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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Model Based Approach to Extract Health Information from Textual Data

In current nuclear power plants (NPPs) a large amount of condition-based data is being generated and stored to assess and monitor component health and performance. The format of this data can be either numeric (e.g., pump vibration data) or textual (e.g., condition report which assess component health). While assessing component health from numeric data can be performed with a large variety of methods, the extraction of information from textual data still remains a challenge. Natural language processing (NLP) methods are starting to be deployed in current NPPs mainly to filter out incident reports (IRs) that are not safety related by employing supervised machine learning methods. However, these methods do not really provide the quantitative information that might be contained in IRs. This paper presents an approach to extract information from textual data (e.g., from IRs, maintenance reports) that is based on NLP data analytics methods coupled with model-based system engineer (MBSE) models. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence; such analysis includes: part of speech (POS) tagging (i.e., identification of grammatic elements of each string - e.g., nouns, verbs), named entity recognition (i.e., identification of text entities - e.g., names, dates, events), and relation extraction (e.g., coreference resolution). On the other hand, semantic analysis is designed to analyze the logic structure of a sentence. Through a specific set of rules, our methods can identify whether a sentence contains health information of a component (e.g., degraded performance, anomaly behavior) or the causal relationship between two events (i.e., a cause-effect pair). An innovative element of our approach is that semantic analysis relies on MBSE models to identify links between textual elements. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. This paper presents in detail how the integration of NLP methods and MBSE models is performed. Few analysis examples focusing on centrifugal pumps are presented.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1991

IDEF3 formalization report

The Process Description Capture Method (IDEF3) is one of several Integrated Computer-Aided Manufacturing (ICAM) DEFinition methods developed by the Air Force to support systems engineering activities, and in particular, to support information systems development. These methods have evolved as a distillation of 'good practice' experience by information system developers and are designed to raise the performance level of the novice practitioner to one comparable with that of an expert. IDEF3 is meant to serve as a knowledge acquisition and requirements definition tool that structures the user's understanding of how a given process, event, or system works around process descriptions. A special purpose graphical language accompanying the method serves to highlight temporal precedence and causality relationships relative to the process or event being described.

Menzel, Christopher↗
DOE OSTI2026

Nonlinear causality of Israel-Stewart theory with diffusion

We present the first fully nonlinear causality constraints in D = 3 + 1 dimensions for Israel-Stewart theory in the presence of energy and number diffusion in the Eckart and Landau hydrodynamic frames, respectively. These constraints are algebraic inequalities that make no assumption on the underlying geometry of the spacetime or the equation of state. In order to highlight the distinct physical and structural behavior of the two hydrodynamic frames, we discuss the special ultrarelativistic ideal gas equation of state considered in earlier literature in D = 1 + 1 dimensions, and show that our general D = 3 + 1 constraints reduce to their results upon an appropriate choice of angles. For this equation of state in both D = 1 + 1 and D = 3 + 1 dimensions one can show that: (i) there exists a region allowed by nonlinear causality in which the baryon current transitions into a spacelike vector in the Landau frame, and (ii) an analogous argument shows that the solutions of the Eckart frame equations of motion never violate the dominant energy condition, assuming nonlinear causality holds. Furthermore, we then compare our results with those from linearized Israel-Stewart theory and show that the linear causality bounds fail to capture the new physical constraints on energy and number diffusion that are successfully obtained through our nonlinear causality approach.

Quark-gluon plasma↗
NASA NTRS1988

Graph-based real-time fault diagnostics

A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.

Padalkar, S.↗
DOE OSTI2021

Deep-freeze graph training for latent learning

Scientific and engineering advances are primarily driven by multi-tier conceptual constructs and conditional theoretical frameworks. The theories allow predictions of hypothetical system responses, given a set of approximate conditions (ranges of applicability) imposed on latent parameters that cannot be measured directly. Learning to estimate the latent variables (Latent Learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in interpretation, interpolation and extrapolation of limited experimental data. Due to high dimensionality and extreme non-linearity of the materials science problems, very large datasets are typically required for conventional data-driven model development. The vital experimental data collection, particularly on microstructural phases, is very challenging, which makes it difficult to compile a high-quality database. Incorporation of the domain knowledge into the computational graph structure, initialization and optimization processes presents a viable mechanism for developing accurate models, with limited datasets. Furthermore, this study successfully utilized the approach to build the Deep Freeze Graph (DeepFreG) by mapping known causality relationships and by digitizing empirical domain knowledge for Latent Learning (LL), with specific applications in materials science.

