Search NASASearch

SEARCH · Search NASA

Results for “text analysis”

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 91 records · Page 5

Modification of Ni-20Cr corrosion dealloying behavior in molten fluorides via cold work induced plastic deformation

The corrosion dealloying behavior of cold-worked (CW) Ni20Cr alloy (wt%) was studied in molten LiF-NaF-KF (or FLiNaK) salts at 600 °C, equal to a homologous temperature (TH) of 0.52. Alloys were cold-rolled to achieve reductions of thickness of 10%, 30%, and 50% introducing plastic deformation and a high density of dislocations. Potentiostatic holds (Eapplied) were applied in two different electrode potential regimes. At 1.75VK+/K$${\text{V}}_{{\text{K}}^{+}/{\text{K}}}$$, Cr dealloying to Cr(II) and Cr(III) is predominant, while at 1.90 VK+/K$${\text{V}}_{{\text{K}}^{+}/{\text{K}}}$$, both Ni and Cr are oxidized in molten FLiNaK at 600 °C. In these potential regimes, dealloyed Ni20Cr displayed bicontinuous porosity within the grain interior and at grain boundaries, driven by the high driving force for Cr dissolution and sustained by defect mediated outward solid state diffusion of Cr in parallel with surface diffusion of Ni. The bicontinuous porous structure developed was observed to undergo further coarsening and densification of the Ni-rich ligaments at a higher electrode potential. The main effect of CW observed is the introduction of plastic deformation and dislocation substructures that serve as short-circuit paths for Cr solid state diffusion to surfaces exposed to FLiNaK. This modified the evolution of the bicontinuous porous structure which increased with CW substantially. Kinetic analysis reveals that the Cr dealloying at +1.75 VK+/K$${\text{V}}_{{\text{K}}^{+}/{\text{K}}}$$ and 1.90 VK+/K$${\text{V}}_{{\text{K}}^{+}/{\text{K}}}$$ is initially charge transfer controlled, except for the 50% CW condition at 1.90 VK+/K$${\text{V}}_{{\text{K}}^{+}/{\text{K}}}$$, where the process becomes limited by slow Cr defect mediated bulk diffusion. The rate determining factors are explored and compared to experimental results.

Chan, Ho Lun

Semantic Theme Analysis of Pilot Incident Reports

Pilots report accidents or incidents during take-off, on flight and landing to airline authorities and Federal aviation authority as well. The description of pilot reports for an incident contains technical terms related to Flight instruments and operations. Normal text mining approaches collect keywords from text documents and relate them among documents that are stored in database. Present approach will extract specific theme analysis of incident reports and semantically relate hierarchy of terms assigning weights of themes. Once the theme extraction has been performed for a given document, a unique key can be assigned to that document to cross linking the documents. Semantic linking will be used to categorize the documents based on specific rules that can help an end-user to analyze certain types of accidents. This presentation outlines the architecture of text mining for pilot incident reports for autonomous categorization of pilot incident reports using semantic theme analysis.

Thirumalainambi, Rajkumar

Solar Astronomy Data Base: Packaged Information on Diskette

In its role as a library, the National Geophysical Data Center has transferred to diskette a collection of small, digital files of routinely measured solar indices for use on an IBM-compatible desktop computer. Recording these observations on diskette allows the distribution of specialized information to researchers with a wide range of expertise in computer science and solar astronomy. Every data set was made self-contained by including formats, extraction utilities, and plain-language descriptive text. Moreover, for several archives, two versions of the observations are provided - one suitable for display, the other for analysis with popular software packages. Since the files contain no control characters, each one can be modified with any text editor.

Mckinnon, John A.

