Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “human identification”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 181 records · Page 10

Comprehensive analysis of common polymers using hyphenated TGA-FTIR-GC/MS and Raman spectroscopy towards a database for micro- and nanoplastics identification, characterization, and quantitation

Environmental contamination by micro- and nanoplastics (MNPs) is well documented with potential for their increased accumulation globally. Growing public concern over environmental, ecological, and human exposure to MNPs has led to exponential increase in publications, news articles, and reports. Significant knowledge gap exists in standardized analytical methods for the identification and quantification of MNPs from real world environmental samples. Here, in this study, we report comprehensive datasets utilizing thermogravimetric analyzer (TGA) coupled to a Fourier transformed infrared spectrometer (FTIR) and a gas chromatography/mass spectrometer (GC/MS) with corresponding Raman spectral data for the most common polymers documented to be present in the environment (35 plastics of 12 polymer types), to serve as a base line reference for the identification and quantitation of MNPs. Various parameters for TGA-FTIR-GC/MS data acquisition were optimized. Commercial consumer plastic product compositions were identified using this analytical database. Case studies to showcase the utility of the method for polymer mixtures analysis is included. This dataset would serve towards the development of a collaborative, global, comprehensive, and curated public database for the identification of various MNPs and mixtures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Photointerpretation of ERTS-A multispectral images analysis of vegetation and land use for the Valencia Lake Basin region

The area of investigation includes part of the Central and Capital Region of Venezuela. Climatic conditions define the existence of different ecological associations with biological forms well defined in their physiognomic complexity as they reach complete development in environments related to gross geomorphic units: coastal area, northern mountain range, fluvio-lacustrines plains, southern mountain range, southern ridges and parts of Venezuelan central plains. In this area, rainfall varies from 600 mm to 2000 mm and evapotranspiration indexes indicate the existence of vegetation for semiarid conditions ( tropical very dry forest) as well as vegetation for very humid conditions (very humid premontane forest). This project was designed for: (1) Location of areas with representative ecosystems for the purpose of managing planning. (2) Identification of structural and physiognomic characteristics of vegetation. (3) Assessment of human activity effects on environmental debasement. (4) Definition of the content and possibilities of orbital ERTS images for the study of urban and rural land use. (5) Setting photointerpretation patterns applicable to areas of similar gross environmental conditions.

Salas, F.↗

Passive IFF: Autonomous Nonintrusive Rapid Identification of Friendly Assets

A proposed optoelectronic instrument would identify targets rapidly, without need to radiate an interrogating signal, apply identifying marks to the targets, or equip the targets with transponders. The instrument was conceived as an identification, friend or foe (IFF) system in a battlefield setting, where it would be part of a targeting system for weapons, by providing rapid identification for aimed weapons to help in deciding whether and when to trigger them. The instrument could also be adapted to law-enforcement and industrial applications in which it is necessary to rapidly identify objects in view. The instrument would comprise mainly an optical correlator and a neural processor (see figure). The inherent parallel-processing speed and capability of the optical correlator would be exploited to obtain rapid identification of a set of probable targets within a scene of interest and to define regions within the scene for the neural processor to analyze. The neural processor would then concentrate on each region selected by the optical correlator in an effort to identify the target. Depending on whether or not a target was recognized by comparison of its image data with data in an internal database on which the neural processor was trained, the processor would generate an identifying signal (typically, friend or foe ). The time taken for this identification process would be less than the time needed by a human or robotic gunner to acquire a view of, and aim at, a target. An optical correlator that has been under development for several years and that has been demonstrated to be capable of tracking a cruise missile might be considered a prototype of the optical correlator in the proposed IFF instrument. This optical correlator features a 512-by-512-pixel input image frame and operates at an input frame rate of 60 Hz. It includes a spatial light modulator (SLM) for video-to-optical image conversion, a pair of precise lenses to effect Fourier transforms, a filter SLM for digital-to-optical correlation-filter data conversion, and a charge-coupled device (CCD) for detection of correlation peaks. In operation, the input scene grabbed by a video sensor is streamed into the input SLM. Precomputed correlation-filter data files representative of known targets are then downloaded and sequenced into the filter SLM at a rate of 1,000 Hz. When there occurs a match between the input target data and one of the known-target data files, the CCD detects a correlation peak at the location of the target. Distortion- invariant correlation filters from a bank of such filters are then sequenced through the optical correlator for each input frame. The net result is the rapid preliminary recognition of one or a few targets.

