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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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Physical optics accounts for near field, cross polarization, and higher-order modes. Report presents physical-optics analysis of four-reflector, 64-m antennas of Deep Space Network. Analysis thorough and detailed. Report has instructional value as example for designers of large microwave dishes with subreflectors and involving reflector surfaces with hyperboloidal, paraboloidal, ellipsoidal, and more complex shapes.
Air transportation system designers have had only limited success using traditional operations research and parametric modeling approaches in their analyses of innovations. They need a systemic methodology for modeling of safety-critical infrastructure that is comprehensive, objective, and sufficiently concrete, yet simple enough to be used with reasonable investment. The methodology must also be amenable to quantitative analysis so issues of system safety and stability can be rigorously addressed. However, air transportation has proven itself an extensive, complex system whose behavior is difficult to describe, no less predict. There is a wide range of system analysis techniques available, but some are more appropriate for certain applications than others. Specifically in the area of complex system analysis, the literature suggests that both agent-based models and network analysis techniques may be useful. This paper discusses the theoretical basis for each approach in these applications, and explores their historic and potential further use for air transportation analysis.
A future of increased wildfires requires greater integration of spatial analysis and local knowledge of emergency responders. We examine the application of a Potential Operational Delineations (PODs) framework for strategic pre-fire planning in Marin County. PODs are spatial units for wildfire management that combine predictive modeling and local firefighter expertise to identify potential control locations as unit boundaries and assess the difficulty of suppression within units. Additionally, this project explores the integration of road networks and social vulnerability to assess environmental justice in evacuation safety. This project constitutes a novel application of the PODs framework as it integrates expertise from Marin County senior firefighters with a Fireline Location Model (FLM) to achieve POD definition and uses a Suppression Difficulty Score (SDS) to rank each POD. The FLM uses network analysis and hydrologic modeling to identify key roads and ridgelines as boundaries and combines them with expert knowledge, in the form of workshops, to construct PODs. Once identified, PODs are classified using SDS, which includes processed inputs such as LiDAR-derived aboveground biomass, ECOSTRESS Evaporative Stress Index, land use cover type from Sentinel-2 Imagery, and a digital elevation model. Environmental justice for evacuation safety incorporated three key road metrics such as connectivity, travel area, and exit capacity, the Social Vulnerability Index from the Center for Disease Control, and cell coverage to determine a final Evacuation Difficulty Score. Results indicate a strong link between road networks as primary POD boundaries, with ridgelines and waterways as secondary and tertiary locations. Specifically, we find 78.5% of expertise-identified POD boundaries align with FLM-determined boundaries. More validation is needed to support this process; however, initial results signal a feasible framework to integrate expertise and spatial analysis in local level strategic fire planning
Developing climate‐resilient crops requires a detailed understanding of stress‐induced gene expression dynamics, as maladaptive responses can compromise their productivity and survival. Sorghum, a globally important cereal with exceptional tolerance to multiple abiotic stresses, provides a powerful system for investigating these dynamics. However, how stress type, tissue specificity, and temporal progression jointly shape transcriptomic responses in crops remains poorly understood. Here, we present a comparative, time‐resolved transcriptomic atlas of sorghum responses to drought, heat, and salinity stress across shoot and root tissues. Integrative analyses revealed that tissue specificity is the dominant determinant of abiotic stress‐induced gene reprogramming across all three stresses. Building on these global comparisons, we focused on heat stress, as it elicited the most coherent and pronounced transcriptional and regulatory responses, enabling deeper network‐level interrogation. Co‐expression network analysis identified tissue‐specific modules enriched for phytohormone‐responsive genes, while gene regulatory network (GRN) mapping and cistrome analyses uncovered transcription factors (TFs) controlling key hub genes within these modules. Together, this study provides a foundational transcriptomic and network‐based resource for dissecting the regulatory architecture of abiotic stress responses in sorghum and offers prioritized candidates for future functional validation and engineering of climate‐resilient crops.
