Results for “knowledge extraction” — Page 17

NASA NTRS2024

ISS External Microorganisms: A Planetary Protection Experiment to Inform Requirements for Crewed Missions to Mars

We have developed, tested, and flown a caddy capable of collecting aseptic samples from external surfaces of the ISS (International Space Station). The sampling caddy is certified for use during US EVA (extra vehicular activity) and was launched to ISS in the summer of 2023. We are scheduled to collect samples from 6 locations outside ISS during an EVA in May of 2024. We will freeze these samples at -80°C on orbit and return them to Earth. We will then extract and sequence any DNA collected during the EVA using next generation sequencing technologies to characterize the community composition and function of each sample. Measuring the type and quantity of microbes present on the exterior of ISS will allow us to address knowledge gap 2B, “Acceptable levels of microbial/organic releases from humans and support systems” described in a 2019 COSPAR report. Collecting data about microbial release from current crewed vehicles will inform requirements for acceptable leak rates for future crewed missions to Mars. The sampling kit consists of eight commercially available, sterile, DNA free, macrofoam swabs ( 23 mm. diameter ) installed in custom aluminum end effectors. Each end effector is housed in and individual aluminum canister. Each canister contains a 0.2 μm Teflon filter to allow the interior volume to accommodate pressure changes without permitting microbial contaminants to enter the sterile interior volume. A handle repurposed from the space shuttle tile repair kit is used to remove the end effector from the sample canister, collect a sample by swabbing a surface and then replace the end effector in its canister. The canisters and end effectors were cleaned and assembled on Earth. Prior to installing the sterile swab the canisters and end effectors were sterilized in an autoclave at 134°C, 215 kPA, for 7 min.. The final assembly occurred in a sterilized class II biosafety cabinet. We will collect six samples from the 1) airlock vestibule, 2) airlock thermal cover, 3) a gap in the micrometeorite shielding near the airlock, 4) a handrail near the airlock, 5) the CDRA (Carbon Dioxide Removal Assembly) vent, and 6) the VES (Vacuum Exhaust System) vent. The remaining two swabs will be reserved as controls. One swab will be exposed during the EVA without touching any surfaces to act as a blank. The final swab will remain sealed until the entire sampling kit is returned to earth. Based on previously published results from the Russian segment, we hypothesize that there will be detectable microbes at some or all of these locations. Ground-based testing of this sampling caddy confirms that the swabs remain sterile as the canisters transition in and out of vacuum. We were able to retrieve, viable bacterial and fungal cells as well as DNA from samples collected from US space suits during vacuum chamber tests lasting as long as seven hours. Based on these results and feedback from the test subjects the sampling caddy was modified to improve ergonomics and meet US EVA safety requirements. Bayonet probes were added to the sides of the sample kit as alternate mounting points. Additional locking features were added to the end effector and the filter stack to prevent inadvertent release during use. The opening mechanism was changed from one where the end effector was rocked laterally to defeat a ball detent to a twist-to-open threaded closure for similar reasons. Demonstrating, this sampling caddy’s effectiveness during a US EVA will allow us to address knowledge gaps identified in COSPAR reports and begin to define planetary protection requirements for life support systems on crewed missions to mars. This kit could also be used to collect contamination control samples during Artemis missions to verify requirements and could be easily modified for robotic sample collection.

Planetary Protection↗

NASA NTRSDate not supplied

ISS External Microorganisms: Collecting Planetary Protection Samples During Extravehicular Activity

