inspired us to create this protocol, along with a brief conceptual description of the distributed computing models it employs. We describe the protocol design in more detail, discussing many of the program design considerations and techniques we have adopted. Finally, we describe how this model is especially suitable for supporting the implementation of distributed expert system applications.

Barry, Matthew R.↗
DOE OSTI2018

Impact of hydrologic boundaries on microbial planktonic and biofilm communities in shallow terrestrial subsurface environments

Subsurface environments contain a large proportion of planetary microbial biomass and harbor diverse communities responsible for mediating biogeochemical cycles important to groundwater used by human society for consumption, irrigation, agriculture and industry. Within the saturated zone, capillary fringe and vadose zones, microorganisms can reside in two distinct phases (planktonic or biofilm), and significant differences in community composition, structure and activity between free-living and attached communities are commonly accepted. However, largely due to sampling constraints and the challenges of working with solid substrata, the contribution of each phase to subsurface processes is largely unresolved. Here, we synthesize current information on the diversity and activity of shallow freshwater subsurface habitats, discuss the challenges associated with sampling planktonic and biofilm communities across spatial, temporal and geological gradients, and discuss how biofilms may be constrained within shallow terrestrial subsurface aquifers. We suggest that merging traditional activity measurements and sequencing/-omics technologies with hydrological parameters important to sediment biofilm assembly and stability will help delineate key system parameters. Ultimately, integration will enhance our understanding of shallow subsurface ecophysiology in terms of bulk-flow through porous media and distinguish the respective activities of sessile microbial communities from more transient planktonic communities to ecosystem service and maintenance.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Digitizing Named Entities Found Within Letters of Agreement

Letters of Agreement (LOAs) are text-based air traffic control documents that contain procedures and actions agreed upon by the different parties, typically two or more FAA facilities, that are subject to an agreement. The documents contain among other things generic constraints, which are explicit and implicit combinations of procedures that limit a flight’s trajectory and affects pilot actions. For example, a controller may be required, to assign a specific altitude to an aircraft crossing the boundary between two airspaces. Although LOA generic constraints directly impact the trajectory of an aircraft, they are not currently available in a digital form that can be used for (or directly ingested into automated) flight planning. Instead, the constraints are manually input into an onboard or ground based system. LOA documents are primarily stored at a controlling facility and the generic constraints are implemented by experienced air traffic controllers and pilots primarily using voice instructions. This increases the workload of the controllers, likelihood of error (e.g., due to noisy communication) and makes it impractical for implementation with unmanned aircraft. Therefore, steps must be taken to make existing constraints machine interpretable to enable e.g., automated handoffs which in turn would reduce controller workload. With recent advances in natural language processing, especially the rise in digitization of text documents (e.g., medical documents) and automated extraction of information therein, it is now possible to extract flight specific constraints from LOAs. The goal of this work is to digitize named entities through a combination of natural language processing tasks: named entity disambiguation, toponym resolution, and numeric parsing to extract general constraint components contained within LOAs, herein referred to as Entity Enhancement (EE). Starting with a small list of named entities (e.g., ARTCC, Tower, Altitude and Speed), EE can extract the named entities while simultaneously converting the string-based output into a digital format using an ensemble of processes like rule-based gazetteers and syntactic-lexical patterns. The digital format contains a diverse set of information based on the entity label in question, ranging from standardized facility names to units of measure (e.g., feet) and other numeric information. Upon validating our approach using a truth dataset, we show an overall F1-Score of 0.71 for the extraction process. Looking beyond entity enhancement, we are also working towards the goal of completely digitizing the general constraints by performing EE and fitting them into a standardized exchange model (XM) such as the Aeronautical Information Exchange Model (AIXM). This will allow for easy distribution and dissemination of LOA constraints to air users, better searchability within documents, and enable ingestion into automated flight planning. Finally, we show a preliminary version of the proposed XM architecture and demonstrate how the model can be populated from the EE output.

