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At least 703 records · Page 39

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NASA NTRS1995

Optimal lunar trajectories for a combined chemical-electric propulsion spacecraft

Spacecraft which utilize electric propulsion (EP) systems are capable of delivering a greater payload fraction compared to spacecraft using conventional chemical propulsion systems. Several researchers have investigated numerous applications of low-thrust EP including a manned Mars mission, scientific missions to the outer planets, and lunar missions. In contrast, the study of optimal combined high and low-thrust spacecraft trajectories has been limited. In response to the release of NASA's 1994 Announcement of Opportunity (AO) for Discovery class interplanetary exploration missions, a preliminary investigation of a lunar comet rendezvous mission using a solar electric propulsion (SEP) spacecraft was performed. The Discovery mission (eventually named Diana) was envisioned to be a two-phase scientific exploration mission: the first phase involved exploration of the moon and second phase involved rendezvous with a comet. The initial phase began with a chemical propulsion translunar injection and chemical insertion into a lunar orbit, followed by a low-thrust SEP transfer to a circular, polar, low-lunar orbit (LLO). After scientific data was collected at the moon, the SEP spacecraft performed a spiral lunar escape maneuver to begin the interplanetary leg of the mission. After escape from the Earth-moon system, the SEP spacecraft maneuvered in interplanetary space and performed a rendezvous with a short period comet. An initial study that demonstrated the feasibility of using EP for the lunar and comet orbit transfer was performed under the grant NAG3-1581. This final report is a continuation of the initial research efforts in support of the Discovery mission proposal that was submitted to NASA Headquarters in October 1994. Section 2 discusses the lunar orbit transfer phase of the Diana mission which involves both chemical and electric propulsion stages. Section 3 discusses the chemical lunar orbit insertion (LOI) burn optimization. Finally, section 4 presents the conclusions of this research effort.

Kluever, Craig A.↗

NASA NTRS2000

A Mars Exploration Discovery Program

The Mars Exploration Program should consider following the Discovery Program model. In the Discovery Program a team of scientists led by a PI develop the science goals of their mission, decide what payload achieves the necessary measurements most effectively, and then choose a spacecraft with the capabilities needed to carry the payload to the desired target body. The primary constraints associated with the Discovery missions are time and money. The proposer must convince reviewers that their mission has scientific merit and is feasible. Every Announcement of Opportunity has resulted in a collection of creative ideas that fit within advertised constraints. Following this model, a "Mars Discovery Program" would issue an Announcement of Opportunity for each launch opportunity with schedule constraints dictated by the launch window and fiscal constraints in accord with the program budget. All else would be left to the proposer to choose, based on the science the team wants to accomplish, consistent with the program theme of "Life, Climate and Resources". A proposer could propose a lander, an orbiter, a fleet of SCOUT vehicles or penetrators, an airplane, a balloon mission, a large rover, a small rover, etc. depending on what made the most sense for the science investigation and payload. As in the Discovery program, overall feasibility relative to cost, schedule and technology readiness would be evaluated and be part of the selection process.

Hansen, C. J.↗

DOE OSTI2021

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. Furthermore, the model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots

Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown function given input/output data. Type 2: Consider a collection of variables and functions, some of which are unknown, indexed by the nodes and hyperedges of a hypergraph (a generalized graph where edges can connect more than two vertices). Given partial observations of the variables of the hypergraph (satisfying the functional dependencies imposed by its structure), approximate all the unobserved variables and unknown functions. Type 3: Expanding on Type 2, if the hypergraph structure itself is unknown, use partial observations of the variables of the hypergraph to discover its structure and approximate its unknown functions. These hypergraphs offer a natural platform for organizing, communicating, and processing computational knowledge. While most scientific problems can be framed as the data-driven discovery of unknown functions in a computational hypergraph whose structure is known (Type 2), many require the data-driven discovery of the structure (connectivity) of the hypergraph itself (Type 3). We introduce an interpretable Gaussian Process (GP) framework for such (Type 3) problems that does not require randomization of the data, access to or control over its sampling, or sparsity of the unknown functions in a known or learned basis. Its polynomial complexity, which contrasts sharply with the super-exponential complexity of causal inference methods, is enabled by the nonlinear ANOVA capabilities of GPs used as a sensing mechanism.