We have developed, tested, and flown a caddy capable of collecting aseptic samples from external surfaces of the ISS (International Space Station). The sampling caddy is certified for use during US EVA (extra vehicular activity) and was launched to ISS in the summer of 2023. We are scheduled to collect samples from 6 locations outside ISS during an EVA in May of 2024. We will freeze these samples at -80°C on orbit and return them to Earth. We will then extract and sequence any DNA collected during the EVA using next generation sequencing technologies to characterize the community composition and function of each sample. Measuring the type and quantity of microbes present on the exterior of ISS will allow us to address knowledge gap 2B, “Acceptable levels of microbial/organic releases from humans and support systems” described in a 2019 COSPAR report. Collecting data about microbial release from current crewed vehicles will inform requirements for acceptable leak rates for future crewed missions to Mars. The sampling kit consists of eight commercially available, sterile, DNA free, macrofoam swabs ( 23 mm. diameter ) installed in custom aluminum end effectors. Each end effector is housed in and individual aluminum canister. Each canister contains a 0.2 μm Teflon filter to allow the interior volume to accommodate pressure changes without permitting microbial contaminants to enter the sterile interior volume. A handle repurposed from the space shuttle tile repair kit is used to remove the end effector from the sample canister, collect a sample by swabbing a surface and then replace the end effector in its canister. The canisters and end effectors were cleaned and assembled on Earth. Prior to installing the sterile swab the canisters and end effectors were sterilized in an autoclave at 134°C, 215 kPA, for 7 min.. The final assembly occurred in a sterilized class II biosafety cabinet. We will collect six samples from the 1) airlock vestibule, 2) airlock thermal cover, 3) a gap in the micrometeorite shielding near the airlock, 4) a handrail near the airlock, 5) the CDRA (Carbon Dioxide Removal Assembly) vent, and 6) the VES (Vacuum Exhaust System) vent. The remaining two swabs will be reserved as controls. One swab will be exposed during the EVA without touching any surfaces to act as a blank. The final swab will remain sealed until the entire sampling kit is returned to earth. Based on previously published results from the Russian segment, we hypothesize that there will be detectable microbes at some or all of these locations. Ground-based testing of this sampling caddy confirms that the swabs remain sterile as the canisters transition in and out of vacuum. We were able to retrieve, viable bacterial and fungal cells as well as DNA from samples collected from US space suits during vacuum chamber tests lasting as long as seven hours. Based on these results and feedback from the test subjects the sampling caddy was modified to improve ergonomics and meet US EVA safety requirements. Bayonet probes were added to the sides of the sample kit as alternate mounting points. Additional locking features were added to the end effector and the filter stack to prevent inadvertent release during use. The opening mechanism was changed from one where the end effector was rocked laterally to defeat a ball detent to a twist-to-open threaded closure for similar reasons. Demonstrating, this sampling caddy’s effectiveness during a US EVA will allow us to address knowledge gaps identified in COSPAR reports and begin to define planetary protection requirements for life support systems on crewed missions to mars. This kit could also be used to collect contamination control samples during Artemis missions to verify requirements and could be easily modified for robotic sample collection.

Planetary Protection↗

NASA NTRS1992

Knowledge based imaging for terrain analysis

A planetary rover will have various vision based requirements for navigation, terrain characterization, and geological sample analysis. In this paper we describe a knowledge-based controller and sensor development system for terrain analysis. The sensor system consists of a laser ranger and a CCD camera. The controller, under the input of high-level commands, performs such functions as multisensor data gathering, data quality monitoring, and automatic extraction of sample images meeting various criteria. In addition to large scale terrain analysis, the system's ability to extract useful geological information from rock samples is illustrated. Image and data compression strategies are also discussed in light of the requirements of earth bound investigators.

Holben, Rick↗

NASA NTRS1994

Multiresolutional schemata for unsupervised learning of autonomous robots for 3D space operation

This paper describes a novel approach to the development of a learning control system for autonomous space robot (ASR) which presents the ASR as a 'baby' -- that is, a system with no a priori knowledge of the world in which it operates, but with behavior acquisition techniques that allows it to build this knowledge from the experiences of actions within a particular environment (we will call it an Astro-baby). The learning techniques are rooted in the recursive algorithm for inductive generation of nested schemata molded from processes of early cognitive development in humans. The algorithm extracts data from the environment and by means of correlation and abduction, it creates schemata that are used for control. This system is robust enough to deal with a constantly changing environment because such changes provoke the creation of new schemata by generalizing from experiences, while still maintaining minimal computational complexity, thanks to the system's multiresolutional nature.