Stephen S. B. Clarke↗
NASA NTRS2022

Vegetation and vantage point influence visibility across diverse ecosystems: Implications for animal ecology

Visual information can influence animal behavior and habitat use in diverse ways. Visibility is the property that relates 3D habitat structure to accessibility of visual information. Despite the importance of visibility in animal ecology, this property remains largely unstudied. Our objective was to assess how habitat structure from diverse environments and animal position within that structure can influence visibility. We gathered terrestrial lidar data (1 cm at 10 m) in four ecosystems (forest, shrub-steppe, prairie, and desert) to characterize viewsheds (i.e., estimates of visibility based on spatially explicit sightlines) from multiple vantage points. Both ecosystem-specific structure and animal position influenced potential viewsheds. Generally, as height of the vantage point above the ground increased, viewshed extent also increased, but the relationships were not linear. In low-structure ecosystems (prairie, shrub-steppe, and desert), variability in viewsheds decreased as vantage points increased to heights above the vegetation canopy. In the forest, however, variation in viewsheds was highest at intermediate heights, and markedly lower at the lowest and highest vantage points. These patterns are likely linked to the amount, heterogeneity, and distribution of vegetation structure occluding sightlines. Our work is the first to apply a new method that can be used to estimate viewshed properties relevant to animals (i.e., viewshed extent and variability). We demonstrate that these properties differ across terrestrial landscapes in complex ways that likely influence many facets of animal ecology and behavior.

Rachel Stein↗
DOE OSTI2015

Metagenomic analysis of planktonic microbial consortia from a non-tidal urban-impacted segment of James River

Knowledge of the diversity and ecological function of the microbial consortia of James River in Virginia, USA, is essential to developing a more complete understanding of the ecology of this model river system. Metagenomic analysis of James River's planktonic microbial community was performed for the first time using an unamplified genomic library and a 16S rDNA amplicon library prepared and sequenced by Ion PGM and MiSeq, respectively. From the 0.46-Gb WGS library (GenBank:SRR1146621; MG-RAST:4532156.3), 4 x 10 6 reads revealed >3 x 10 6 genes, 240 families of prokaryotes, and 155 families of eukaryotes. From the 0.68-Gb 16S library (GenBank:SRR2124995; MG-RAST:4631271.3; EMB:2184), 4 x 10 6 reads revealed 259 families of eubacteria. Results of the WGS and 16S analyses were highly consistent and indicated that more than half of the bacterial sequences were Proteobacteria , predominantly Comamonadaceae . The most numerous genera in this group were Acidovorax (including iron oxidizers, nitrotolulene degraders, and plant pathogens), which accounted for 10 % of assigned bacterial reads. Polaromonas were another 6 % of all bacterial reads, with many assignments to groups capable of degrading polycyclic aromatic hydrocarbons. Albidiferax (iron reducers) and Variovorax (biodegraders of a variety of natural biogenic compounds as well as anthropogenic contaminants such as polycyclic aromatic hydrocarbons and endocrine disruptors) each accounted for an additional 3% of bacterial reads. Comparison of these data to other publically-available aquatic metagenomes revealed that this stretch of James River is highly similar to the upper Mississippi River, and that these river systems are more similar to aquaculture and sludge ecosystems than they are to lakes or to a pristine section of the upper Amazon River. Altogether, these analyses exposed previously unknown aspects of microbial biodiversity, documented the ecological responses of microbes to urban effects, and revealed the noteworthy presence of 22 human-pathogenic bacterial genera (e.g., Enterobacteriaceae , pathogenic Pseudomonadaceae , and ‘Vibrionales' ) and 6 pathogenic eukaryotic genera (e.g., Trypanosomatidae and Vahlkampfiidae). This information about pathogen diversity may be used to promote human epidemiological studies, enhance existing water quality monitoring efforts, and increase awareness of the possible health risks associated with recreational use of James River.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A sorghum practical haplotype graph facilitates genome‐wide imputation and cost‐effective genomic prediction