Exposure to spaceflight poses risk to human health in complex ways. To help manage this risk, the Human Systems Risk Board (HSRB) at the National Aeronautics and Space Administration (NASA) maintains a set of causal diagrams that attempt to explain how spaceflight hazards generate health risks and lead to adverse outcomes both in-mission, immediately post-mission, and over the long term. These causal risk diagrams are formulated as directed acyclic graphs (DAGs) and can function as knowledge graphs of connected risks and outcomes. These DAGs have proven useful for communication, and, through network analysis, have allowed for the identification of structurally important factors in the risk network. However, the utility these DAGs provide is directly proportional to their verisimilitude, making assessment of this trait using empirical data – whether from actual human spaceflight or various spaceflight analogue exposures and model organisms – a high priority. In this research we explore the use of machine learning algorithms to learn DAG structure from empirical data as a means of evaluating human-elicited DAG structures. To do so, we test several different graph structure-learning algorithms on data concerning changes in the bones of rats and mice after exposure to either spaceflight or a spaceflight analogue. We explore potential methods for indexing the similarity between each algorithm’s output DAG with all the others and with that of the expert-elicited DAG. We discuss next steps in this ongoing line of research and open science initiatives underway to complete them.
The data links established between the U.S. and Europe during the September 1985 International Cometary Explorer/Comet Giacobini-Zinner encounter are summarized. The Space Physics Analysis Network (SPAN), which is a link between U.S. universities, research institutes and NASA centers, was responsible for the rapid dissemination and analysis of the data obtained from the encounter. The network was then linked across the Atlantic to support investigators involved in a European experiment on board the spacecraft. It is concluded that SPAN provided ESA personnel with a unique opportunity to experience near-real-time data acquisition. The data transfer was performed successfully, and the experience gained proved useful in assessing ESA's needs for future participation in scientific international networking.
The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks and the lack of efficient verification procedures. In this paper, we propose to leverage emerging multimodal, vision-language, foundation models (VLMs) as a lens through which we can reason about vision models. VLMs have been trained on a large body of images accompanied by their textual description, and are thus implicitly aware of high-level, human-understandable concepts describing the images. We describe a logical specification language Con spec designed to facilitate writing specifications in terms of these concepts. To define and formally check Con spec specifications, we build a map between the internal representations of a given vision model and a VLM, leading to an efficient verification procedure of natural-language properties for vision models. We demonstrate our techniques on a ResNet-based classifier trained on the RIVAL-10 dataset using CLIP as the multimodal model.
Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.
The Network Control Center (NCC) currently uses the NCC Data System (NCCDS) to schedule customer spacecraft communication requests for the Space Network (SN). The NCC/Request Oriented Scheduling Engine (NCC/ROSE), which implements an operational concept called flexible scheduling, is being tested as a potential replacement for the NCCDS scheduler in an effort to increase the efficiency of the NCC scheduling operations. This paper describes the high fidelity benchmark tests being conducted on NCC/ROSE, the evaluation techniques used to compare schedules, and the results of the tests. This testing will verify the increases in efficiency and productivity that can help the NCC meet the anticipated scheduling loads well into the next century.
