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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

AI Data Quality Monitoring with Hydra

Hydra is an extensible framework for training and managing AI for near real time monitoring that aims to replace the tedious and repetitive data quality monitoring activities the shift crew and online monitoring coordinator typically perform. It continuously scans incoming data in the form of monitoring plots for signs of problems, flagging them for human review. A web app was developed such that experts can efficiently label images for training. Labels are stored in a database for use in training and model validation. Backed up by a comprehensive database, it utilizes an additional web based front-end for viewing the current monitoring status from anywhere in the world. The system has been in production use for the GlueX experiment at Jefferson Lab for more than 2 years with new features still under active development.

Britton, Thomas↗
NASA NTRS1992

Comparative assessment of psychomotor performance - Target prediction by humans and macaques (Macaca mulatta)

Although nonhuman primates such as rhesus monkeys (Macaca mulatta) have been useful models of many aspects of cognition and performance, it has been argued that, unlike humans, they may lack the capacity to respond as predictor-operators. Data from the present series of experiments undermine this claim, suggesting instead a continuity of predictive competency between humans and nonhuman primates. A prediction coefficient was devised to examine the degree to which each subject's response path approximated the optimal predictive strategy. Whereas human subjects (N= 30) generally predicted more accurately, rhesus monkeys (N= 10) also significantly anticipated the movements of the target in all conditions. It appears that humans and rhesus monkeys both exhibit the capacity to respond to where a stimulus is going.

Washburn, David A.↗
NASA NTRS1993

Performing speech recognition research with hypercard

The purpose of this paper is to describe a HyperCard-based system for performing speech recognition research and to instruct Human Factors professionals on how to use the system to obtain detailed data about the user interface of a prototype speech recognition application.

Shepherd, Chip↗
NASA NTRS1994

Evaluation of lens distortion errors using an underwater camera system for video-based motion analysis

Video-based motion analysis systems are widely employed to study human movement, using computers to capture, store, process, and analyze video data. This data can be collected in any environment where cameras can be located. One of the NASA facilities where human performance research is conducted is the Weightless Environment Training Facility (WETF), a pool of water which simulates zero-gravity with neutral buoyance. Underwater video collection in the WETF poses some unique problems. This project evaluates the error caused by the lens distortion of the WETF cameras. A grid of points of known dimensions was constructed and videotaped using a video vault underwater system. Recorded images were played back on a VCR and a personal computer grabbed and stored the images on disk. These images were then digitized to give calculated coordinates for the grid points. Errors were calculated as the distance from the known coordinates of the points to the calculated coordinates. It was demonstrated that errors from lens distortion could be as high as 8 percent. By avoiding the outermost regions of a wide-angle lens, the error can be kept smaller.

Poliner, Jeffrey↗
NASA NTRS1999

Autonomous Rovers for Mars Exploration

Rovers will play a critical role in the exploration of Mars. Near-term mission plans call for long traverses over unknown terrain, robust navigation and instrument placement, and reliable operations for extended periods of time. Longer-term missions may visit multiple science sites in a single day and perform opportunistic science data collection, as well as complex scouting, construction, and maintenance tasks in preparation for an eventual human presence. The Pathfinder mission demonstrated the potential for robotic Mars exploration but at the same time indicated the need for more rover autonomy. The highly ground-intensive control with infrequent communication and high latency limited the effectiveness of the Sojourner rover. When failures occurred, Sojourner often sat idle for extended periods of time, awaiting further commands from earth. In future missions, the tasks will be more complex and extended; hence there will be even more situations where things do not go exactly as planned. Significant advances in rover autonomy are needed to cope with increasing task complexity and greater execution uncertainty. Towards this end, we have designed an on-board executive architecture that incorporates robust operation, resource utilization, and failure recovery. In addition, we have designed ground tools to produce and refine contingent schedules that take advantage of the on-board architecture's flexible execution characteristics. Together, the on-board executive and the ground tools constitute an integrated rover autonomy architecture. This work draws from our experience with the Deep Space One autonomy experiment, with enhancements to ensure robust operation in the face of the unpredictable, complex environment that the rover will encounter on Mars. The rover autonomy architecture is currently being developed and deployed on the Marsokhod rover platform at NASA Ames Research Center. The capabilities of the rover autonomy architecture to support autonomous operations will be demonstrated concretely in upcoming field tests.