Moynihan, Philip↗

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗

pixelvar79/ESGAN-Flowering-Detection-paper

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

Varela, Sebastian↗

Matrix Metalloproteinases as Candidate Antigenic Determinants for Anti‐Tumor Autoantibodies in Human Ovarian Cancer: A Post Hoc Analysis

Circulating antibodies in patients with cancer can facilitate the identification of accessible epitopes on autoantigens expressed by tumors. To identify previously unrecognized protein targets in ovarian cancer, we computationally assessed a heptapeptide consensus motif (VPELGHE, flanked by two cysteine residues yielding a cyclic nonapeptide under oxidizing conditions) previously discovered via phage display-based epitope mapping of autoantibodies in patients. Eight proteins associated with ovarian cancer encompass amino acid sequences similar to the consensus motif and were, therefore, considered as candidate native autoantigens. Among these candidate targets, however, matrix metalloproteinase 14 (MMP14) demonstrates gene expression that is both high and negatively correlated with survival in ovarian cancer patient cohorts. MMP14 protein levels are also stable in tumor versus non-tumor tissues. Moreover, the corresponding heptapeptide mimic in MMP14 occurs within an α-helical secondary structural element observed in its catalytic domain. These findings demonstrate that a subset of patient-derived autoantibodies may interact with a previously unknown antigenic epitope found in MMP14 and other MMPs, thereby providing opportunities for the development of new targeted agents.

Biochemistry & Molecular Biology↗

A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes

Due to their persistent widespread severe winds, derechos pose significant threats to human safety and property, with impacts comparable to many tornadoes and hurricanes. Yet, automated detection of derechos remains challenging due to the absence of spatiotemporally continuous observations and the complex criteria employed to define the phenomenon. This study presents an objective derecho detection approach capable of automatically identifying derechos through both observations and model results. The approach is grounded in a physically based definition of derechos and integrates three algorithms: (1) the Python Flexible Object Tracker (PyFLEXTRKR) algorithm to track mesoscale convective systems (MCSs), (2) a semantic segmentation convolutional neural network to identify bow echoes, and (3) a comprehensive classification algorithm to detect derechos within MCS life cycles and distinguish derecho-producing from non-derecho-producing MCSs. Using this approach, we developed a novel high-resolution (4 km and hourly) observational dataset of derechos and accompanying derecho-producing MCSs over the United States east of the Rocky Mountains from 2004 to 2021. The dataset consists of two subsets based on different gust speed data sources and is analyzed to document the climatology of derechos in the United States. On average, 12–15 derechos are identified per year, aligning with previous estimations (∼6–21 events annually). The spatial distribution and seasonal variation patterns are consistent with prior studies, showing peak occurrences in the Great Plains and the Midwest during the warm season. Additionally, during the study period, derechos account for approximately 3.1 % of measured damaging gusts (≥25.93 m s−1) over the eastern United States. The dataset is publicly available at https://doi.org/10.5281/zenodo.14835362 (Li et al., 2025).

54 ENVIRONMENTAL SCIENCES↗

Dynamic Vehicle Assessment for Intelligent Contingency Management of Urban Air Mobility Vehicles

New algorithms will be required to ensure passenger and bystander safety during the expected era of autonomous urban air mobility (UAM) aircraft. This paper examines an approach for assessing the vehicle capability to fly itself and to complete a mission safely. The concepts combine elements of system identification, adaptive control, flight dynamics, envelope predictions, and handling qualities, as well as human pilot intuition. The approach is applied to a simulation of a generic distributed electric propulsion urban air mobility-type aircraft, which was developed under the NASA Transformational Tools and Technologies (TTT) project, Autonomous Systems / Intelligent Contingency Management subproject.

flight envelope↗

A randomized multiplex CRISPRi-Seq approach for the identification of critical combinations of genes

