Human Research Program (HRP) Approach - SANS
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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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Research program on methodologies of managing research and development projects
Research in Controlled Ecological Life Support Systems (CELSS) conducted by NASA indicate that plant based systems are feasible candidates for human support in space. Ames has responsibility for research and development, systems integration and control, and space flight experiment portions of the CELSS program. Important areas for development of new methods and technologies are biomass production, waste processing, water purification, air revitalization, and food processing. For the plant system, the approach was to identify the flexibility and response time for the food, water, and oxygen production, and carbon dioxide consumption processes. Tremendous increases in productivity, compared with terrestrial agriculture, were realized. Waste processing research emphasizes recycle (transformation) of human wastes, trash, and inedible biomass to forms usable as inputs to the plant production system. Efforts to improve efficiency of the plant system, select new CELSS crops for a balanced diet, and initiate closed system research with the Crop Growth Research Chambers continue. The System Control and Integration program goal is to insure orchestrated system operation of the biological, physical, and chemical operation of the biological, physical, and chemical component processors of the CELSS. Space flight studies are planned to verify adequate operation of the system in reduced gravity or microgravity environments.
Research and Technology Operating Plan Summary - FY 1971
The Materials Research (MR) Pathway within the Light Water Reactor Sustainability (LWRS) Program is charged with performing the research and development (R&D) to develop the scientific basis for understanding and predicting long-term environmental degradation behavior of materials in nuclear reactors. Furthermore, it is essential to use the mechanistic understanding of degradation phenomena in materials to develop mitigation, repair, and new material alternatives for existing components. The work will provide data and methods to assess performance of systems, structures, and components (SSCs) essential to safe and sustained reactor operations. The R&D products developed from the LWRS Program will be used by utilities, industry groups, and regulators to inform operational and regulatory requirements for materials in reactor SSCs subjected to long-term operation conditions, providing key input to both regulators and industry. The intent of this research is to help in reducing the operating costs, which may be in the form of offset maintenance costs due to better predictive models for component lifetimes, improved analysis of materials through nondestructive evaluation, reduced costs for repairs, or extended performance of plants through the selection of improved replacement materials. To best achieve this, industry experience and guidance are important as is their role in coordinated or collaborative research projects. The objectives of this report are to describe the motivation and organization of the MR Pathway within the LWRS Program, provide details on the individual research tasks within the MR Pathway, describe the outcomes and deliverables of the MR Pathway, including recent technical highlights and progress, and list the requirements for performing the research.
The objectives of this report are to describe the motivation and organization of the MR Pathway within the LWRS program; provide details on the individual research tasks within the MR Pathway; describe the outcomes and deliverables of the MR Pathway, including recent technical highlights and progress; and describe the requirements for performing this critically important research.
We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.
The program objectives are fully defined in the original proposal entitled 'Program of Research in Flight Dynamics in GW at NASA Langley Research Center,' which was originated March 20, 1975, and in the renewals of the research program from December 1, 2000 to November 30, 2001. The program in its present form includes three major topics: 1) the improvement of existing methods and development of new methods for wind tunnel and flight test data analysis, 2) the application of these methods to wind tunnel and flight test data obtained from advanced airplanes, 3) the correlation of flight results with wind tunnel measurements, and theoretical predictions. The Principal Investigator of the program is Dr. Vladislav Klein. Three Graduate Research Scholar Assistants (K. G. Mas, M. M. Eissa and N. M. Szyba) also participated in the program. Specific developments in the program during the period Dec. 1, 2001 through Nov. 30, 2002 included: 1) Data analysis of highly swept delta wing aircraft from wind and water tunnel data, and 2) Aerodynamic characteristics of the radio control aircraft from flight test.
The program objectives are fully defined in the original proposal entitled 'Program of Research in Flight Dynamics in the Joint Institute for the Advancement of Flight Sciences (JIAFS) at NASA-Langley Research Center,' which was originated March 20, 1975 and in the renewal of the research program dated December 1, 1991. The program includes four major topics: (1) the improvement of existing methods and development of new methods for flight test data analysis; (2) the application of these methods to real flight test data obtained from advanced airplanes; (3) the correlation of flight results with wind tunnel measurements; and (4) the modeling, and control of aircraft, space structures, and spacecraft.
The program objectives are fully defined in the original proposal entitled Program of Research in Flight Dynamics in GW at NASA Langley Research Center, which was originated March 20, 1975, and in the renewals of the research program from January 1, 2003 to September 30, 2005. The program in its present form includes three major topics: 1. the improvement of existing methods and development of new methods for wind tunnel and flight data analysis, 2. the application of these methods to wind tunnel and flight test data obtained from advanced airplanes, 3. the correlation of flight results with wind tunnel measurements, and theoretical predictions.
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