At the NASA Glenn Research Center, NASA Langley Research Center's Flight Optimization System (FLOPS) and the design optimization testbed COMETBOARDS with regression and neural-network-analysis approximators have been coupled to obtain a preliminary aircraft design methodology. For a subsonic aircraft, the optimal design, that is the airframe-engine combination, is obtained by the simulation. The aircraft is powered by two high-bypass-ratio engines with a nominal thrust of about 35,000 lbf. It is to carry 150 passengers at a cruise speed of Mach 0.8 over a range of 3000 n mi and to operate on a 6000-ft runway. The aircraft design utilized a neural network and a regression-approximations-based analysis tool, along with a multioptimizer cascade algorithm that uses sequential linear programming, sequential quadratic programming, the method of feasible directions, and then sequential quadratic programming again. Optimal aircraft weight versus the number of design iterations is shown. The central processing unit (CPU) time to solution is given. It is shown that the regression-method-based analyzer exhibited a smoother convergence pattern than the FLOPS code. The optimum weight obtained by the approximation technique and the FLOPS code differed by 1.3 percent. Prediction by the approximation technique exhibited no error for the aircraft wing area and turbine entry temperature, whereas it was within 2 percent for most other parameters. Cascade strategy was required by FLOPS as well as the approximators. The regression method had a tendency to hug the data points, whereas the neural network exhibited a propensity to follow a mean path. The performance of the neural network and regression methods was considered adequate. It was at about the same level for small, standard, and large models with redundancy ratios (defined as the number of input-output pairs to the number of unknown coefficients) of 14, 28, and 57, respectively. In an SGI octane workstation (Silicon Graphics, Inc., Mountainview, CA), the regression training required a fraction of a CPU second, whereas neural network training was between 1 and 9 min, as given. For a single analysis cycle, the 3-sec CPU time required by the FLOPS code was reduced to milliseconds by the approximators. For design calculations, the time with the FLOPS code was 34 min. It was reduced to 2 sec with the regression method and to 4 min by the neural network technique. The performance of the regression and neural network methods was found to be satisfactory for the analysis and design optimization of the subsonic aircraft.
Understanding flow traffic patterns in networks, such as the Internet or service provider networks, is crucial to improving their design and building them robustly. However, as networks grow and become more complex, it is increasingly cumbersome and challenging to study how the many flow patterns, sizes and the continually changing source-destination pairs in the network evolve with time. Here, we present Netostat, a visualization-based network analysis tool that uses visual representation and a mathematics framework to study and capture flow patterns, using graph theoretical methods such as clustering, similarity and difference measures. Netostat generates an interactive graph of all traffic patterns in the network, to isolate key elements that can provide insights for traffic engineering. We present results for U.S. and European research networks, ESnet and GEANT, demonstrating network state changes, to identify major flow trends, potential points of failure, and bottlenecks.
The application of NASTRAN to a complex piping design evaluation problem is summarized. Emphasis is placed on structural modeling aspects, problems encountered in modeling and analyzing curved pipe sections, principal results, and relative merits of using NASTRAN as a pipe analysis and design tool. In addition, the piping and manifolding system was analyzed with SNAP (Structural Network Analysis Program). The parallel SNAP study provides a basis for limited comparisons between NASTRAN and SNAP as to solution agreement and computer execution time and costs.
Comparative network analysis provides effective computational means for gaining novel insights into the structural and functional compositions of biological networks. In recent years, various methods have been developed for biological network alignment, whose main goal is to identify important similarities and critical differences between networks in terms of their topology and composition. A major impediment to advancing network alignment techniques has been the lack of gold-standard benchmarks that can be used for accurate and comprehensive performance assessment of such algorithms. The original NAPAbench (network alignment performance assessment benchmark) was developed to address this problem, and it has been widely utilized by many researchers for the development, evaluation, and comparison of novel network alignment techniques. In this work, we introduce NAPAbench 2—a major update of the original NAPAbench that was introduced in 2012. NAPAbench 2 includes a completely redesigned network synthesis algorithm that can generate protein-protein interaction (PPI) network families whose characteristics closely match those of the latest real PPI networks. Furthermore, the network synthesis algorithm comes with an intuitive GUI that allows users to easily generate PPI network families with an arbitrary number of networks of any size, according to a flexible user-defined phylogeny. In addition, NAPAbench 2 provides updated benchmark datasets—created using the redesigned network synthesis algorithm—which can be used for comprehensive performance assessment of network alignment algorithms and their scalability.
Our understanding of Alzheimer’s disease (AD) pathophysiology remains incomplete. Here, for this work, we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.