Lacaze, Alberto↗

NASA NTRS2023

Extending Aquatic Spectral Information with the First Radiometric IR-B Field Observations

Planetary radiometric observations enable remote sensing of biogeochemical parameters to describe spatiotemporal variability in aquatic ecosystems. For approximately the last half century, the science of aquatic radiometry has established a knowledge base using primarily, but not exclusively, visible wavelengths. Scientific subdisciplines supporting aquatic radiometry have evolved hardware, software, and procedures to maximize competency for exploiting visible wavelength information. This perspective culminates with the science requirement that visible spectral resolution must be continually increased to extract more information. Other sources of information, meanwhile, remain underexploited, particularly information from nonvisible wavelengths. Herein, absolute radiometry is used to evaluate spectral limits for deriving and exploiting aquatic data products, specifically the normalized water-leaving radiance, Γ(λ)⁠, and its derivative products. Radiometric observations presented herein are quality assured for individual wavebands, and spectral verification is conducted by analyzing celestial radiometric results, comparing agreement of above- and in-water observations at applicable wavelengths, and evaluating consistency with bio-optical models and optical theory. The results presented include the first absolute radiometric field observations of Γ(λ) within the IR-B spectral domain (i.e. spanning 1400–3000 nm), which indicate that IR-B signals confer greater and more variable flux than formerly ascribed. Black-pixel processing, a routine correction in satellite and in situ aquatic radiometry wherein a spectrum is offset corrected relative to a nonvisible waveband (often IR-B or a shorter legacy waveband) set to a null value, is shown to degrade aquatic spectra and derived biogeochemical parameters.

aquatic optics↗

NASA NTRS2003

Complex Organics from Laboratory Simulated Interstellar Ices

Many of the volatiles in interstellar dense clouds exist in ices surrounding dust grains. The low temperatures of these ices (T < 50 K) preclude most chemical reactions, but photolysis can drive reactions that produce a suite of new species, many of which are complex organics. We study the UV and proton radiation processing of interstellar ice analogs to explore links between interstellar chemistry, the organics in comets and meteorites, and the origin of life on Earth. The high D/H ratios in some interstellar species, and the knowledge that many of the organics in primitive meteorites are D-enriched, suggest that such links are plausible. Once identified, these species may serve as markers of interstellar heritage of cometary dust and meteorites. Of particular interest are our findings that UV photolysis of interstellar ice analogs produce molecules of importance in current living organisms, including quinones, amphiphiles, and amino acids. Quinones are essential in vital metabolic roles such as electron transport. Studies show that quinones should be made wherever polycyclic aromatic hydrocarbons are photolyzed in interstellar ices. In the case of anthracene-containing ices, we have observed the production of 9-anthrone and 9,10 anthraquinone, both of which have been observed in the Murchison meteorite. Amphiphiles are also made when mixed molecular ices are photolyzed. These amphiphiles self-assemble into fluorescent vesicles when placed in liquid water, as do Murchison extracts. Both have the ability to trap an ionic dye. Photolysis of plausible ices can also produce alanine, serine, and glycine as well as a number of small alcohols and amines. Flash heating of the room temperature residue generated by such experiments generates mass spectral distributions similar to those of IDPs. The detection of high D/H ratios in some interstellar molecular species, and the knowledge that many of the organics, such as hydroxy and amino acids, in primitive meteorites are D-enriched provides evidence for a connection between intact organic material in the interstellar medium and in meteorites. Thus, some of the oxidized aromatics, amphiphiles, amino acids, hydroxy acids, and other compounds found in meteorites may have had an interstellar ancestry and not solely a product of parent body aqueous alteration. Such compounds should also be targeted for searches of organics in cometary dust.

Dworkin, J. P.↗

DOE OSTI2026

Weak-charge form-factor determination at the electron-ion collider

Determining the weak charge form factor, 𝐹 𝑊 ⁡(𝑄 2 ), of nuclei over a continuous range of momentum transfers, 0 ≲ 𝑄 2 ≲ 0.1 GeV 2 , is essential for mapping out the distribution of neutrons in nuclei. The neutron density distribution has significant implications for a broad range of areas, including studies of nuclear structure, neutron stars, and physics beyond the Standard Model. Currently, our knowledge of 𝐹 𝑊 ⁡(𝑄 2 ) comes primarily from fixed target experiments that measure the parity-violating asymmetry in coherent elastic electron-ion scattering. Fixed target experiments, such as CREX and PREX-1,2, have provided high-precision weak charge form factor extractions for the 48 Ca and 208 Pb nuclei, respectively. However, a major limitation of fixed target experiments is that they each provide data only at a single value of 𝑄 2 . With the proposed electron-ion collider (EIC) on the horizon, we explore its potential to impact the determination of the weak charge form factor. While it cannot compete with the precision of fixed target experiments, it can provide data over a wide and continuous range of 𝑄 2 values, and for a wide variety of nuclei. We show that with data corresponding to an integrated luminosity of ℒ ∼ 500/𝐴 fb −1 , where 𝐴 is the nucleus atomic weight, the EIC can significantly impact constraints by lifting degeneracies in theoretical models of the neutron density distribution. Ensuring EIC detector coverage at low 𝑄 2 and large negative pseudorapidities will be essential for such 𝐹 𝑊 ⁡(𝑄 2 ) measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Multi-Tiered Estimation for Correlation Spectroscopy in 3D (MTECS3D) v0.1