Identifying virulence-critical genes from pathogens is often limited by functional redundancy. To rapidly interrogate the contributions of combinations of genes to a biological outcome, we have developed a multiplex, randomized CRISPR interference sequencing (MuRCiS) approach. At its center is a new method for the randomized self-assembly of CRISPR arrays from synthetic oligonucleotide pairs. When paired with PacBio long-read sequencing, MuRCiS allowed for near-comprehensive interrogation of all pairwise combinations of a group of 44 Legionella pneumophila virulence genes encoding highly conserved transmembrane proteins for their role in pathogenesis. Both amoeba and human macrophages were challenged with L. pneumophila bearing the pooled CRISPR array libraries, leading to the identification of several new virulence-critical combinations of genes. lpg2888 and lpg3000 were particularly fascinating for their apparent redundant functions during L. pneumophila human macrophage infection, while lpg3000 alone was essential for L. pneumophila virulence in the amoeban host Acanthamoeba castellanii. Thus, MuRCiS provides a method for rapid genetic examination of even large groups of redundant genes, setting the stage for application of this technology to a variety of biological contexts and organisms.

79 ASTRONOMY AND ASTROPHYSICS↗

Spatial Analysis of West Nile Virus: Predictive Risk Modeling of a Vector-borne Infectious Disease in Illinois by Means of NASA Earth Observation Systems

West Nile Virus is a mosquito-borne virus of the family Flaviviridae. It infects birds and various mammals, including humans, and can cause encephalitis that may prove fatal, notably among vulnerable populations. Since its identification in New York City in 1999, WNV has become established in a broad range of ecological settings throughout North America, infecting more than 25,300 people and killing 1133 as of 2008 (CDC,2009). WNV is transmitted by mosquitoes that feed on infected birds. As a result, the degree of human infection depends on local ecology and human exposure. This study hypothesizes that remote sensing and GIS can be used to analyze environmental determinants of WNV transmission, such as climate, elevation, land cover, and vegetation densities, to map areas of WNV risk for surveillance and intervention.

Renneboog, Nathan↗

A randomized multiplex CRISPRi-Seq approach for the identification of critical combinations of genes

Identifying virulence-critical genes from pathogens is often limited by functional redundancy. To rapidly interrogate the contributions of combinations of genes to a biological outcome, we have developed a mu ltiplex, r andomized C RISPR i nterference s equencing (MuRCiS) approach. At its center is a new method for the randomized self-assembly of CRISPR arrays from synthetic oligonucleotide pairs. When paired with PacBio long-read sequencing, MuRCiS allowed for near-comprehensive interrogation of all pairwise combinations of a group of 44 Legionella pneumophila virulence genes encoding highly conserved transmembrane proteins for their role in pathogenesis. Both amoeba and human macrophages were challenged with L. pneumophila bearing the pooled CRISPR array libraries, leading to the identification of several new virulence-critical combinations of genes. lpg2888 and lpg3000 were particularly fascinating for their apparent redundant functions during L. pneumophila human macrophage infection, while lpg3000 alone was essential for L. pneumophila virulence in the amoeban host Acanthamoeba castellanii . Thus, MuRCiS provides a method for rapid genetic examination of even large groups of redundant genes, setting the stage for application of this technology to a variety of biological contexts and organisms.

59 BASIC BIOLOGICAL SCIENCES↗

A randomized multiplex CRISPRi-Seq approach for the identification of critical combinations of genes

Identifying virulence-critical genes from pathogens is often limited by functional redundancy. To rapidly interrogate the contributions of combinations of genes to a biological outcome, we have developed a mu ltiplex, r andomized C RISPR i nterference s equencing (MuRCiS) approach. At its center is a new method for the randomized self-assembly of CRISPR arrays from synthetic oligonucleotide pairs. When paired with PacBio long-read sequencing, MuRCiS allowed for near-comprehensive interrogation of all pairwise combinations of a group of 44 Legionella pneumophila virulence genes encoding highly conserved transmembrane proteins for their role in pathogenesis. Both amoeba and human macrophages were challenged with L. pneumophila bearing the pooled CRISPR array libraries, leading to the identification of several new virulence-critical combinations of genes. lpg2888 and lpg3000 were particularly fascinating for their apparent redundant functions during L. pneumophila human macrophage infection, while lpg3000 alone was essential for L. pneumophila virulence in the amoeban host Acanthamoeba castellanii . Thus, MuRCiS provides a method for rapid genetic examination of even large groups of redundant genes, setting the stage for application of this technology to a variety of biological contexts and organisms.