36 MATERIALS SCIENCE↗
NASA NTRS2014

Focal Gray Matter Plasticity as a Function of Long Duration Head Down Tilted Bed Rest: Preliminary Results

Long duration spaceflight (i.e., 22 days or longer) has been associated with changes in sensorimotor systems, resulting in difficulties that astronauts experience with posture control, locomotion, and manual control. The microgravity environment is an important causal factor for spaceflight induced sensorimotor changes. Whether these sensorimotor changes are solely related to peripheral changes from reduced vestibular stimulation, body unloading, body fluid shifts or that they may be related to structural and functional brain changes is yet unknown. However, a recent study reported associations between microgravity and flattening of the posterior eye globe and protrusion of the optic nerve [1] possibly as the result of increased intracranial pressure due to microgravity induced bodily fluid shifts [3]. Moreover, elevated intracranial pressure has been related to white matter microstructural damage [2]. Thus, it is possible that spaceflight may affect brain structure and thereby cognitive functioning. Long duration head down tilt bed rest has been suggested as an exclusionary analog to study microgravity effects on the sensorimotor system [4]. Bed rest mimics microgravity in body unloading and bodily fluid shifts. In consideration of the health and performance of crewmembers both in- and post-flight, we are conducting a prospective longitudinal 70-day bed rest study as an analog to investigate the effects of microgravity on brain structure [5]. Here we present results of the first six subjects. Six subjects were assessed at 12 and 7 days before-, at 7, 30, and ~70 days in-, and at 8 and 12 days post 70 days of bed rest at the NASA bed rest facility in UTMB, Galveston, TX, USA. At each time point structural MRI scans (i.e., high resolution T1-weighted imaging and Diffusion Tensor Imaging (DTI)) were obtained using a 3T Siemens scanner. Focal changes over time in gray matter density were assessed using the voxel based morphometry 8 (VBM8) toolbox under SPM. Longitudinal processing in VBM8 includes linear registration of each scan to the mean of the subject and subsequently transforming all scans in to MNI space by applying the warp from the mean subject to MNI to the individual gray matter segmentations. Modulation was applied so that all images represented the volume of the original structure in native space. Voxel wise analysis was carried out on the gray matter images after smoothing, using a flexible factorial design with family wise error correction. Focal changes in white matter microstructural integrity were assessed using tract based spatial statistics (TBSS) as part of FMRIB software library (FSL). TBSS registers all DTI scans to standard space. It subsequently creates a study specific white matter skeleton of the major white matter tracts. For each subject, for each DTI metric (i.e. fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD)), the maximum value in a line perpendicular to the skeleton tract is projected to the skeleton. Non-parametric permutation based t-tests and ANOVA's were used for voxel-wise comparison of the skeletons. For both VBM and TBSS, comparison of pre bed rest measurements did not show significant differences. VBM analysis revealed decreased gray matter density in bilateral areas including the frontal medial cortex, the insular cortex and the caudate (see Figure) from 'pre to in bed rest'. Over the same time period, there was an increase in gray matter density in the cerebellum, occipital-, and parietal cortex, including the precuneus (see Figure). The majority of these changes did not recover from 'during to post bed rest'. TBSS analysis did not reveal significant changes in white matter microstructural integrity after correction for multiple comparisons. Uncorrected analyses (p<.015) revealed an increase in RD in the cerebellum and brainstem from pre bed rest to the first week in bed rest that did not recover post bed rest. Extended bed rest, which is an analog for microgravity, can result in gray matter changes and potentially in microstructural white matter changes in areas that are important for neuro motor behavior and cognition. These changes did not recover at two weeks post bed rest. Whether the effects of bed rest wear off at longer times post bed rest, and if they are associated with behavior are important questions that warrant further research.

Koppelmans, V.↗
NASA NTRS2014

Advanced Modeling and Uncertainty Quantification for Flight Dynamics; Interim Results and Challenges