Anderson, Corin↗
NASA NTRS1995

Summary of a Crew-Centered Flight Deck Design Philosophy for High-Speed Civil Transport (HSCT) Aircraft

Past flight deck design practices used within the U.S. commercial transport aircraft industry have been highly successful in producing safe and efficient aircraft. However, recent advances in automation have changed the way pilots operate aircraft, and these changes make it necessary to reconsider overall flight deck design. Automated systems have become more complex and numerous, and often their inner functioning is partially or fully opaque to the flight crew. Recent accidents and incidents involving autoflight system mode awareness Dornheim, 1995) are an example. This increase in complexity raises pilot concerns about the trustworthiness of automation, and makes it difficult for the crew to be aware of all the intricacies of operation that may impact safe flight. While pilots remain ultimately responsible for mission success, performance of flight deck tasks has been more widely distributed across human and automated resources. Advances in sensor and data integration technologies now make far more information available than may be prudent to present to the flight crew.

Palmer, Michael T.↗
NASA NTRS2012

Data Comm Flight Deck Human-in-the-Loop Simulation

This presentation discusses an upcoming simulation for data comm in the terminal area. The purpose of the presentation is to provide the REDAC committee with a summary of some of the work in Data Comm that is being sponsored by the FAA. The focus of the simulation is upon flight crew human performance variables, such as crew procedures, timing and errors. The simulation is scheduled to be conducted in Sept 2012.

Lozito, Sandra↗
NASA NTRSDate not supplied

Prospects for Future Human Space Flight Missions to Near-Earth Asteroids

The forthcoming Near-Earth Object (NEO) Surveyor space telescope is a foundational asset designed to complete NASA’s congressionally-mandated goal of cataloging ≥90% of NEOs ≥140 meters in size as soon as practical, and discover Earth impactors far in advance. NEO Surveyor is also critical to near-Earth asteroid (NEA) exploration because it will find suitable low-Δv NEAs for robotic and human missions, often on Earth-like orbits with long synodic periods. Such NEAs have usually not been detected until imminent Earth launch opportunities because they were not observable until close to Earth. Discovering them far enough in advance to deploy missions requires a deep-space infrared survey telescope such as NEO Surveyor. In addition to posing hazards and being scientifically important, NEAs contain resources, such as water (OH), that could be utilized off-Earth. They also offer unique opportunities for the most ambitious human voyages ever undertaken. The Apollo program forever changed humanity’s perspective by showing us Earthrise from our Moon through human eyes. Crewed missions to NEAs will forever change our perspective again by showing us Earth as a distant point of light in the heavens as seen from an asteroid by astronauts. Planetary defense endeavors to understand asteroid and comet impact risks and develop mitigation capabilities. NEA exploration is synergistic in multiple ways. NEO Surveyor will discover and help characterize Earth impactors and accessible NEAs. Heavy-lift launch is highly enabling for both human NEA missions and planetary defense. Reconnaissance missions for planetary defense can also characterize NEAs prior to crewed missions. In situ resource utilization (ISRU) systems may be applicable to deflecting or destroying hazardous NEAs. NEA characterization data critically inform planetary defense efforts and indicate NEA types suitable for human exploration or ISRU. In 2010, NASA performed the Near-Earth Object (NEO) Human Space Flight (HSF) Accessible Targets Study (NHATS), creating an automated online system monitoring mission accessibility of NEAs 2 . NHATS database NEAs meet criteria that require less total mission Δv and/or round-trip mission duration than the Martian surface or even Mars orbit. There are currently 4,658 NHATS NEAs, and many rival or exceed lunar orbit/surface accessibility. Thus, NHATS NEAs could be explored by humans prior to attempting a Mars mission. Human missions to NEAs would test human-rated spacecraft systems with less cost and risk than Mars missions. Thus, human missions to NEAs are compelling in their own right while also providing prudent preparation for more demanding Mars missions. Lunar missions can offer similar opportunities, but they differ from NEA missions in important ways. Lunar missions pose less demanding psychological challenges to crew and ground staff. Earth light-time delay for communications is significantly longer during NEA missions. NEA missions are available that require more propulsion system consumables than lunar missions but less than Mars missions. In this paper, we summarize NEA accessibility for human missions, discuss motivations for such missions, survey knowledge about NEAs that informs future human missions, present exemplar mission opportunities to the currently known NHATS NEAs, and describe future work towards enabling human NEA missions.