Abstract Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to accelerate cultivar development. To help with this, we developed a Sorghum bicolor Practical Haplotype Graph (PHG) pangenome database that stores haplotypes and variant information. We developed two PHGs in sorghum that were used to identify genome‐wide variants for 24 founders of the Chibas sorghum breeding program from 0.01x sequence coverage. The PHG called single nucleotide polymorphisms (SNPs) with 5.9% error at 0.01x coverage—only 3% higher than PHG error when calling SNPs from 8x coverage sequence. Additionally, 207 progenies from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes were imputed from PHG parental haplotypes and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from .57–.73 and are similar to prediction accuracies obtained with genotyping‐by‐sequencing or targeted amplicon sequencing (rhAmpSeq) markers. This study demonstrates the use of a sorghum PHG to impute SNPs from low‐coverage sequence data and shows that the PHG can unify genotype calls across multiple sequencing platforms. By reducing input sequence requirements, the PHG can decrease the cost of genotyping, make GS more feasible, and facilitate larger breeding populations. Our results demonstrate that the PHG is a useful research and breeding tool that maintains variant information from a diverse group of taxa, stores sequence data in a condensed but readily accessible format, unifies genotypes across genotyping platforms, and provides a cost‐effective option for genomic selection.

Jensen, Sarah E.↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2025

Machine learning reveals genes impacting oxidative stress resistance across yeasts

Reactive oxygen species (ROS) are highly reactive molecules encountered by yeasts during routine metabolism and during interactions with other organisms, including host infection. Here, we characterize the variation in resistance to the ROS-inducing compound tert -butyl hydroperoxide across the ancient yeast subphylum Saccharomycotina and use machine learning (ML) to identify gene families whose sizes are predictive of ROS resistance. The most predictive features are enriched in gene families related to cell wall organization and include two reductase gene families. We estimate the quantitative contributions of features to each species’ classification to guide experimental validation and show that overexpression of the old yellow enzyme (OYE) reductase increases ROS resistance in Kluyveromyces lactis , while Saccharomyces cerevisiae mutants lacking multiple mannosyltransferase-encoding genes are hypersensitive to ROS. Altogether, this work provides a framework for how ML can uncover genetic mechanisms underlying trait variation across diverse species and inform trait manipulation for clinical and biotechnological applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Assessing the economic viability of unconventional rare earth element feedstocks

Sourcing rare earth elements (REEs) from unconventional feedstocks has substantial environmental and societal-economic benefits, but its economic viability remains unclear. Here, we propose a framework for assessing the economic viability of unconventional REE feedstocks. Here, we first develop a database of capital and operating expenses for REE recovery and establish consistent process unit costs for common stages in the conventional supply chain. We then use market prices to develop well-defined methods for determining diverse product values. Finally, we report the effects of economies of scale and feedstock purity on capital and operating costs. These tools can reduce the uncertainty in the assessment of the economic viability of producing REE from unconventional feedstocks, determine the value of innovative pathways for REE production from diverse sources, and inform research priorities across a wide range of REE product development stages, contributing to a sustainable REE supply.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

LAMP detection assays for boxwood blight pathogens: A comparative genomics approach

Rapid and accurate molecular diagnostic tools are critical to efforts to minimize the impact and spread of emergent pathogens. The identification of diagnostic markers for novel pathogens presents several challenges, especially in the absence of information about population diversity and where genetic resources are limited. The objective of this study was to use comparative genomics datasets to find unique target regions suitable for the diagnosis of two fungal species causing a newly emergent blight disease of boxwood. Candidate marker regions for loop-mediated isothermal amplification (LAMP) assays were identified from draft genomes of Calonectria henricotiae and C. pseudonaviculata , as well as three related species not associated with this disease. To increase the probability of identifying unique targets, we used three approaches to mine genome datasets, based on (i) unique regions, (ii) polymorphisms, and (iii) presence/absence of regions across datasets. From a pool of candidate markers, we demonstrate LAMP assay specificity by testing related fungal species, common boxwood pathogens, and environmental samples containing 445 diverse fungal taxa. In conclusion, this comparative-genomics-based approach to the development of LAMP diagnostic assays is the first of its kind for fungi and could be easily applied to diagnostic marker development for other newly emergent plant pathogens.