Ellis, Nicole A. (ORCID:0000000195928415)↗

A Comprehensive Characterization of Microorganisms and Allergens in Spacecraft Environment

The determination of risk from infectious disease during long-duration missions is composed of several factors including the concentration and the characteristics of the infectious agent. Thus, a thorough knowledge of the microorganisms aboard spacecraft is essential in mitigating infectious disease risk to the crew. While stringent steps are taken to minimize the transfer of potential pathogens to spacecraft, several medically significant organisms have been isolated from both the Mir and International Space Station (ISS). Historically, the method for isolation and identification of microorganisms from spacecraft environmental samples depended upon their growth on culture media. Unfortunately, only a fraction of the organisms may grow on a culture medium, potentially omitting those microorganisms whose nutritional and physical requirements for growth are not met. Thus, several pathogens may not have been detected, such as Legionella pneumophila, the etiological agent of Legionnaire s disease. We hypothesize that environmental analysis using non-culture-based technologies will reveal microorganisms, allergens, and microbial toxins not previously reported in spacecraft, allowing for a more complete health assessment. The development of techniques for this flight experiment, operationally named SWAB, has already provided advances in NASA laboratory processes and beneficial information toward human health risk assessment. The translation of 16S ribosomal DNA sequencing for the identification of bacteria from the SWAB experiment to nominal operations has increased bacterial speciation of environmental isolates from previous flights three fold compared to previous conventional methodology. The incorporation of molecular-based DNA fingerprinting using repetitive sequence-based polymerase chain reaction (rep-PCR) into the capabilities of the laboratory has provided a methodology to track microorganisms between crewmembers and their environment. Both 16S ribosomal DNA identification and bacterial fingerprinting have improved NASA s capability to better understand spacecraft environments and determine the source of contamination events. Preflight sampling has been completed for air, surface, and water samples. In-flight sample collection has been completed for a total of 8 air and surface sample collection sessions. In-flight hardware has performed well and the surface sampling device received positive feedback from the crew for its ease of use. While processing and analysis continue for these samples, early results have begun to provide information on the spacecraft environment. Using a method called Denaturing Gradient Gel Electrophoresis (DGGE), several air and samples were evaluated to determine the types of organisms that were present. Using only molecular techniques, DGGE does not depend on any microbial growth on culture media, allowing a more comprehensive assessment of the spacecraft interior. Preliminary results have identified several microorganisms that would not have been isolated using current technology, though none of these organisms would be considered medically significant. Interestingly, the isolation of Gram negative organisms is greater using DGGE than conventional media based isolation. The cause of this finding is unclear, though it may be the result of the technique s ability to isolate both viable and non-viable bacteria. The next phase of the SWAB sample analysis is the use of quantitative polymerase chain reaction (QPCR) to look for specific medically significant organisms. While not as broad as DGGE, QPCR is much more sensitive and may reveal findings that were not seen during the initial evaluation. Together, this information will lead toward an accurate microbial risk assessment to help set flight requirements to protect the safety, health, and performance of the crew.

Castro, V.A.↗

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗

Genomic reconstruction of Bacillus anthracis from complex environmental samples enables high-throughput identification and lineage assignment in Pakistan

Bacillus anthracis, the causative agent of anthrax, is a highly virulent zoonotic pathogen primarily affecting domesticated and wild herbivores. Human exposure to B. anthracis is primarily through contact with infected animals or contaminated animal products. In Pakistan, where livestock vaccines are largely unavailable and infected carcasses are often disposed of improperly, the risk to humans, wildlife and livestock is significant. Currently, the diagnosis of anthrax infections and outbreak tracing necessitates the isolation and culturing of B. anthracis, a process that requires BSL-3 facilities. In this study, we show that positive identification, genome reconstruction and lineage assignment can be accomplished using bioinformatic analysis of DNA extracted directly from environmental samples that would otherwise provide the starting material for isolation and culturing. This approach does not require laboratory target enrichment as is necessary for other pathogens, due in part to the extremely high bacterial load in the bloodstream in the deceased animals. Using these methods, we greatly expand the knowledge of endemic B. anthracis in Pakistan. We provide the first reference B. anthracis genomes from Pakistan since the 1970s and identify A.Br.014 Aust94 as a minor circulating sublineage alongside the dominant A.Br.047 Vollum. Future work will focus on the limits of detection and will determine if this bioinformatic method can be expanded more broadly for B. anthracis or other pathogens to replace typical culture-based methods.