Near-Earth asteroids↗
NASA NTRSDate not supplied

Resilience, ASRS, and the Narrative about Human Error

We present a study of weather-related incident reports submitted to NASA’s Aviation Safety Reporting System (ASRS) by air carrier pilots in the US. Using specific examples, we examine the relevant aspects of human performance and resilience management exhibited during these incidents. We describe the common narrative about human error and how ASRS data can be used to change it.

resilience↗
DOE OSTI2018

Polybrominated diphenyl ether (PBDE) neurotoxicity: a systematic review and meta-analysis of animal evidence

Background: Polybrominated diphenyl ethers (PBDEs) are ubiquitous in the environment, with well-documented and widespread human exposure. A recent systematic review and meta-analysis of human studies found an association between prenatal serum PBDE concentrations and a decrease in the IQ of children. Objective: Our goal was to demonstrate the application of systematic review methods, including meta-analysis (if possible), to animal toxicology data to draw conclusions about hazard. We performed a systematic review of experimental animal studies on the neurotoxicity of PBDEs following developmental exposure. Outcomes assessed included measures related to learning, memory, and attention, which parallel the intelligence-related outcomes evaluated in the systematic review of human studies. Methods: We searched PubMed, Embase, and Toxline for experimental studies in non-human mammals published in English through August 15, 2016. Evaluation of risk of bias and the overall body of evidence followed guidance developed by the National Toxicology Program’s Office of Health Assessment and Translation (OHAT). Results: The animal data on PBDEs and learning, memory, and attention were available for BDEs 47, 99, 153, 203, 206, and 209 and the technical mixture DE-71. These data were diverse and complex, with studies using varying designs and outcomes. There were concerns about risk of bias for many studies, and study reporting of methods and results was often incomplete. A meta-analysis of six studies showed consistent evidence of an increase in latency (effect size of 25.8 [CI, 20.3 to 31.2]) in the last trial of the Morris water maze in PBDE-exposed animals, with low heterogeneity. However, for most endpoints, there were unexplained inconsistencies across studies. There was also no consistent evidence of a dose-response relationship. Conclusions: Our analyses illustrates the application of systematic review methods to a chemical hazard assessment. There is a moderate level of evidence that exposure to BDEs 47, 99, and 209 affects learning. For other PBDEs and other endpoints, the level of evidence was low or very low. The meta-analysis led to stronger conclusions than would have been drawn based on a qualitative review of the evidence. The systematic review also identified factors that increased concerns about risk of bias in individual studies that might be remedied by better study reporting.

Polybrominated Diphenyl Ether↗
NASA NTRS2014

Quantification of In-flight Physical Changes: Anthropometry and Neutral Body Posture

Currently, NASA does not have sufficient in-flight anthropometric data gathered to assess the impact of physical body shape and size changes on suit sizing. For developing future planetary and reduced gravity suits, NASA needs to quantify the impacts of microgravity on anthropometry, body posture, and neutral body postures (NBP) to ensure optimal crew performance, fit, and comfort. To obtain these impacts, anthropometric data, circumference, length, height, breadth, and depth for body segments (i.e. chest, waist, bicep, thigh, calf) from astronauts for pre, in-, and postflight conditions needs to be collected. Once this data has been collected, a comparison between pre, in-, and postflight anthropometric values will be analyzed, yielding microgravity factors. The NBP will be used to determined body posture (joint angle) changes between subjects throughout the duration of a mission. Data collection, starting with Increments 37/38, is still in progress with the completion of 3 out of 12 subjects. NASA suit engineers and NASA's Extravehicular Activity (EVA) Project Office have identified that suit fit in microgravity could become an issue. It has been noted that crewmembers often need to adjust their suit sizing once they are in orbit. This adjustment could be due to microgravity effects on anthropometry and postural changes, and is necessary to ensure optimal crew performance, fit, and comfort in space. To date, the only data collected to determine the effects of microgravity on physical human changes have been during Skylab, STS-57, and a recent HRP study on seated height changes due to spinal elongation (Spinal Elongation, Master Task List [MTL] #221). The Skylab and the STS-57 studies found that there is a distinct neutral body posture (NBP) based on photographs. The still photographs showed that there is a distinguishable posture with the arms raised and the shoulder abducted; and, in addition, the knees were flexed with noticeable hip flexion and the foot plantar flexed [1,2]. This is the one standard set of body joint angles for a NBP in microgravity. A recent simulated microgravity NBP study [3] has shown an individual variability and inconsistencies in defining NBP. This variation may be influenced by spinal growth, the type of suit fit, and other potential anthropometry factors such as spinal curvature, age, and gender. The variation aspect of this essential data is required for all kinds of space device designs (e.g. suits, habitat, mobility aids, etc.). The method proposed considers the dynamic nature of body movement and will use a measurement technique to continually monitor posture and develop a probability likelihood of the natural posture and how the NBP postures are affected by anthropometry. Additionally, Skylab studies found that crewmembers experienced a stature growth of up to 3%. The data included 3 crewmembers that showed that there is a bi-phasic stature growth once the crew enters into weightlessness. However, the Spinal Elongation study identified that the crewmembers could experience about a 6% growth in seated height and a 3% stature growth, when exposed to microgravity. The results prove that not all anthropometric measurements have the same microgravity percent growth factor. For EVA and suit engineers to properly update the sizing protocol for microgravity, they need additional anthropometric data from space missions. Hence, this study is aimed to gather additional in-flight anthropometric measurements, such as length, depth, breadth, and circumference, to determine the changes to body shape and size due to microgravity effects. It is anticipated that by recording the potential changes to body shape and size, a better suit sizing protocol will be developed for ISS and other space missions. In essence, this study will help NASA quantify the impacts of microgravity on anthropometry to ensure optimal crew performance, fit, and comfort. This study will use simplistic data collection techniques, 3D laser scanning, digital still, and video data, and perform photogrammetric analyses to determine the changes that occur to the body shape, size, and NBP when exposed to a microgravity environment.