As part of the NASA Vehicle Systems Safety Technologies (VSST), Assuring Safe and Effective Aircraft Control Under Hazardous Conditions (Technical Challenge #3), an effort is underway within Boeing Research and Technology (BR&T) to address Advanced Modeling and Uncertainty Quantification for Flight Dynamics (VSST1-7). The scope of the effort is to develop and evaluate advanced multidisciplinary flight dynamics modeling techniques, including integrated uncertainties, to facilitate higher fidelity response characterization of current and future aircraft configurations approaching and during loss-of-control conditions. This approach is to incorporate multiple flight dynamics modeling methods for aerodynamics, structures, and propulsion, including experimental, computational, and analytical. Also to be included are techniques for data integration and uncertainty characterization and quantification. This research shall introduce new and updated multidisciplinary modeling and simulation technologies designed to improve the ability to characterize airplane response in off-nominal flight conditions. The research shall also introduce new techniques for uncertainty modeling that will provide a unified database model comprised of multiple sources, as well as an uncertainty bounds database for each data source such that a full vehicle uncertainty analysis is possible even when approaching or beyond Loss of Control boundaries. Methodologies developed as part of this research shall be instrumental in predicting and mitigating loss of control precursors and events directly linked to causal and contributing factors, such as stall, failures, damage, or icing. The tasks will include utilizing the BR&T Water Tunnel to collect static and dynamic data to be compared to the GTM extended WT database, characterizing flight dynamics in off-nominal conditions, developing tools for structural load estimation under dynamic conditions, devising methods for integrating various modeling elements into a real-time simulation capability, generating techniques for uncertainty modeling that draw data from multiple modeling sources, and providing a unified database model that includes nominal plus increments for each flight condition. This paper presents status of testing in the BR&T water tunnel and analysis of the resulting data and efforts to characterize these data using alternative modeling methods. Program challenges and issues are also presented.

Hyde, David C.↗
NASA NTRS1978

Photometric and spectral properties of some T Tauri stars

Photometric and spectroscopic data have been obtained for selected T Tauri members of the Taurus-Aurigae cloud and the Orion complex. A correlation between the intensity ratio of calcium and hydrogen emission lines and the infrared excess at 3.5 microns is found for these stars, which indicates a causal relationship between 'chromospheric activity' and emission processes in the circumstellar shells. It is argued that a comparison with properties of well-studied novae could lead to a better understanding of the physical structure of T Tauri stars.

Warner, J. W.↗
DOE OSTI2024

Strong gravity extruding peaks in speed of sound profiles of massive neutron stars

The speed of sound squared (SSS) s 2 in massive neutron stars (NSs) characterizes not only the stiffness of supradense neutron-rich matter within but also equivalently properties of the curved geometry due to the strong-field gravity and matter-geometry coupling. A peaked density or radius profile of s 2 has been predicted for massive NSs using various NS equation of state (EOS) models. However, the nature, cause, location and size of the peak in s 2 profiles are still very EOS model dependent. In this work, we investigate systematically s 2 profiles in massive NSs in a new approach that is independent of the nuclear EOS model and without any presumption about the NS structure and/or composition. In terms of the small quantities (reduced radius, the energy density and pressure scaled by their central values), we perform double-element perturbative expansions in solving perturbatively the scaled Tolman-Oppenheimer-Volkoff (TOV) equations and analyzing s 2 profiles from the Newtonian limit to the general relativistic (GR) case. The GR term in the TOV equations plays a twofold role: it compresses NS matter and modifies the pressure/energy density ratio from small values in Newtonian stars showing no s 2 peak to large ones for massive NSs possessing a peak in their s 2 profiles, and eventually takes away the peak in extremely compact/massive NSs approaching the causality limit. In particular, the peaked behavior in s 2 is expected to emerge near the center of massive NSs like PSR J⁢0740+6620, while a sharp phase transition is unlikely to occur there. Furthermore, these features revealed from our analyses are universal as they are intrinsic properties of the GR stellar structure equations independent of the still very uncertain EOS of supradense neutron-rich matter in NSs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Medium-Range Structural Order as the Driver of Activated Dynamics and Complexity Reduction in Glass-Forming Liquids

Here, we analyze in depth the Elastically Collective Nonlinear Langevin Equation theory of activated dynamics in metastable liquids to establish that the predicted inter-relationships between the alpha relaxation time, local cage and collective elastic barriers, dynamic localization length, and shear modulus are causally related within the theory to the medium range order (MRO) static correlation length. The latter grows exponentially with density for metastable hard sphere fluids and as a nonuniversal inverse power law with temperature for supercooled liquids under isobaric conditions. The physical origin of predicted connections between the alpha time and other metrics of cage order and the thermodynamic inverse dimensionless compressibility is fully established. It is discovered that although kinetic constraints from the real space first coordination shell are important for the alpha time, they are of secondary importance compared to the consequences of the more universal MRO correlations in both the modestly and deeply metastable regimes. This understanding sheds new light on the theoretical basis for, and prior successes of, the predictive mapping of chemically complex thermal liquids to effective hard sphere fluids based on matching their dimensionless compressibilities, a scheme we call “complexity reduction”. In essence, the latter is equivalent to the physical requirement that the thermal liquid MRO correlation equals that of its effective hard sphere analog. The mapping alone is shown to provide a remarkable level of quantitative predictive power for the glass transition temperature T g of 21 molecular and polymer liquids. Predictions for the chemically specific absolute magnitude and growth with cooling of the MRO correlation length are obtained and lie in the window of 2–6 nm at T g . Dynamic heterogeneity, elastic facilitation, and beyond pair structure issues are briefly discussed. Future opportunities to theoretically analyze the equilibrated deep glass regime are outlined.