Complete Decoding and Reporting of Aviation Routine Weather Reports (METARs)

Aviation Routine Weather Report (METAR) provides surface weather information at and around observation stations, including airport terminals. These weather observations are used by pilots for flight planning and by air traffic service providers for managing departure and arrival flights. The METARs are also an important source of weather data for Air Traffic Management (ATM) analysts and researchers at NASA and elsewhere. These researchers use METAR to correlate severe weather events with local or national air traffic actions that restrict air traffic, as one example. A METAR is made up of multiple groups of coded text, each with a specific standard coding format. These groups of coded text are located in two sections of a report: Body and Remarks. The coded text groups in a U.S. METAR are intended to follow the coding standards set by National Oceanic and Atmospheric Administration (NOAA). However, manual data entry and edits made by a human report observer may result in coded text elements that do not follow the standards, especially in the Remarks section. And contrary to the standards, some significant weather observations are noted only in the Remarks section and not in the Body section of the reports. While human readers can infer the intended meaning of non-standard coding of weather conditions, doing so with a computer program is far more challenging. However such programmatic pre-processing is necessary to enable efficient and faster database query when researchers need to perform any significant historical weather analysis. Therefore, to support such analysis, a computer algorithm was developed to identify groups of coded text anywhere in a report and to perform subsequent decoding in software. The algorithm considers common deviations from the standards and data entry mistakes made by observers. The implemented software code was tested to decode 12 million reports and the decoding process was able to completely interpret 99.93 of the reports. This document presents the deviations from the standards and the decoding algorithm. Storing all decoded data in a database allows users to quickly query a large amount of data and to perform data mining on the data. Users can specify complex query criteria not only on date or airport but also on weather condition. This document also describes the design of a database schema for storing the decoded data, and a Data Warehouse web application that allows users to perform reporting and analysis on the decoded data. Finally, this document presents a case study correlating dust storms reported in METARs from the Phoenix International airport with Ground Stops issued by Air Route Traffic Control Centers (ATCSCC). Blowing widespread dust is one of the weather conditions when dust storm occurs. By querying the database, 294 METARs were found to report blowing widespread dust at the Phoenix airport and 41 of them reported such condition only in the Remarks section of the reports. When METAR is a data source for an ATM research, it is important to include weather conditions not only from the Body section but also from the Remarks section of METARs.

METAR Decoder/Parser

A Model Based Approach to Extract Health Information from Textual Data

In current nuclear power plants (NPPs) a large amount of condition-based data is being generated and stored to assess and monitor component health and performance. The format of this data can be either numeric (e.g., pump vibration data) or textual (e.g., condition report which assess component health). While assessing component health from numeric data can be performed with a large variety of methods, the extraction of information from textual data still remains a challenge. Natural language processing (NLP) methods are starting to be deployed in current NPPs mainly to filter out incident reports (IRs) that are not safety related by employing supervised machine learning methods. However, these methods do not really provide the quantitative information that might be contained in IRs. This paper presents an approach to extract information from textual data (e.g., from IRs, maintenance reports) that is based on NLP data analytics methods coupled with model-based system engineer (MBSE) models. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence; such analysis includes: part of speech (POS) tagging (i.e., identification of grammatic elements of each string - e.g., nouns, verbs), named entity recognition (i.e., identification of text entities - e.g., names, dates, events), and relation extraction (e.g., coreference resolution). On the other hand, semantic analysis is designed to analyze the logic structure of a sentence. Through a specific set of rules, our methods can identify whether a sentence contains health information of a component (e.g., degraded performance, anomaly behavior) or the causal relationship between two events (i.e., a cause-effect pair). An innovative element of our approach is that semantic analysis relies on MBSE models to identify links between textual elements. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. This paper presents in detail how the integration of NLP methods and MBSE models is performed. Few analysis examples focusing on centrifugal pumps are presented.