Young, K. S.↗
DOE OSTI2025

A Data-Agnostic, Continuous Machine Learning Framework for Application in High Energy Physics and Beyond: Phase 1 Final Scientific/Technical Report

This Phase 1 effort has focused on the development of continual learning frameworks for use in machine learning, specifically in the applied context of High Energy Physics (HEP). Machine learning (ML) is a transformative technology by which computers, typically through the use of neural networks, are able to perform tasks with proficiency that rivals or surpasses that of human users. Model Degradation & Catastrophic Forgetting are two undesired phenomena which can occur in ML where the performance of a model degrades when either deployed on novel data streams, or trained on novel data which are sufficiently different than the data the models were initially trained on. A natural example where these sorts of effects can be observed is in the performance of detectors in harsh environments, where the detector signature may change over the lifetime of the detector as it ages and deteriorates — precisely what occurs in the experiments conducted in HEP. Real world HEP data is therefore an excellent test-ground and use-case for Continual Learning paradigms, which are techniques used in ML to counteract these problems. Ensemble learning is one such technique, where multiple smaller models are trained on subsets of the overall data and are ensembled together during inference. The intuition behind this technique is that, although there are shifts in the distributions which govern the incoming data streams, these shifts are not expected to be homogeneous or global. If a sufficient diversity in solutions within the various sub-models has been achieved, then at least one sub-model is expected to retain its performance within the overall ensemble. One further strength of this approach is that the architectures of the various models do not need to be identical, and in fact even different modalities of data can naturally be combined in this way. This work focused on applying ensemble learning techniques to derive results using two main datasets, anomaly detection in HEP data & time-series forecasting in semiconductor manufacturing data. Semiconductor manufacturing involves data with surprising similarity to that of HEP (e.g. wafer maps look very similar to digi-occupancy maps) and Cerium Lab’s prominence within the semiconductor industry makes semiconductor manufacturing a natural opportunity for commercialization of this work. Our efforts have led to two strong results. The first is that we evaluated the proposed ensembling techniques using previously proposed machine learning architectures for use in anomaly detection, namely AutoEncoder based models and their derivatives. We also developed new architectures which have not been evaluated in this context before. In fact, this work marks the first use of Vision Transformers for anomaly detection in HEP. Second, we demonstrated that ensemble learning significantly improves model performance in scenarios prone to degradation, validating its effectiveness across both HEP and semiconductor datasets. These results further support ensemble learning as a powerful strategy for mitigating catastrophic forgetting and maintaining robust performance in evolving data environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1994

Architecture and evolution of Goddard Space Flight Center Distributed Active Archive Center

The Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) has been developed to enhance Earth Science research by improved access to remote sensor earth science data. Building and operating an archive, even one of a moderate size (a few Terabytes), is a challenging task. One of the critical components of this system is Unitree, the Hierarchical File Storage Management System. Unitree, selected two years ago as the best available solution, requires constant system administrative support. It is not always suitable as an archive and distribution data center, and has moderate performance. The Data Archive and Distribution System (DADS) software developed to monitor, manage, and automate the ingestion, archive, and distribution functions turned out to be more challenging than anticipated. Having the software and tools is not sufficient to succeed. Human interaction within the system must be fully understood to improve efficiency to improve efficiency and ensure that the right tools are developed. One of the lessons learned is that the operability, reliability, and performance aspects should be thoroughly addressed in the initial design. However, the GSFC DAAC has demonstrated that it is capable of distributing over 40 GB per day. A backup system to archive a second copy of all data ingested is under development. This backup system will be used not only for disaster recovery but will also replace the main archive when it is unavailable during maintenance or hardware replacement. The GSFC DAAC has put a strong emphasis on quality at all level of its organization. A Quality team has also been formed to identify quality issues and to propose improvements. The DAAC has conducted numerous tests to benchmark the performance of the system. These tests proved to be extremely useful in identifying bottlenecks and deficiencies in operational procedures.

Bedet, Jean-Jacques↗
DOE OSTI2026

Machine-Learning-Driven Discovery of Water Splitting BaFe 2 O 4 and Human-in-the-Loop Improvement via Al-Substitution for Increased Thermal Stability

Thermochemical hydrogen (TCH) production offers a promising method for converting thermal energy into hydrogen fuel through heat-driven redox cycles of metal oxides. Here, in this work a defect graph neural network (dGNN) was used to predict oxygen vacancy formation energies ΔH V O combined with Materials Project predictions of oxygen chemical potential stability to screen candidate oxides via high-throughput database analysis. BaFe 2 O 4 was identified as a promising material for experimental validation based on its predicted ΔH V O , oxygen chemical potential stability range, and potential for tunable substitutions to improve thermal properties. Experimental validation using thermogravimetric analysis (TGA), stagnation flow reactor (SFR), X-ray diffraction (XRD), and electron microscopy confirmed positive water-splitting behavior but also revealed limitations in thermal stability under aggressive reduction conditions. To address this, a human-in-the-loop modification strategy was employed introducing Al substitution in BaFe 2–x Al x O 4 ; this modification improves thermal stability, alters the crystal structure and enhances overall performance. These results demonstrate a combined computational and experimental workflow in which machine learning accelerates identification of promising candidates, while targeted experimental design enables optimization of functional performance. This approach advances the development of robust, cost-effective TCH materials and highlights the importance of integrating data-driven discovery with human-guided materials design in paving the way for scalable hydrogen production technologies.