cancer↗
NASA NTRS1994

The Buffer Diagnostic Prototype: A fault isolation application using CLIPS

This paper describes problem domain characteristics and development experiences from using CLIPS 6.0 in a proof-of-concept troubleshooting application called the Buffer Diagnostic Prototype. The problem domain is a large digital communications subsystems called the real-time network (RTN), which was designed to upgrade the launch processing system used for shuttle support at KSC. The RTN enables up to 255 computers to share 50,000 data points with millisecond response times. The RTN's extensive built-in test capability but lack of any automatic fault isolation capability presents a unique opportunity for a diagnostic expert system application. The Buffer Diagnostic Prototype addresses RTN diagnosis with a multiple strategy approach. A novel technique called 'faulty causality' employs inexact qualitative models to process test results. Experimental knowledge provides a capability to recognize symptom-fault associations. The implementation utilizes rule-based and procedural programming techniques, including a goal-directed control structure and simple text-based generic user interface that may be reusable for other rapid prototyping applications. Although limited in scope, this project demonstrates a diagnostic approach that may be adapted to troubleshoot a broad range of equipment.

Porter, Ken↗
NASA NTRS2018

Results from ORACLES-2016: Observed Biomass Burning Aerosol Properties over the Southeast Atlantic Ocean and the Relationship to Meteorology

The Southeast Atlantic Ocean (SEA), with seasonal biomass burning (BB) smoke plumes overlying persistent stratocumulus cloud deck, offers an excellent natural laboratory to make the observations necessary to understand the complexities of aerosol-cloud-radiation interactions. The first field deployment of the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign was conducted in September of 2016 out of Walvis Bay, Namibia. During this deployment, two NASA aircraft (a P-3 and an ER-2) were flown with a suite of aerosol, cloud, radiation, and meteorological instruments for remote-sensing and in-situ observations. In this talk, I will first present results from an intercomparison of the different instrument retrievals for the 2016 flights, with a specific focus on the measures of aerosol absorption. In ORACLES, data collected by eight different instrument teams are used to derive aerosol properties over this region by independent methods. We focus on the single scattering albedo (SSA), a measure of the relative absorption versus total extinction by aerosols, as the main comparable property between all methods. I will also discuss comparisons of the retrieved aerosol absorption parameters, as well as the agreement and the variability seen between different instruments, and spatially over the study region. Another important factor in understanding aerosol radiative effects is how the magnitude of these effects may be modified by meteorological conditions. I will next describe observations collected from the P-3 aircraft (from near-surface up to 6-7 kilometers) which show a strong correlation between the in-situ pollution indicators (carbon monoxide and aerosol properties) and atmospheric water vapor content, seen at all altitudes above the boundary layer. This condition is seen to persist over all flights, with minimal detrainment during advection from the continental source. In exploring the potential causal factors behind and implications of this relationship, we see indications that convective dynamics over the continent likely contribute to this elevated signal, but neither meteorological reanalysis nor trajectory analysis fully capture the magnitude and vertical structure of the observed elevated signal. Finally, I will discuss the radiative implications of the observed correlations, a topic of ongoing analysis in ORACLES: understanding the mechanisms which cause water vapor to covary with plume strength is important to quantifying the radiative effects (direct and semi-direct) of biomass burning aerosol in the region.