This innovative software estimates rotational diffusion coefficients of particles from X-ray photon correlation spectroscopy (XPCS) data of monodispersed particle systems. It is the first method capable of extracting rotational diffusion information from three-dimensional particle systems using XPCS. Using the angular-temporal cross-correlation of the XPCS images, this software is able to estimate the rotational diffusion coefficients with only a few percent relative errors while requiring minimal prior knowledge of particle structures. This software enhances XPCS analysis capabilities, allowing researchers to study translational and rotational Brownian dynamics of particles in suspension across various temporal and spatial scales.

Hu, Zixi↗

DOE OSTI2025

Microwave-assisted catalytic conversion of waste biomass and plastic feedstocks via thermochemical routes

Microwave-assisted catalytic conversion of waste feedstocks to fuels and value-added chemicals shows incredible promise as an efficient pathway to support the U.S. Department of Energy’s vision toward strengthening the nation’s energy independence. Microwave-heated systems have the potential to outperform conventional technologies through energy-efficient heating and improved product selectivity. This chapter emphasizes microwave-assisted catalytic approaches for waste conversion, allowing maximum energy recovery and extraction of valuable chemicals from waste feedstock such as biomass and plastics while reducing undesired byproducts. A gap remains in understanding how microwaves interact with materials to enable rapid and selective heating, which is crucial for improving catalytic efficiency. This chapter attempts to address this knowledge gap by proposing mechanisms that explain the microwave-catalytic interactions for efficient conversion of biomass-plastic wastes. In addition, comparisons with conventional catalytic technologies as well as the potential for scale ups and future commercialization of microwave-catalytic waste conversion technologies are also discussed.

microwave-assisted catalytic conversion↗

DOE OSTI2025

PDF Entity Annotation Tool (PEAT)

While different text mining approaches – including the use of Artificial Intelligence (AI) and other machine based methods - continue to expand at a rapid pace, the tools used by researchers to create the labeled datasets required for training, modeling, and evaluation remain rudimentary. Labeled datasets contain the target attributes the machine is going to learn; for example, training an algorithm to delineate between images of a car or truck would generally require a set of images with a quantitative description of the underlying features of each vehicle type. Development of labeled textual data that can be used to build natural language machine learning models for scientific literature is not currently integrated into existing manual workflows used by domain experts. Published literature is rich with important information, such as different types of embedded text, plots, and tables that can all be used as inputs to train ML/natural language processing (NLP) models, when extracted and prepared in machine readable formats. Currently, both normalized data extraction of use to domain experts and extraction to support development of ML/NLP models are labor intensive and cumbersome manual processes. Automatic extraction of data and information from formats such as PDFs that are optimized for layout and human readability, not machine readability. The PDF (Portable Document Format) Entity Annotation Tool (PEAT) was developed with the goal of allowing users to annotate publications within their current print format, while also allowing those annotations to be captured in a machine-readable format. One of the main issues with traditional annotation tools is that they require transforming the PDF into plain text to facilitate the annotation process. While doing so lessens the technical challenges of annotating data, the user loses all structure and provenance that was inherent in the underlying PDF. Also, textual data extraction from PDFs can be an error prone process. Challenges include identifying sequential blocks of text and a multitude of document formats (multiple columns, font encodings, etc.). As a result of these challenges, using existing tools for development of NLP/ML models directly from PDFs is difficult because the generated outputs are not interoperable. We created a system that allows annotations to be completed on the original PDF document structure, with no plain text extraction. The result is an application that allows for easier and more accurate annotations. In addition, by including a feature that grants the user the ability to easily create a schema, we have developed a system that can be used to annotate text for different domain-centric schemas of relevance to subject matter experts. Different knowledge domains require distinct schemas and annotation tags to support machine learning.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Spectroscopic Online Monitoring: Using a Multi-Track Visible Spectrometer to Facilitate a Mass Balance Study in a Simulated TALSPEAK Process