organic↗
NASA NTRS2014

Use of Semi-Autonomous Tools for ISS Commanding and Monitoring

As the International Space Station (ISS) has moved into a utilization phase, operations have shifted to become more ground-based with fewer mission control personnel monitoring and commanding multiple ISS systems. This shift to fewer people monitoring more systems has prompted use of semi-autonomous console tools in the ISS Mission Control Center (MCC) to help flight controllers command and monitor the ISS. These console tools perform routine operational procedures while keeping the human operator "in the loop" to monitor and intervene when off-nominal events arise. Two such tools, the Pre-positioned Load (PPL) Loader and Automatic Operators Recorder Manager (AutoORM), are used by the ISS Communications RF Onboard Networks Utilization Specialist (CRONUS) flight control position. CRONUS is responsible for simultaneously commanding and monitoring the ISS Command & Data Handling (C&DH) and Communications and Tracking (C&T) systems. PPL Loader is used to uplink small pieces of frequently changed software data tables, called PPLs, to ISS computers to support different ISS operations. In order to uplink a PPL, a data load command must be built that contains multiple user-input fields. Next, a multiple step commanding and verification procedure must be performed to enable an onboard computer for software uplink, uplink the PPL, verify the PPL has incorporated correctly, and disable the computer for software uplink. PPL Loader provides different levels of automation in both building and uplinking these commands. In its manual mode, PPL Loader automatically builds the PPL data load commands but allows the flight controller to verify and save the commands for future uplink. In its auto mode, PPL Loader automatically builds the PPL data load commands for flight controller verification, but automatically performs the PPL uplink procedure by sending commands and performing verification checks while notifying CRONUS of procedure step completion. If an off-nominal condition occurs during procedure execution, PPL Loader notifies CRONUS through popup messages, allowing CRONUS to examine the situation and choose an option of how PPL loader should proceed with the procedure. The use of PPL Loader to perform frequent, routine PPL uplinks offloads CRONUS to better monitor two ISS systems. It also reduces procedure performance time and decreases risk of command errors. AutoORM identifies ISS communication outage periods and builds commands to lock, playback, and unlock ISS Operations Recorder files. Operation Recorder files are circular buffer files of continually recorded ISS telemetry data. Sections of these files can be locked from further writing, be played back to capture telemetry data that occurred during an ISS loss of signal (LOS) period, and then be unlocked for future recording use. Downlinked Operation Recorder files are used by mission support teams for data analysis, especially if failures occur during LOS. The commands to lock, playback, and unlock Operations Recorder files are encompassed in three different operational procedures and contain multiple user-input fields. AutoORM provides different levels of automation for building and uplinking the commands to lock, playback, and unlock Operations Recorder files. In its automatic mode, AutoORM automatically detects ISS LOS periods, then generates and uplinks the commands to lock, playback, and unlock Operations Recorder files when MCC regains signal with ISS. AutoORM also features semi-autonomous and manual modes which integrate CRONUS more into the command verification and uplink process. AutoORMs ability to automatically detect ISS LOS periods and build the necessary commands to preserve, playback, and release recorded telemetry data greatly offloads CRONUS to perform more high-level cognitive tasks, such as mission planning and anomaly troubleshooting. Additionally, since Operations Recorder commands contain numerical time input fields which are tedious for a human to manually build, AutoORM's ability to automatically build commands reduces operational command errors. PPL Loader and AutoORM demonstrate principles of semi-autonomous operational tools that will benefit future space mission operations. Both tools employ different levels of automation to perform simple and routine procedures, thereby offloading human operators to perform higher-level cognitive tasks. Because both tools provide procedure execution status and highlight off-nominal indications, the flight controller is able to intervene during procedure execution if needed. Semi-autonomous tools and systems that can perform routine procedures, yet keep human operators informed of execution, will be essential in future long-duration missions where the onboard crew will be solely responsible for spacecraft monitoring and control.

Brzezinski, Amy S.↗
DOE OSTI2015

PanFP: Pangenome-based functional profiles for microbial communities

For decades there has been increasing interest in understanding the relationships between microbial communities and ecosystem functions. Current DNA sequencing technologies allows for the exploration of microbial communities in two principle ways: targeted rRNA gene surveys and shotgun metagenomics. For large study designs, it is often still prohibitively expensive to sequence metagenomes at both the breadth and depth necessary to statistically capture the true functional diversity of a community. Although rRNA gene surveys provide no direct evidence of function, they do provide a reasonable estimation of microbial diversity, while being a very cost effective way to screen samples of interest for later shotgun metagenomic analyses. However, there is a great deal of 16S rRNA gene survey data currently available from diverse environments, and thus a need for tools to infer functional composition of environmental samples based on 16S rRNA gene survey data. As a result, we present a computational method called pangenome based functional profiles (PanFP), which infers functional profiles of microbial communities from 16S rRNA gene survey data for Bacteria and Archaea. PanFP is based on pangenome reconstruction of a 16S rRNA gene operational taxonomic unit (OTU) from known genes and genomes pooled from the OTU s taxonomic lineage. From this lineage, we derive an OTU functional profile by weighting a pangenome s functional profile with the OTUs abundance observed in a given sample. We validated our method by comparing PanFP to the functional profiles obtained from the direct shotgun metagenomic measurement of 65 diverse communities via Spearman correlation coefficients. These correlations improved with increasing sequencing depth, within the range of 0.8 0.9 for the most deeply sequenced Human Microbiome Project mock community samples. PanFP is very similar in performance to another recently released tool, PICRUSt, for almost all of survey data analysed here. But, our method is unique in that any OTU building method can be used, as opposed to being limited to closed reference OTU picking strategies against specific reference sequence databases. In conclusion, we developed an automated computational method, which derives an inferred functional profile based on the 16S rRNA gene surveys of microbial communities. The inferred functional profile provides a cost effective way to study complex ecosystems through predicted comparative functional metagenomes and metadata analysis. All PanFP source code and additional documentation are freely available online at GitHub.

16S rRNA survey↗
DOE OSTI2016

Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data

An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.

97 MATHEMATICS AND COMPUTING↗