Science & Technology - Other Topics↗

DOE OSTI2020

The atomic bombings: facts, myths and more

Seventy-five years later, the Trinity test of July 16, 1945 is still rightly remembered as a monumental scientific achievement. The test has a multifaceted legacy representing incredible discovery, unparalleled hope and unprecedented peril. The weapons designed and tested at wartime Los Alamos were not intended to deter, but to destroy: On August 6, 1945, the world’s first weaponized nuclear device was used for that very purpose. Little Boy, a gun-assembled enriched uranium weapon, detonated over Hiroshima that morning destroying several square miles of the city and killing tens of thousands. Three days later, Fat Man, an imploding plutonium weapon, destroyed Nagasaki. On August 14, an armistice was declared and a few weeks later World War II came to an official end. We’ve all heard these historical facts before, but was it really that simple? Nuclear strike + nuclear strike = unconditional surrender? Bringing World War II to an end was an arduous, complex, and very tenuous undertaking with important historical nuances to consider.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2024

Unsupervised physics-informed disentanglement of multimodal data

Here, we introduce physics-informed multimodal autoencoders (PIMA) - a variational inference framework for discovering shared information in multimodal datasets. Individual modalities are embedded into a shared latent space and fused through a product-of-experts formulation, enabling a Gaussian mixture prior to identify shared features. Sampling from clusters allows cross-modal generative modeling, with a mixture-of-experts decoder that imposes inductive biases from prior scientific knowledge and thereby imparts structured disentanglement of the latent space. This approach enables cross-modal inference and the discovery of features in high-dimensional heterogeneous datasets. Consequently, this approach provides a means to discover fingerprints in multimodal scientific datasets and to avoid traditional bottlenecks related to high-fidelity measurement and characterization of scientific datasets.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1974

Small astronomy satellite-A, Uhuru data analysis

Objectives were to conduct observations with the first satellite entirely devoted to X-ray astronomy and to analyze the results obtained. A catalog of X-ray sources was generated, and results of discoveries and further detailed observations of sources were presented in scientific journals and meetings. A list of how objectives were met, a brief description of the instrument, significant results, the X-ray catalog, and a complete bibliography of results are included.

Koch, D.↗

NASA NTRS1987

The sun's spots and flares

The Solar Maximum Mission (SMM), designed to study the solar activity, was launched on February 14, 1980, just before the 1980 peak of sunspot and flare activity. The seven instruments aboard the SMM, information received by each of the instruments, and the performance of these instruments are described, together with the repair mission carried out to replace the attitude control module and the defective electronics in the satellite's observatory. The highlights of the scientific results obtained by the SMM mission and the new discoveries made are discussed, with special attention given to the flare loops, flare loop interactions, and the mass ejection events recorded.

Rust, David M.↗

NASA NTRS1997

STS-82 Flight Day 08 Highlights

The eighth day of the STS-82 mission begins with the crew, Commander Kenneth D. Bowersox, Pilot Scott J. Horowitz, Payload Commander Mark C. Lee, and Mission Specialists Gregory J. Harbaugh, Steven L. Smith, Joseph R. Tanner, and Steven A. Hawley performing the final spacewalk of the mission. Lee and Smith attach several thermal insulation blankets to three equipment compartments at the top of the Support Systems Module section of Hubble which contain key data processing, electronics and scientific instrument telemetry packages. Following the completion of that work, Lee and Smith briefly return to the airlock while flight controllers evaluated a possible glitch with one of four Reaction Wheel Assembly units in Hubble used to maneuver the telescope for its scientific observations. A spare Reaction Wheel Assembly was available aboard Discovery for a swap out during an additional spacewalk had it been necessary, but a few hours later, after further analysis, payload controllers reported that the Reaction Wheel Assembly was in excellent shape and operating at the proper speed.

Source record↗

NASA NTRS2002

NASA Langley/CNU Distance Learning Programs

NASA Langley Research Center and Christopher Newport University (CNU) provide, free to the public, distance learning programs that focus on math, science, and/or technology over a spectrum of education levels from K-adult. The effort started in 1997, and we currently have a suite of five distance-learning programs. We have around 450,000 registered educators and 12.5 million registered students in 60 countries. Partners and affiliates include the American Institute of Aeronautics and Astronautics (AIAA), the Aerospace Education Coordinating Committee (AECC), the Alliance for Community Media, the National Educational Telecommunications Association, Public Broadcasting System (PBS) affiliates, the NASA Learning Technologies Channel, the National Council of Teachers of Mathematics (NCTM), the Council of the Great City Schools, Hampton City Public Schools, Sea World Adventure Parks, Busch Gardens, ePALS.com, and Riverdeep. Our mission is based on the "Horizon of Learning," a vision for inspiring learning across a continuum of educational experiences. The programs form a continuum of educational experiences for elementary youth through adult learners. The strategic plan for the programs will evolve to reflect evolving national educational needs, changes within NASA, and emerging system initiatives. Plans for each program component include goals, objectives, learning outcomes, and rely on sound business models. It is well documented that if technology is used properly it can be a powerful partner in education. Our programs employ both advances in information technology and in effective pedagogy to produce a broad range of materials to complement and enhance other educational efforts. Collectively, the goals of the five programs are to increase educational excellence; enhance and enrich the teaching of mathematics, science, and technology; increase scientific and technological literacy; and communicate the results of NASA discovery, exploration, innovation and research. All pre-college distance learning programs support the national mathematics, science, and technology standards; support K-12 systemic change; involve educators in their development, implementation, and evaluation; and are based on alliances and partnerships. In addition the programs seek to invoke a sense of geographic, ethnic and cultural diversity by featuring schools from all over the U.S.; schools from urban, suburban, and rural areas; public, private, and religious schools; and schools with large populations of African-American, Asian and Hispanic students.

