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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

On the Natural Structure of Amino Acid Patterns in Families of Protein Sequences

All known terrestrial proteins are coded as continuous strings of ≈20 amino acids. The patterns formed by the repetitions of elements in groups of finite sequences describes the natural architectures of protein families. We present a method to search for patterns and groupings of patterns in protein sequences using a mathematically precise definition for “repetition”, an efficient algorithmic implementation and a robust scoring system with no adjustable parameters. We show that the sequence patterns can be well-separated into disjoint classes according to their recurrence in nested structures. The statistics of the occurrences of patterns indicate that short repetitions are sufficient to account for the differences between natural families and randomized groups of sequences by more than 10 standard deviations, while contiguous sequence patterns shorter than 5 residues are effectively random in their occurrences. A small subset of patterns is sufficient to account for a robust ”familiarity” definition between arbitrary sets of sequences.

Pablo Turjanski↗
NASA NTRSDate not supplied

Recent Activities of the Marshall Space Flight Center Natural Environments Branch Terrestrial & Planetary Environments Team

- The Natural Environments (NE) Branch is within the MSFC Engineering Directorate’s Spacecraft and Vehicle System (S&VS) Department - S&VS plans, performs and directs the technical Design, Analysis, Test, Evaluation, Verification, Integration, and Applied Research and Development of future Spacecraft and Launch Vehicle Systems - Current programs we support - Space Launch System, Human Landing System, Mars Ascent Vehicle, Orion, Gateway - NE maintains expertise in terrestrial, space, and planetary natural environments definition, characterization, and analysis - Responsibility to disseminate this expertise to Agency programs and projects in support of environment definition, requirements development, vehicle development, operations, and sustaining engineering through program’s life cycle - Branch has two teams; Terrestrial and Planetary Environments (T&PE) and Space Environments (SE) - T&PE has extensive expertise characterizing terrestrial surface and atmospheric (aloft) environments - Winds, temperature, humidity, pressure, lightning, density, precipitation, radiant energy, aerosols, sea state, sea surface temperature - Maintains climatological databases for engineering analyses

Ryan K. Decker↗
NASA NTRSDate not supplied

From Natural Language Requirements to the Verification of Programmable Logic Controllers: Integrating FRET into PLCverif

PLCverif is an actively developed project at CERN, enabling the formal verification of Programmable Logic Controller (PLC) programs in critical systems. In this paper, we present our work on improving the formal requirements specification experience in PLCverif through the use of natural language. To this end, we integrate NASA’s FRET, a formal requirement elicitation and authoring tool, into PLCverif. FRET is used to specify formal requirements in structured natural language, which automatically translates into temporal logic formulae. FRET’s output is then directly used by PLCverif for verification purposes. We discuss practical challenges that PLCverif users face when authoring requirements and the FRET features that help alleviate these problems. We present the new requirement formalization workflow and report our experience using it on two critical CERN case studies.

FRET↗
NASA NTRS2022

Virtual Assistant for First Responders Using Natural Language Understanding and Optical Character Recognition

Commercial deep learning capabilities are available for many applications such as computer vision processing and intelligent chat bots. The Google Cloud Platform product Google Dialogflow provides lifelike conversational artificial intelligence (AI) using machine learning (ML) to generate natural conversations between computers and humans. This ML utilizes natural language understanding (NLU) to recognize a user’s intent and extracts key information into a form of entities. We have developed a user-friendly application through understanding the hazardous material database, first aid safety guidelines and observing the process of first responders who access this information in the field. We created the Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL) virtual assistant using Dialogflow1 and TensorFlow2 paired with EasyOCR.3 The chatbot supports first responders by providing voice interaction which helps limit additional steps such as browsing through multiple categories when searching for information. Using feedback from our field interviews, the voice interface has been developed to enable the first responder to focus on the immediate emergency. With less distractions, the first responder is able to engage the incident more effectively. The partial hands-free TruePAL chatbot assistant improves the accessibility to the correct guidance by an average of 1.9 seconds compared to the widely used application, NIH WISER, which requires full attention to operate. We combined this intelligent chatbot with a separate visual processing capability to produce hazardous signage analysis and generate the proper guidance for first responders. With the evolving functionality of AI tools, the use of virtual assistants in first responder technology will be an advancement, benefiting the safety of both first responders and civilians.