97 - MATHEMATICS AND COMPUTING

Libra

Libra is a Python package that extends support of its parent package, SQLAlchemy. Libra’s primary functionality supports the dynamic creation of object-oriented analogs of SQL tables from a variety of user-defined, text-based schema definition formats and provides quality control and analysis tools and methods

Spears, Brady

Mining Product Reviews for Important Product Features of Refurbished iPhones

Problem: Remanufacturers want to increase consumer interest in refurbished products, which motivates the need to understand which product features are important to buyers of refurbished products such as mobile phones. Research Questions: This study addresses two questions. First, which product features are most important for buyers of refurbished iPhones? Second, how do those preferences differ from the preferences of buyers of new iPhones? Methods: Online reviews of iPhones are obtained and converted into a document–term matrix. Using this text model, three subsets of features are identified using statistical analysis of frequency of mention: most frequent, average, and least frequent. A logistic regression (LR) model is then used to identify which features are most predictive of whether a review is for a new or refurbished phone. Results: Buyers of refurbished phones mention battery health, screen/display, shell condition, and brand significantly more often than other features. Directly contrasting reviews of refurbished versus new phones shows that shell condition, brand, speaker, and charger are found to be the most predictive product features indicated in reviews for refurbished phones. Of those, the shell condition is significantly more predictive than the others. Implications: The results identify product features that remanufacturers of iPhones can emphasize to increase customer demand.

Anisi, Atefeh

Software for Analyzing Sequences of Flow-Related Images

Spotlight is a computer program for analysis of sequences of images generated in combustion and fluid physics experiments. Spotlight can perform analysis of a single image in an interactive mode or a sequence of images in an automated fashion. The primary type of analysis is tracking of positions of objects over sequences of frames. Features and objects that are typically tracked include flame fronts, particles, droplets, and fluid interfaces. Spotlight automates the analysis of object parameters, such as centroid position, velocity, acceleration, size, shape, intensity, and color. Images can be processed to enhance them before statistical and measurement operations are performed. An unlimited number of objects can be analyzed simultaneously. Spotlight saves results of analyses in a text file that can be exported to other programs for graphing or further analysis. Spotlight is a graphical-user-interface-based program that at present can be executed on Microsoft Windows and Linux operating systems. A version that runs on Macintosh computers is being considered.

Klimek, Robert

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Electronic health records

Spotlight-8 Image Analysis Software

Spotlight is a cross-platform GUI-based software package designed to perform image analysis on sequences of images generated by combustion and fluid physics experiments run in a microgravity environment. Spotlight can perform analysis on a single image in an interactive mode or perform analysis on a sequence of images in an automated fashion. Image processing operations can be employed to enhance the image before various statistics and measurement operations are performed. An arbitrarily large number of objects can be analyzed simultaneously with independent areas of interest. Spotlight saves results in a text file that can be imported into other programs for graphing or further analysis. Spotlight can be run on Microsoft Windows, Linux, and Apple OS X platforms.

Klimek, Robert

Search for heavy resonances decaying into two Higgs bosons in the $\text {b}{\bar{\text {b}}} \tau ^{+} \tau ^{-}$ final state in proton–proton collisions at $\sqrt{s} = 13\,\text {Te}\hspace{-.08em}\text {V}$

A search is presented for massive narrow-width resonances in the mass range of 1–4.5 TeV , decaying into pairs of Higgs bosons (HH). The search uses proton–proton collision data at a center-of-mass energy of 13 TeV collected with the CMS detector at the CERN LHC during 2016–2018, corresponding to an integrated luminosity of 138 fb -1 . The analysis targets final states where one Higgs boson decays into a pair of bottom quarks and the other into a pair of tau leptons, ${\text {X}} \rightarrow {\text {HH}} \rightarrow \text {b}{\bar{\text {b}}}\,\tau ^{+}\tau ^{-}$. It uses a single large radius jet to reconstruct the ${\text {H}} \rightarrow \text {b}{\bar{\text {b}}}$ decay, while the ${\text {H}} \rightarrow \tau ^{+}\tau ^{-}$ decay products can either be contained within a single large radius jet or appear as two isolated tau leptons. The observed data are consistent with standard model background expectations. Upper limits at 95% confidence level are set on the production cross section for resonant HH production for masses between 1 and 4.5 TeV. This analysis sets the most sensitive limits to date on ${\text {X}} \rightarrow {\text {HH}} \rightarrow \text {b}\bar{\hbox {b}}\, \tau ^{+}\tau ^{-}$ decays in the mass range of 1.4–4.5 TeV.