1st report↗
DOE OSTI2020

Measurements of a quantum bulk acoustic resonator using a superconducting qubit

Phonon modes at microwave frequencies can be cooled to their quantum ground state using conventional cryogenic refrigeration, providing a convenient way to study and manipulate quantum states at the single phonon level. Phonons are of particular interest because mechanical deformations can mediate interactions with a wide range of different quantum systems, including solid-state defects, superconducting qubits, and optical photons when using optomechanically active constructs. Phonons, thus, hold promise for quantum-focused applications as diverse as sensing, information processing, and communication. Here, we describe a piezoelectric quantum bulk acoustic resonator (QBAR) with a 4.88 GHz resonant frequency, which, at cryogenic temperatures, displays large electromechanical coupling strength combined with a high intrinsic mechanical quality factor, Q i ≈ 4.3 × 10 4 . Using a recently developed flip-chip technique, we couple this QBAR resonator to a superconducting qubit on a separate die and demonstrate the quantum control of the mechanics in the coupled system. Furthermore, this approach promises a facile and flexible experimental approach to quantum acoustics and hybrid quantum systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

PrimerDesign-M: A multiple-alignment based multiple-primer design tool for walking across variable genomes

Analyses of entire viral genomes or mtDNA requires comprehensive design of many primers across their genomes. In addition, simultaneous optimization of several DNA primer design criteria may improve overall experimental efficiency and downstream bioinformatic processing. To achieve these goals, we developed PrimerDesign-M. It includes several options for multiple-primer design, allowing researchers to efficiently design walking primers that cover long DNA targets, such as entire HIV-1 genomes, and that optimizes primers simultaneously informed by genetic diversity in multiple alignments and experimental design constraints given by the user. PrimerDesign-M can also design primers that include DNA barcodes and minimize primer dimerization. PrimerDesign-M finds optimal primers for highly variable DNA targets and facilitates design flexibility by suggesting alternative designs to adapt to experimental conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Design of Electrostatic Aberration Correctors for Scanning Transmission Electron Microscopy

Abstract In a scanning transmission electron microscope (STEM), producing a high-resolution image generally requires an electron beam focused to the smallest point possible. However, the magnetic lenses used to focus the beam are unavoidably imperfect, introducing aberrations that limit resolution. Modern STEMs overcome this by using hardware aberration correctors comprised of many multipole elements, but these devices are complex, expensive, and can be difficult to tune. We demonstrate a design for an electrostatic phase plate that can act as an aberration corrector. The corrector is comprised of annular segments, each of which is an independent two-terminal device that can apply a constant or ramped phase shift to a portion of the electron beam. We show the improvement in image resolution using an electrostatic corrector. Engineering criteria impose that much of the beam within the probe-forming aperture be blocked by support bars, leading to large probe tails for the corrected probe that sample the specimen beyond the central lobe. We also show how this device can be used to create other STEM beam profiles such as vortex beams and probes with a high degree of phase diversity, which improve information transfer in ptychographic reconstructions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Machine learning approaches for crystallographic classification from synthetic 2D X-ray diffraction data