Caton, Randall↗

NASA NTRS2009

Titan Saturn System Mission

Titan is a high priority for exploration, as recommended by NASA's 2006 Solar System Exploration (SSE) Roadmap. NASA's 2003 National Research Council (NRC) Decadal Survey and ESA's Cosmic Vision Program Themes. Recent revolutionary Cassini-Huygens discoveries have dramatically escalated interest in Titan as the next scientific target in the outer solar system. This study demonstrates that an exciting Titan Saturn System Mission (TSSM) that explores two worlds of intense astrobiological interest can be initiated now as a single NASA/ESA collaboration.

Titan Saturn System Mission (TSSM)↗

NASA NTRS2020

The Integration of Life Sciences in Space: Astrobiology and Space Biology Virtual Workshops Report

A series of virtual workshops was held during June 2020 to seek ways to integrate the efforts of the astrobiology and space biology research communities under a broad umbrella of space life sciences. The overall goal was to help inspire creativity that will guide us towards new synergistic ideas complementing these existing disciplines that are of such importance to NASA. Workshop participants aspired to: (1) Exploit synergies across the biological sciences at NASA, (2) Foster research, enabling technology, and mission concepts that support commonalities in space biology, astrobiology, synthetic biology, planetary protection, and relevant human health, performance, and habitation concerns, (3) Envision the development of an “Arc of Biology in Space” to encompass this multi-faceted joint research community. The focused objective of the workshop series was to explore and demonstrate how the integration of astrobiology and space biology could be achieved, identify strengths and weaknesses in the current state of the art, and recognize where our greatest challenges lay. Specifically, we seek to: (1) Establish a scientific framework for an integrated life sciences effort, (2) Pioneer discovery by creating unique opportunities in the fundamental biological sciences, (3) Explore novel combinations of existing technologies across the relevant disciplines, (4) Invent new technologies and applications in space life sciences, and (5) Creatively increase access to spaceflight, emerging and novel technologies, Earth analogs, and simulated natural and spaceflight environments. The community aims for a broad arc of biological competence in the context of space and planetary science, spaceflight, and habitation. We will present dominant themes and innovative ideas that resulted from this interchange of relevant communities.

astrobiology↗

NASA NTRSDate not supplied

The Integration of Life Sciences in Space: Astrobiology and Space Biology Virtual Workshops Report

astrobiology↗

DOE OSTI2026

Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice

Searches for new phenomena in complex scientific data are predominantly model-dependent, optimized for specific hypotheses, and therefore limited in their coverage of the space of possible signals. Recently, new AI-based model-agnostic search strategies, many of which have been pioneered in high-energy physics, have been proposed which provide a complementary paradigm, prioritizing broad exploration over tailored analyses. These techniques offer an opportunity to enhance the overall discovery potential of modern experiments, especially in regimes where theoretical guidance is scarce. In this document, we review the conceptual framework behind the main classes of AI-based model-agnostic strategies. We discuss the potential pitfalls of these methods, and strategies for their validation and interpretation. We aim for this document to serve as a useful reference both for practitioners and for researchers interested in learning more about these model-agnostic search strategies.