Hayrapetyan, A. [Yerevan Physics Institute]

Teaching audience analysis to the technical student

Teaching audience analysis, as practiced in a technical writing course for engineering students, is discussed. Audience analysis is described as the task of defining the audience for a particular piece of writing and determining those characteristics of the audience which constrain the writer and effect reception of the message. A mature technical writing style that shows the tension produced when a text is written to be read and understood is considered in terms of audience analysis. Techniques include: (1) conveying to students the concept that a reader with certain expectations exist, (2) team teaching to preserve the context of a given technical discipline, and (3) assigning a technical report that addresses a variety of readers, thus establishing the complexity of audience oriented writing.

Debs, M. B.

Information Extraction for System-Software Safety Analysis: Calendar Year 2008 Year-End Report

This annual report describes work to integrate a set of tools to support early model-based analysis of failures and hazards due to system-software interactions. The tools perform and assist analysts in the following tasks: 1) extract model parts from text for architecture and safety/hazard models; 2) combine the parts with library information to develop the models for visualization and analysis; 3) perform graph analysis and simulation to identify and evaluate possible paths from hazard sources to vulnerable entities and functions, in nominal and anomalous system-software configurations and scenarios; and 4) identify resulting candidate scenarios for software integration testing. There has been significant technical progress in model extraction from Orion program text sources, architecture model derivation (components and connections) and documentation of extraction sources. Models have been derived from Internal Interface Requirements Documents (IIRDs) and FMEA documents. Linguistic text processing is used to extract model parts and relationships, and the Aerospace Ontology also aids automated model development from the extracted information. Visualizations of these models assist analysts in requirements overview and in checking consistency and completeness.

Malin, Jane T.

Automation for System Safety Analysis

This presentation describes work to integrate a set of tools to support early model-based analysis of failures and hazards due to system-software interactions. The tools perform and assist analysts in the following tasks: 1) extract model parts from text for architecture and safety/hazard models; 2) combine the parts with library information to develop the models for visualization and analysis; 3) perform graph analysis and simulation to identify and evaluate possible paths from hazard sources to vulnerable entities and functions, in nominal and anomalous system-software configurations and scenarios; and 4) identify resulting candidate scenarios for software integration testing. There has been significant technical progress in model extraction from Orion program text sources, architecture model derivation (components and connections) and documentation of extraction sources. Models have been derived from Internal Interface Requirements Documents (IIRDs) and FMEA documents. Linguistic text processing is used to extract model parts and relationships, and the Aerospace Ontology also aids automated model development from the extracted information. Visualizations of these models assist analysts in requirements overview and in checking consistency and completeness.

Malin, Jane T.

GPM V05 Gridded Text Products

This presentation will summarize the changes in the products for the GPM V05 reprocessing cycle. It will concentrate on discussing the gridded text product from the core satellite retrievals. However, all aspects of the GPROF GMI changes in this product are equally appropriate to the other two gridded text products. The GPM mission reprocessed its products in May of 2017 as part of a continuing improvement of precipitation retrievals. This lead to important improvement in the retrievals and therefore also necessitated reprocessing the gridded test products. The V05 GPROF changes not only improved the retrievals but substantially alerted the format and this compelled changes to the gridded text products. Especially important in this regard is the GPROF2017 (used in V05) change from reporting the fraction of the total precipitation rate that occurring as convection or in liquid phase. Instead, GPROF2017, and therefore V05 gridded text products, report the rate of convective precipitation in mm/hr. The GPROF2017 algorithm now reports the frozen precipitation rate in mm/hr rather than the fraction of total precipitation that is liquid. Because of the aim of the gridded text product is to remain simple the radar and combined results will also change in V05 to reflect this change in the GMI retrieval. The presentation provides an analysis of these changes as well as presenting a comparison with the swath products from which the hourly text grids were derived.