Crystallographic structure identification is crucial for understanding material properties; however, current methodologies often depend on labor-intensive and time-consuming analyses of 2D X-ray diffraction (XRD) patterns. To address these limitations, this study employs synthetic 2D XRD patterns combined with deep learning (DL) techniques to enable automated and high-throughput classification of the seven crystal systems and 230 space groups. We introduce the novel Auto Diffraction Pipeline, designed to generate synthetic 2D XRD spot patterns from crystallographic information files under diverse conditions, including varying zone axes, atomic substitution, atomic depletion and mechanical loading. These conditions enhance the realism of synthetic data, mitigating the scarcity of experimental datasets and enabling the creation of large representative training sets. Convolutional neural networks were trained and validated on these synthetic datasets to classify crystallographic structures across multiple scenarios. Our results demonstrate that integrating synthetic 2D XRD patterns with DL facilitates rapid, accurate and automated crystallographic classification, promoting the wider adoption of data-driven approaches in materials science.

Shahnazari, Ayoub [Univ. of Rochester, NY (United ↗
DOE OSTI2019

Comparative Biochemical and Structural Analysis of Novel Cellulose Binding Proteins ($ T\overline{a}pirins\ $) from Extremely Thermophilic Caldicellulosiruptor Species

Genomes of extremely thermophilic Caldicellulosiruptor species encode novel cellulose binding proteins, called $ t\overline{a}pirins\ $, located proximate to the type IV pilus locus. The C-terminal domain of Caldicellulosiruptor kronotskyensis $ t\overline{a}pirin\ $ 0844 (Calkro_0844) is structurally unique and has a cellulose binding affinity akin to that seen with family 3 carbohydrate binding modules (CBM3s). Here, full-length and C-terminal versions of $ t\overline{a}pirins\ $ from Caldicellulosiruptor bescii (Athe_1870), Caldicellulosiruptor hydrothermalis (Calhy_0908), Caldicellulosiruptor kristjanssonii (Calkr_0826), and Caldicellulosiruptor naganoensis (NA10_0869) were produced recombinantly in Escherichia coli and compared to Calkro_0844. All five $ t\overline{a}pirins\ $ bound to microcrystalline cellulose, switchgrass, poplar, and filter paper but not to xylan. Densitometry analysis of bound protein fractions visualized by SDS-PAGE revealed that Calhy_0908 and Calkr_0826 (from weakly cellulolytic species) associated with the cellulose substrates to a greater extent than Athe_1870, Calkro_0844, and NA10_0869 (from strongly cellulolytic species). Perhaps this relates to their specific needs to capture glucans released from lignocellulose by cellulases produced in Caldicellulosiruptor communities. Calkro_0844 and NA10_0869 share a higher degree of amino acid sequence identity (>80% identity) with each other than either does with Athe_1870 (~50%). The levels of amino acid sequence identity of Calhy_0908 and Calkr_0826 to Calkro_0844 were only 16% and 36%, respectively, although the three-dimensional structures of their C-terminal binding regions were closely related. Unlike the parent strain, C. bescii mutants lacking the $ t\overline{a}pirin\ $ genes did not bind to cellulose following short-term incubation, suggesting a role in cell association with plant biomass. Given the scarcity of carbohydrates in neutral terrestrial hot springs, $ t\overline{a}pirins\ $ likely help scavenge carbohydrates from lignocellulose to support growth and survival of Caldicellulosiruptor species.The mechanisms by which microorganisms attach to and degrade lignocellulose are important to understand if effective approaches for conversion of plant biomass into fuels and chemicals are to be developed. Caldicellulosiruptor species grow on carbohydrates from lignocellulose at elevated temperatures and have biotechnological significance for that reason. Novel cellulose binding proteins, called $ t\overline{a}pirins\ $, are involved in the way that Caldicellulosiruptor species interact with microcrystalline cellulose, and additional information about the diversity of these proteins across the genus, including binding affinity and three-dimensional structural comparisons, is provided here.

09 BIOMASS FUELS↗
DOE OSTI2023

Genome-sequenced bacterial collection from sorghum aerial root mucilage

ABSTRACT A collection of 47 bacteria isolated from the mucilage of aerial roots of energy sorghum is available at the Great Lakes Bioenergy Research Center, Michigan State University, Michigan, USA. We enriched bacteria with putative plant-beneficial phenotypes and included information on phenotypic diversity, taxonomy, and whole genome sequences.