Nuclear energy is a promising low-carbon energy candidate to meet the increased demand for green energy, where the integration of fuel recycling can have significant benefits for material usage and waste reduction. Utilizing in situ monitoring tools can provide ample opportunities to better control and safeguard nuclear material recycle processes while also offering knowledge and insight into real-time solution properties. The simultaneous measurement of analytical targets in multiple process locations can enable real-time mass balance and material accountancy calculations. This is demonstrated here with a mass balance study of Nd 3+ on countercurrent aqueous/organic metal extraction within a single centrifugal contactor. The Nd 3+ concentration was simultaneously monitored at the inlets and outlets of both aqueous and organic phases using a visible absorbance detector that allowed for the simultaneous measurement of up to six locations. The Nd 3+ concentration was calculated by using chemical data science algorithms, where model training sets were collected on a single track of the detector. The discussion includes addressing the challenges of using a model collected on a single track and applying it as a model across the other tracks on the detector. Each track of the detector corresponds to one measurement location on the contactor. The difference in the integrated moles of Nd 3+ between the inlet and outlet at the end of the experiment was near zero, indicating that the mass balance of this experiment was maintained. Overall, the online spectroscopic monitoring was able to follow changing solution conditions and accurately measure the concentration of Nd 3+ in different locations within the contactor system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Using Generative AI to implement the discrepancy checker for a Nearly Autonomous Management and Control System for Advanced Reactors

Developments related to generative artificial intelligence (AI) have brought a major breakthrough in AI. These developments are rapidly accelerating developments in different science and engineering applications. Nearly Autonomous Management and Control (NAMAC) system provides recommendations to the operator for maintaining the safety and performance of the reactor. The discrepancy checker (DC) is an important component of the NAMAC) system, whose goal is to determine if the plant is moving towards the expected system state after the control actions are injected. In this work, we explore generative AI methods, particularly, a generative pretrained transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert the operator in situations outside NAMAC’s scope and act as a chatbot the operator can use to retrieve relevant information. This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs are trained on huge amounts of undisclosed general domain datasets. We explored two methods to adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A small knowledge base (information file) that encompasses rules for DC implementation and some general information related to NAMAC has been created to support DC implementation using GPT. In this work, the GPT-based DC implementations have been tested for their reasoning abilities, comprehension, information retrieval, and extraction abilities. It should be noted that this paper only presents a preliminary study to test the feasibility of DC implementation using generative AI technology. Given the potential risks and severe consequences associated with nuclear reactor applications, combined with the black-box nature of AI, extensive offline and online testing and reliability analyses of GPT-based DCs are needed for further developing such capabilities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1997

Rocket Engine Numerical Simulator (RENS)

Work is being done at three universities to help today's NASA engineers use the knowledge and experience of their Apolloera predecessors in designing liquid rocket engines. Ground-breaking work is being done in important subject areas to create a prototype of the most important functions for the Rocket Engine Numerical Simulator (RENS). The goal of RENS is to develop an interactive, realtime application that engineers can utilize for comprehensive preliminary propulsion system design functions. RENS will employ computer science and artificial intelligence research in knowledge acquisition, computer code parallelization and objectification, expert system architecture design, and object-oriented programming. In 1995, a 3year grant from the NASA Lewis Research Center was awarded to Dr. Douglas Moreman and Dr. John Dyer of Southern University at Baton Rouge, Louisiana, to begin acquiring knowledge in liquid rocket propulsion systems. Resources of the University of West Florida in Pensacola were enlisted to begin the process of enlisting knowledge from senior NASA engineers who are recognized experts in liquid rocket engine propulsion systems. Dr. John Coffey of the University of West Florida is utilizing his expertise in interviewing and concept mapping techniques to encode, classify, and integrate information obtained through personal interviews. The expertise extracted from the NASA engineers has been put into concept maps with supporting textual, audio, graphic, and video material. A fundamental concept map was delivered by the end of the first year of work and the development of maps containing increasing amounts of information is continuing. Find out more information about this work at the Southern University/University of West Florida. In 1996, the Southern University/University of West Florida team conducted a 4day group interview with a panel of five experts to discuss failures of the RL10 rocket engine in conjunction with the Centaur launch vehicle. The discussion was recorded on video and audio tape. Transcriptions of the entire proceedings and an abbreviated video presentation of the discussion highlights are under development. Also in 1996, two additional 3year grants were awarded to conduct parallel efforts that would complement the work being done by Southern University and the University of West Florida. Dr. Prem Bhalla of Jackson State University in Jackson, Mississippi, is developing the architectural framework for RENS. By employing the Rose Rational language and Booch Object Oriented Programming (OOP) technology, Dr. Bhalla is developing the basic structure of RENS by identifying and encoding propulsion system components, their individual characteristics, and cross-functionality and dependencies. Dr. Ruknet Cezzar of Hampton University, located in Hampton, Virginia, began working on the parallelization and objectification of rocket engine analysis and design codes. Dr. Cezzar will use the Turbo C++ OOP language to translate important liquid rocket engine computer codes from FORTRAN and permit their inclusion into the RENS framework being developed at Jackson State University. The Southern University/University of West Florida grant was extended by 1 year to coordinate the conclusion of all three efforts in 1999.