A.Br.047 Vollum↗

The Identification and Modeling of Visual Cue Usage in Manual Control Task Experiments

Many fields of endeavor require humans to conduct manual control tasks while viewing a perspective scene. Manual control refers to tasks in which continuous, or nearly continuous, control adjustments are required. Examples include flying an aircraft, driving a car, and riding a bicycle. Perspective scenes can arise through natural viewing of the world, simulation of a scene (as in flight simulators), or through imaging devices (such as the cameras on an unmanned aerospace vehicle). Designers frequently have some degree of control over the content and characteristics of a perspective scene; airport designers can choose runway markings, vehicle designers can influence the size and shape of windows, as well as the location of the pilot, and simulator database designers can choose scene complexity and content. Little theoretical framework exists to help designers determine the answers to questions related to perspective scene content. An empirical approach is most commonly used to determine optimum perspective scene configurations. The goal of the research effort described in this dissertation has been to provide a tool for modeling the characteristics of human operators conducting manual control tasks with perspective-scene viewing. This is done for the purpose of providing an algorithmic, as opposed to empirical, method for analyzing the effects of changing perspective scene content for closed-loop manual control tasks.

Sweet, Barbara Townsend↗

Long-Term Evaluation of Remedial Technology Performance in the Laboratory: Multi-year Experimental Test Plan

An understanding of the long-term effectiveness of remediation technologies is central to sustainable environmental cleanup. Long-term experiments are valuable for reducing uncertainty and predicting remediation outcomes at scales required for regulatory compliance. However, these types of tests can be costly and challenging to interpret. Therefore, contaminated sites often rely on short-term laboratory experiments that may not account for the potentially significant effects of gradual, time- dependent processes governing contaminant retention, release, and species transformation. For example, short-term lab experiments (from months to a year) conducted with sediments from the unsaturated and saturated zones at the Hanford Site play an important role in initial evaluations of remediation technologies but cannot capture the full extent of time-dependent reactions, leaving uncertainties in field-scale deployment. Here, long-term testing will be conducted on select technologies based on their performance in short-term testing. The overall objective is to directly address the challenges described above by generating and analyzing data on long-term (2–10 years) efficacy of selected remediation technologies, integrating this understanding into models, and providing critical input for remediation planning, monitoring, and 5-year review cycles for field-implemented remedies. Specific objectives include: 1. Evaluating long-term efficiency of promising remedies under site-specific conditions. 2. Generating robust parameters for modeling, reducing uncertainty in predictive simulations. 3. Advancing integrated monitoring by combining geochemical and geophysical observations. 4. Informing field-scale implementation by incrementally advancing technologies, identifying failure mechanisms early, and prioritizing robust, cost-effective remedies. Through systematic evaluation of technologies in laboratory-scale column experiments, integrated monitoring, and modeling support, this project is designed to bolster confidence in the long-term robustness of selected technologies. The ultimate outcome is the identification and deployment of more reliable, cost-effective remedies that safeguard human health and the environment while reducing the uncertainties that have historically hindered cleanup progress at Hanford Site. An experimental approach was developed and initiated for long-term testing potentially up to 10 years. The table below summarizes the experimental approach developed for testing select technologies and presented in this multi-year experimental test plan.

54 ENVIRONMENTAL SCIENCES↗

A guide for performing system safety analysis

A general guide is presented for performing system safety analyses of hardware, software, operations and human elements of an aerospace program. The guide describes a progression of activities that can be effectively applied to identify hazards to personnel and equipment during all periods of system development. The general process of performing safety analyses is described; setting forth in a logical order the information and data requirements, the analytical steps, and the results. These analyses are the technical basis of a system safety program. Although the guidance established by this document cannot replace human experience and judgement, it does provide a methodical approach to the identification of hazards and evaluation of risks to the system.

Brush, J. M.↗