Mechan-Llontop, Marco E.↗
DOE OSTI2017

Virioplankton Assemblage Structure in the Lower River and Ocean Continuum of the Amazon

ABSTRACT The Amazon River watershed and its associated plume comprise a vast continental and oceanic area. The microbial activities along this continuum contribute substantially to global carbon and nutrient cycling, and yet there is a dearth of information on the diversity, abundance, and possible roles of viruses in this globally important river. The aim of this study was to elucidate the diversity and structure of virus assemblages of the Amazon River-ocean continuum. Environmental viral DNA sequences were obtained for 12 locations along the river’s lower reach ( n = 5) and plume ( n = 7). Sequence assembly yielded 29,358 scaffolds, encoding 82,546 viral proteins, with 15 new complete viral genomes. Despite the spatial connectivity mediated by the river, virome analyses and physical-chemical water parameters clearly distinguished river and plume ecosystems. Bacteriophages were ubiquitous in the continuum and were more abundant in the transition region. Eukaryotic viruses occurred mostly in the river, while the plume had more viruses of autotrophic organisms ( Prochlorococcus , Synechococcus ) and heterotrophic bacteria ( Pelagibacter ). The viral families Microviridae and Myoviridae were the most abundant and occurred throughout the continuum. The major functions of the genes in the continuum involved viral structures and life cycles, and viruses from plume locations and Tapajós River showed the highest levels of functional diversity. The distribution patterns of the viral assemblages were defined not only by the occurrence of possible hosts but also by water physical and chemical parameters, especially salinity. The findings presented here help to improve understanding of the possible roles of viruses in the organic matter cycle along the river-ocean continuum. IMPORTANCE The Amazon River forms a vast plume in the Atlantic Ocean that can extend for more than 1,000 km. Microbial communities promote a globally relevant carbon sink system in the plume. Despite the importance of viruses for the global carbon cycle, the diversity and the possible roles of viruses in the Amazon are poorly understood. The present work assesses, for the first time, the abundance and diversity of viruses simultaneously in the river and ocean in order to elucidate their possible roles. DNA sequence assembly yielded 29,358 scaffolds, encoding 82,546 viral proteins, with 15 new complete viral genomes from the 12 river and ocean locations. Viral diversity was clearly distinguished by river and ocean. Bacteriophages were the most abundant and occurred throughout the continuum. Viruses that infect eukaryotes were more abundant in the river, whereas phages appeared to have strong control over the host prokaryotic populations in the plume.

Amazon↗
DOE OSTI2018

Visual Analytics of Heterogeneous Data Using Hypergraph Learning

For real-world learning tasks (e.g., classification), graph-based models are commonly used to fuse the information distributed in diverse data sources, which can be heterogeneous, redundant, and incomplete. Thesemodels represent the relations in different datasets as pairwise links. However, these links cannot deal with high-order relations which connect multiple objects (e.g., in public health datasets, more than two patient groups admitted by the same hospital in 2014). In this article, we propose a visual analytics approach forthe classification on heterogeneous datasets using the hypergraph model. The hypergraph is an extension to traditional graphs in which a hyperedge connects multiple vertices instead of just two. We model various high-order relations in heterogeneous datasets as hyperedges and fuse different datasets with a unified hypergraph structure. We use the hypergraph learning algorithm for predicting missing labels in the datasets.To allow users to inject their domain knowledge into the model-learning process, we augment the traditional learning algorithm in a number of ways. In addition, we also propose a set of visualizations which enable the user to construct the hypergraph structure and the parameters of the learning model interactively during the analysis. Here, we demonstrate the capability of our approach via two real-world cases.

97 MATHEMATICS AND COMPUTING↗