PPS

User’s Manual for RESRAD-RDD&IND Code Version 2: Vol. 2—User’s Guide for RESRAD-RDD&IND Code

Version 2.0 of the RESRAD-RDD&IND computer code is designed to support the implementation of protective action guides (PAGs) after a nuclear emergency incident including a radiological dispersal device (RDD) and/or an improvised nuclear device (IND) incident (EPA 2017). Eight different group types, addressing various decisions, are available for selection. The RESRAD-RDD&IND code calculates radiological doses, stay times, etc., for the selected group that the user wishes to focus on. (That is, the results for all the groups are not calculated simultaneously, and the input for those other groups do not matter, although some parameter values are shared between groups.) Version 2.0 has a user-friendly interface so that the RESRAD-RDD&IND code can be used with minimal training. For example, the user can select the major characteristics of the problem-event type, source term, and decision type from the left side of the interface and then calculate the results with the default assumptions for the exposure scenarios. More in-depth analysis would include specifying site-specific exposure scenario characteristics in the right side of the interface. The procedures for data entry and results viewing are self-explanatory. This is because common window maneuvering features and text instructions were incorporated in the interface design. General and context-specific help are available to aid users entering parameter values, as well. The RESRAD-RDD&IND computer code gives the user the option to select either an RDD or IND incident for analysis. For an RDD event analysis, 11 radionuclides (Am-241, Cf-252, Cm-244, Co-60, Cs-137, Ir-192, Po-210, Pu-238, Pu-239, Ra-226, and Sr-90) are included. These 11 radionuclides are the radionuclides most likely used for an RDD. More than 90 radionuclides can be selected for an IND event analysis. Initial default concentrations are provided for 44 radionuclides for a uranium-fueled IND event. These 44 radionuclides are those that would contribute significantly to the radiation dose associated with a uranium-fueled bomb detonation. The radionuclides generated from ingrowth of these 44 initial radionuclides are also automatically included in the analysis. Pu-239, Cs-134m, Ru-105, and Rb-89 and their progeny can be selected for analysis if they are detected and their concentrations are determined. This user’s guide, which is Volume 2 of the User’s Manual for RESRAD-RDD&IND Code Version 2, provides instructions to users on how to install the RESRAD-RDD&IND code, navigate the interface, and use the various features, including those discussed above, to set up an analysis and view/print the results in text outputs. Volume 1 of the User’s Manual for RESRAD-RDD&IND Code Version 2 (Yu et al. 2026), which contains descriptions of the methodology and theoretical basis for dose modeling and the mathematical equations implemented in the code, can be accessed and viewed through the Help menu in the code or can be downloaded from the RESRAD website (https://resrad.evs.anl.gov).

22 GENERAL STUDIES OF NUCLEAR REACTORS

TEMPEST3 surface runoff water chemistry and organic matter composition

Coastal flooding, driven by storm surges and sea level rise, can mobilize organic matter (OM) via runoff, while introducing compositionally distinct OM (e.g., estuarine OM) into the system. To understand event-scale OM dynamics, we monitored source waters and surface runoff during an ecosystem-scale field manipulation experiment, TEMPEST (Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments), in June 2024. The TEMPEST experiment is part of the COMPASS-FME (Coastal Observations, Mechanisms, and Predictions Across Systems and Scales – Field, Measurements, and Experiments) project and designed to investigate biogeochemical and ecological impacts of freshwater and seawater flooding on coastal terrestrial-aquatic interface ecosystems by simulating freshwater and seawater storm events in two 2000m2 coastal upland forest plots (freshwater and brackish seawater plots). The temporal coverage of this dataset is during the TEMPESTⅢ event (June 11-13, 2024). This dataset contains: - Surface runoff discharge measured by flumes - Sensor data (specific conductivity, salinity, dissolved oxygen, and temperature) - Particle size distribution - Total suspended sediment concentrations (TSS), particulate and dissolved organic carbon (POC, DOC) concentrations, total nitrogen and total dissolved nitrogen (TN, TDN) concentrations - Bulk particulate and dissolved OM compositions (stable C and N isotopes of particulates and optical measurements of chromophoric dissolved OM) - High resolution mass spectrometry analysis data - Water isotope data All data files are plain-text CSV (comma-separated value), and no special software is required to read them.

COMPASS-FME