Chow, Edward↗
NASA NTRS2023

On the Physical Nature of the So-Called Prominence Tornadoes

The term ‘tornado’ has been used in recent years to describe several solar phenomena, from large-scale eruptive prominences to small-scale photospheric vortices. It has also been applied to the generally stable quiescent prominences, sparking a renewed interest in what historically was called ‘prominence tornadoes’. This paper carries out an in-depth review of the physical nature of ‘prominence tornadoes’, where their name subconsciously makes us think of violent rotational dynamics. However, after careful consideration and analysis of the published observational data and theoretical models, we conclude that ‘prominence tornadoes’ do not differ in any substantial way from other stable solar prominences. There is simply no unequivocal observational evidence of sustained and coherent rotational movements in quiescent prominences that would justify a distinct category of prominences sharing the name with the well-known atmospheric phenomenon. The visual impression of the column-like silhouettes, the perceived helical motions, or the suggestive Doppler-shift patterns all have a simpler, more likely explanation. They are a consequence of projection effects combined with the presence of oscillations and/or counter-streaming flows. ‘Prominence tornadoes’ are thus just manifestations of the complex nature of solar prominences when observed in specific projections. These coincidental viewing angles, together with the presence of fine-structure dynamics and simple yet profoundly distorting projection effects, may sometimes play havoc with our intuitive understanding of perceived shapes and motions, leading to the incorrect analogy with atmospheric tornadoes.

Stanislav Gunar↗
NASA NTRS2023

Drought and Deluge: the Recurrence of Hydroclimate Extremes During the Past 600 Years in Eastern Australia’s Natural Resource Management (NRM) Clusters

Recent extremes of flood and drought across Australia have raised questions about the recurrence of such rare events and highlighted the importance of understanding multi-decadal climate variability. However, instrumental records over the past century are too short to adequately characterise climate variability on multi-decadal and longer timescales or robustly determine extreme event frequencies and their duration. Palaeoclimate reconstructions can provide much-needed information to help address this problem. Here, we use the 600-year hydroclimate record captured in the eastern Australian and New Zealand Drought Atlas (ANZDA) to analyse drought and pluvial frequency trends for East Australian Natural Resource Management (NRM) clusters. This partitioning of the drought atlas grid points into recognised biophysical areas (i.e. NRM clusters) revealed their differences and similarities in drought intensity and pluvial events over time. We find sustained multi-decadal periods of a wet–dry geographic 'seesaw' between eastern to central and southern NRMs (e.g. 1550–1600 CE and 1700–1750 CE). In contrast, other periods reveal spatially consistent wetting (e.g. 1500–1550 CE) or drying (e.g. 1750–1800 CE). Emerging hot spot analysis further shows that some areas that appear naturally buffered from severe drought during the instrumental period have a greater exposure risk when the longer 600-year record is considered. These findings are particularly relevant to management plans when dealing with the impacts of climate extremes developed at regional scales. Our results demonstrate that integrating and extending instrumental records with palaeoclimate datasets will become increasingly important for developing robust and locally specific extreme event frequency information for managing water resources.

Australian hydroclimate↗
NASA NTRS2008

Constellation Program Design Specification for Natural Environments

The Design Specification for Natural Environments (DSNE) completes environment-related requirements from architecture, system level, and lower tier documents by specifying the ranges of environmental conditions that must be accounted for by the design of all Constellation Program (CxP) elements. As such, it forms a part of those specifications. It has been pulled out into a separate document to assure clarity and consistency and to prevent the requirements documents from becoming cluttered with extensive amounts of technical material. It is based on the Constellation Design Reference Missions and Operational Concepts Document and the models, data, and environment descriptions in the Natural Environments Definition for Design (NEDD). The NEDD provides additional detailed environment data and model descriptions to support analytical studies for Constellation (Cx) systems.

Constellation Program↗
NASA NTRSDate not supplied

Emulating Nature: The Future of Sustainable Design with BIDARA

BIDARA (Bio-inspired Design and Research Assistant) is a GPT-4o chatbot that was instructed to help scientists and engineers understand, learn from, and emulate the strategies used by living things to create sustainable designs and technologies. BIDARA can guide users through the Biomimicry Institute’s Design Process, a step-by-step method to propose biomimetic solutions to challenges. This process includes defining the problem, biologizing the challenge, discovering natural models, abstracting design strategies, and emulating nature's lessons.

Brandon Ruffridge↗
NASA NTRSDate not supplied

Contextualizing Air Traffic Management Conversations using Natural Language Understanding

Efficient management of air traffic and mitigation of delays depend on extracting actionable information from unstructured data, such as dialogues from the Federal Aviation Administration’s (FAA’s) Air Traffic Control System Command Center (ATCSCC) telecons. This study presents a pipeline utilizing Natural Language Processing (NLP) methods for Intent Classification (IC) and Slot Filling (SF) to identify and extract Traffic Management Initiatives (TMIs) from aviation-specific dialogues. We leveraged DeBERTa, a pre-trained transformer model, and fine-tuned it to the nuances of the aviation domain. Despite challenges posed by annotation complexities, the IC model achieved promising results with a weighted average F1-score of 0.81. Our results are close to those of human annotators, which demonstrates the model’s strong alignment with human-level performance. The SF model also showed strong performance, achieving a weighted F1-score of 0.97, which demonstrates its effectiveness in accurately predicting key slots. Our analysis revealed limitations in handling less frequent intents and slot labels due to data sparsity, motivating future efforts to adopt joint IC-SF modeling and data augmentation strategies. This research highlights the potential of domain-specific NLP to streamline decision-making in the aviation industry and improve the management of TMIs.

Air Traffic Control Management↗