Davidian, Kenneth O.↗

NASA NTRS1988

Knowledge-based low-level image analysis for computer vision systems

Two algorithms for entry-level image analysis and preliminary segmentation are proposed which are flexible enough to incorporate local properties of the image. The first algorithm involves pyramid-based multiresolution processing and a strategy to define and use interlevel and intralevel link strengths. The second algorithm, which is designed for selected window processing, extracts regions adaptively using local histograms. The preliminary segmentation and a set of features are employed as the input to an efficient rule-based low-level analysis system, resulting in suboptimal meaningful segmentation.

Dhawan, Atam P.↗

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗

DOE OSTI2024

Investigating the Impacts of Direct Dissolution Conditions on the Radiolytic Longevity of Butyramide Extractants

Removing the nitric acid (HNO3) dissolution step in used nuclear fuel (UNF) reprocessing would reduce the volume of radioactive waste streams generated, thereby, improving process efficiency. A promising strategy for this is the direct dissolution of UNF that has been pretreated by voloxidation into an organic solvent composed of specialized extractants and diluent. However, removal of the aqueous HNO3 phase from the envisioned reprocessing system has the potential to drastically change the suite of radiation-induced processes occurring, and thus, alter the longevity of proposed reagents. Furthermore, the impacts of fission product and transuranic metal ion complexation on the aforementioned radiation-induced processes is poorly understood, and yet can cause significant changes in radiolytic longevity. To bridge these knowledge gaps and support the continued development of direct dissolution strategies, we present an investigation into the impacts of direct dissolution conditions on the gamma radiation-induced degradation of N,N-di-(2-ethylhexyl) butyramide (DEHBA) and N,N-di-(2-ethylhexyl)isobutyramide (DEHiBA) ligands—candidate replacements for tributyl phosphate—in pre-equilibrated n-dodecane solvent in the presence and absence of envisioned loading amounts of uranium.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

NASA NTRS2000

A Lifetime of Geodesy and Geophysics: In Rememberence of Bill Kaula

In the early 1960's the secrets that knowledge of the Earth's gravity field would eventually reveal about the processes that govern our planet were yet to be appreciated. It was the beginning of a new science known as space geodesy, which arose at a time when most efforts were devoted to understanding how to extract precise measurements of Earth structure and motions from an orbiting spacecraft. Bill Kaula was central to that beginning and showed the way for many who were to follow, both in time and in the development of approaches most likely to yield results. Bill laid out the theory, analyzed the data, and argued strenuously for a spacecraft mission devoted to measuring gravity to make it all come true in the way he knew it really could. That mission, GRACE, was a long time coming and Bill would not see its final staging, but his influence in making it happen was everywhere. With time, the concepts for measuring the static gravity field of the Earth and terrestrial planets became well advanced, although not universally agreed upon, and certainly not by Bill, who was always eager to argue and challenge traditional methods and thinking. The extension of space geodetic techniques to the planets and the development of new techniques to measure time variations in gravity have recently brought geodesy even closer to the geophysical processes that Bill sought to understand. This presentation will contain a little geodesy, a little history, and a little reminiscing about the leader in our field.

Smith, David E.↗