Amram, Oz [Fermilab] (ORCID:0000000237653123)↗

NASA NTRS2015

Advances in Astromaterials Curation: Supporting Future Sample Return Missions

NASA's Astromaterials, curated at the Johnson Space Center in Houston, are the most extensive, best-documented, and leastcontaminated extraterrestrial samples that are provided to the worldwide research community. These samples include lunar samples from the Apollo missions, meteorites collected over nearly 40 years of expeditions to Antarctica (providing samples of dozens of asteroid bodies, the Moon, and Mars), Genesis solar wind samples, cosmic dust collected by NASA's high altitude airplanes, Comet Wild 2 and interstellar dust samples from the Stardust mission, and asteroid samples from JAXA's Hayabusa mission. A full account of NASA's curation efforts for these collections is provided by Allen, et al [1]. On average, we annually allocate about 1500 individual samples from NASA's astromaterials collections to hundreds of researchers from around the world, including graduate students and post-doctoral scientists; our allocation rate has roughly doubled over the past 10 years. The curation protocols developed for the lunar samples returned from the Apollo missions remain relevant and are adapted to new and future missions. Several lessons from the Apollo missions, including the need for early involvement of curation scientists in mission planning [1], have been applied to all subsequent sample return campaigns. From the 2013 National Academy of Sciences report [2]: "Curation is the critical interface between sample return missions and laboratory research. Proper curation has maintained the scientific integrity and utility of the Apollo, Antarctic meteorite, and cosmic dust collections for decades. Each of these collections continues to yield important new science. In the past decade, new state-of-the-art curatorial facilities for the Genesis and Stardust missions were key to the scientific breakthroughs provided by these missions." The results speak for themselves: research on NASA's astromaterials result in hundreds of papers annually, yield fundamental discoveries about the evolution of the solar system (e.g. [3] and references contained therein), and serve the global scientific community as ground truth for current and planned missions such as NASA's Dawn mission to Vesta and Ceres, and the future OSIRIS REx mission to asteroid Bennu [1,3]

Evans, C. A.↗

DOE OSTI2022

Laboratory Directed Research and Development Program Activities (LDRD 2021 Annual Report)

Each year, Brookhaven National Laboratory (BNL) is required to provide a report of its completed Laboratory Directed Research and Development Program (LDRD) projects to the Department of Energy (DOE) Office of Scientific and Technical Information in accordance with DOE Order 413.2C Chg1 (MinChg) dated August 2, 2018. This report provides a detailed look at the scientific and technical activities for each of the LDRD projects funded by BNL in FY 2021, in fulfillment of that requirement. In FY 2021, the BNL LDRD Program funded 68 projects, 24 of which were new starts, at a total cost of $17.1M. The investments that BNL makes in its LDRD program support the Laboratory’s strategic goals. BNL has identified six scientific initiatives that define the Laboratory’s scientific future and that will enable it to realize its overall vision. This requires simultaneous excellence in all aspects of BNL’s work – from science and operations, to external partnerships with the local, state, and national communities, and beyond. This is enabled by safe, efficient, and secure operations; by an unwavering commitment to a diverse, equitable, and inclusive environment, including workforce development, both with staff and reaching out to the community; and by a strong focus on renewed infrastructure. The six scientific initiatives are: (1) Nuclear Physics: The Electron Ion Collider, (2) Clean Energy and Climate, (3) Quantum Information Science and Technology, (4) Discovery Science Driven by the Human-AI-Facility Integration, (5) High Energy Physics: Building for Discovery, and (6) Accelerating Isotope Production: Ensuring the Nation’s Supply is Secure. The funded projects support BNL’s six scientific initiatives and priority programs as well as new areas of research and competencies at the Laboratory that are consistent with the Laboratory’s vision and mission. In total, these LDRD investments supported 80 postdoctoral researchers and graduate students in whole or in part and resulted in 136 publications and 3 awards. This Program Activities Report represents the future of BNL science; it is an impressive body of exploratory work that investigates many scientific and technical directions in support of the DOE and BNL missions.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2023

Enter Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics

Identifying small molecules (e.g., metabolites) is key towards driving scientific advancement in metabolomics, and gas chromatography–mass spectrometry (GC-MS) is an analytic method that may be applied to facilitate this process. The typical GC-MS identification workflow involves quantifying the similarity of an observed sample spectrum and other features (e.g. retention index) to that of several references, noting the compound of the best-matching reference spectrum as the identified metabolite. While a deluge of similarity metrics exists, none characterize the error rate of generated identifications, thereby presenting an unknown risk of false identification or discovery. To quantify this unknown risk, we propose a model-based framework for estimating the false discovery rate (FDR) among a set of identifications. Extending the traditional mixture modeling framework, our method incorporates both similarity score and experimental information in estimating the FDR. We apply these models to identification lists derived from across 548 samples of varying complexity and sample type (e.g., fungal species, standard mixtures, etc.), comparing their performance to that of the traditional Gaussian mixture model (GMM). Through simulation, we additionally assess the impact of reference library size on the accuracy of FDR estimates. In comparing the best performing model extensions to the GMM, our results indicate relative decreases in median absolute estimation error (MAE) ranging from 12% to 70%, based on comparisons of the median MAEs across all hit-lists. Results indicate that these relative performance improvements generally hold despite library size, however FDR estimation error typically worsens as the set of reference compounds diminishes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Data Sharing in Radiobiology; Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally „Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