Label Propagation is not only a well-known machine learning algorithm for classification but also an effective method for discovering communities and connected components in networks. We propose a new Direction-optimizing Label Propagation Algorithm (DOLPA) framework that enhances the performance of the standard Label Propagation Algorithm (LPA), increases its scalability, and extends its versatility and application scope. As a central feature, the DOLPA framework relies on the use of frontiers and alternates between label push and label pull operations to attain high performance. It is formulated in such a way that the same basic algorithm can be used for finding communities or connected components in graphs by only changing the objective function used. Additionally, DOLPA has parameters for tuning the processing order of vertices in a graph to reduce the number of edges visited and improve the quality of solution obtained. We present the design and implementation of the enhanced algorithm as well as our shared-memory parallelization of it using OpenMP. We also present an extensive experimental evaluation of our implementations using the LFR benchmark and real-world networks drawn from various domains. Compared with an implementation of LPA for community detection available in a widely used network analysis software, we achieve at most five times the F-Score while maintaining similar runtime for graphs with overlapping communities. We also compare DOLPA against an implementation of the Louvain method for community detection using the same LFR-graphs and show that DOLPA achieves about three times the F-Score at just 10% of the runtime. For connected component decomposition, our algorithm achieves orders of magnitude speedups over the basic LP-based algorithm on large-diameter graphs, up to 13.2× speedup over the Shiloach-Vishkin algorithm, and up to 1.6× speedup over Afforest on an Intel Xeon processor using 40 threads.
During early HIV-1 infection, immunodominant T cell responses to highly variable epitopes lead to the establishment of immune escape virus variants. Here we assessed a type 1-polarized monocyte-derived dendritic cell (MDC1)-based approach to selectively elicit cytotoxic T lymphocyte (CTL) responses against highly conserved and topologically important HIV-1 epitopes in HIV-1-infected individuals from the Thailand RV254/SEARCH 010 cohort who initiated antiretroviral therapy (ART) during early infection (Fiebig stages I-IV). Autologous MDC1 were used as antigen presenting cells to induce in vitro CTL responses against HIV-1 Gag, Pol, Env, and Nef as determined by flow cytometry and ELISpot assay. Ultra-conserved or topologically important antigens were respectively identified using the Epigraph tool and a structure-based network analysis approach and compared to overlapping peptides spanning the Gag proteome. MDC1 presenting either the overlapping Gag, Epigraph, or Network 14-21mer peptide pools consistently activated and expanded HIV-1-specific T cells to epitopes identified at the 9-13mer peptide level. Interestingly, some CTL responses occurred outside known or expected HLA associations, providing evidence of new HLA-associated CTL epitopes. Comparative analyses demonstrated more sequence conservation among Epigraph antigens but a higher magnitude of CTL responses to Network and Gag peptide groups. Importantly, CTL responses against topologically constrained Gag epitopes contained in both the Network and Gag peptide pools were selectively enhanced in the Network pool-initiated cultures. Our study supports the use of MDC1 as a therapeutic strategy to induce and focus CTL responses toward putative fitness-constrained regions of HIV-1 to prevent immune escape and control HIV-1 infection.
Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.
Generation of slag (aluminum oxide) is an important issue for the Reusable Solid Rocket Motor (RSRM). Thiokol performed testing to quantify the relationship between raw material variations and slag generation in solid propellants by testing sub-scale motors cast with propellant containing various combinations of aluminum fuel and ammonium perchlorate (AP) oxidizer particle sizes. The test data were analyzed using statistical methods and an artificial neural network. This paper primarily addresses the neural network results with some comparisons to the statistical results. The neural network showed that the particle sizes of both the aluminum and unground AP have a measurable effect on slag generation. The neural network analysis showed that aluminum particle size is the dominant driver in slag generation, about 40% more influential than AP. The network predictions of the amount of slag produced during firing of sub-scale motors were 16% better than the predictions of a statistically derived empirical equation. Another neural network successfully characterized the slag generated during full-scale motor tests. The success is attributable to the ability of neural networks to characterize multiple complex factors including interactions that affect slag generation.