Pistone, Kristina↗
DOE OSTI2023

New perspectives on QFT and gravity from quantum entanglement

The work accomplished by the PI as part of this award included studying the fundamental as- pects of quantum field theory (QFT) and the nature of space-time and gravity via the patterns of quantum entanglement present in these theories. By following these patterns the PI attacked basic questions on the nature of quantum gravity and found new constraints on the dynamical content of QFT. These topics find a natural home within the holographic dualiy, a deep mathemat- ical correspondence discovered in string theory where a gravitational system can be described by a quantum system without gravity. By studying the spatial distribution of quantum correlation, or entanglement, in various quantum systems the PI managed to directly observe the holographic emergence of quantum gravity within this mathematical framework. The PI also characterized the space-time structure that emerges via the duality. In achieving these aims the project shed light on the thermodynamic nature of gravity and had considerable impact on understanding of the uni- fication of gravity with quantum mechanics. Additionally, this work shed light on the structure of quantum entanglement in QFT and in so doing used powerful constraints satisfied by entanglement and it’s generalizations to place bounds on the basic data of the QFT. The project also related these bounds to causality constraints and quantum energy conditions in field theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1985

A possible mechanism to cause the quasi-biennial variability on the solar neutrino flux

It is suggested that the quasi-biennial change in the observed flux of the solar neutrinos is causally related to some non-linear process at the central core of the Sun, being associated with the charge in the central temperature. This process seems to be responsible for the physical adjustment of the internal structure of the Sun. Numerical simulation on this process is able to reproduce the quasi-biennial change in the flux of these neutrinos.

Sakurai, K.↗
DOE OSTI2024

Enhancing Power Grid Resilience with Causal Loops Diagram and Bayesian Networks

Enhancing power grid resilience through improved analysis and planning of Distributed Energy Resources is a key for power system planner. This paper explores the integration of Causal Loop Diagrams (CLDs) and Bayesian Networks (BNs) for enhancing resilience in power systems, focusing on Distributed Energy Resources (DER) planning. By automating CLD analysis in Python's matplotlib, we present a tool for rapid model validation and structural accuracy, crucial for power system planners. This hybrid approach utilizes BNs for inferential depth and CLDs for dynamic system modeling, offering a comprehensive framework for policy formulation and collaborative strategy development against disruptions. Here, we highlight the tool's capability to identify and analyze interconnected feedback loops, facilitating a deeper understanding of DER integration's impact on network resilience. This work aims to bridge quantitative analysis and qualitative insights, addressing the limitations of each method while providing a robust model for power system resilience assessment.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Machine Learning for the Validation of Expert-Elicited Causal Risk Diagrams

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.

directed acyclic graphs↗
DOE OSTI2021

MHD simulations of cold bubble formation from 2/1 tearing mode during massive gas injection in a tokamak

Massive gas injection (MGI) experiments have been carried out in many tokamaks to study disruption dynamics and mitigation schemes. Two events often observed in those experiments are the excitation of the m = 2, n = 1 magnetohydrodynamic mode, and the formation of cold bubble structure in the temperature distribution before the thermal quench (TQ). Here m is the poloidal mode number, n the toroidal mode number. The physics mechanisms underlying those phenomena, however, have not been entirely clear. In this work, our recent NIMROD simulations of the MGI process in a tokamak have reproduced the main features of both events, which has allowed us to examine and establish the causal relation between them. In these simulations, the 3/1 and 2/1 islands are found to form successively after the arrival of impurity ion cold front at the corresponding q = 3 and q = 2 rational surfaces. At the interface between impurity and plasma, a local thin current sheet forms due to an enhanced local pressure gradient and moves inward following the gas cold front, this may contribute to the formation of a dominant 2/1 mode. Following the growth of the 2/1 tearing mode, the impurity penetration into the core region inside the q = 2 surface gives rise to the formation of the cold bubble temperature structure and initiates the final TQ. Here, a subdominant 1/1 mode developed earlier near the q = 1 surface alone does not cause such a cold bubble formation, however, the exact manner of the preceding impurity penetration depends on the nature of the 1/1 mode: kink-tearing or quasi-interchange.

2/1 tearing mode↗
NASA NTRS2005

Internal Dynamics and Crustal Evolution of Mars

The objective of this work is to improve understanding of the internal structure, crustal evolution, and thermal history of Mars by combining geophysical data analysis of topography, gravity and magnetics with results from analytical and computational modeling. Accomplishments thus far in this investigation include: (1) development of a new crustal thickness model that incorporates constraints from Mars meteorites, corrections for polar cap masses and other surface loads, Pratt isostasy, and core flattening; (2) determination of a refined estimate of crustal thickness of Mars from geoid/topography ratios (GTRs); (3) derivation of a preliminary estimate of the k(sub 2) gravitational Love number and a preliminary estimate of possible dissipation within Mars consistent with this value; and (4) an integrative analysis of the sequence of evolution of early Mars. During the remainder of this investigation we will: (1) extend models of degree-1 mantle convection from 2-D to 3-D; (2) investigate potential causal relationships and effects of major impacts on mantle plume formation, with primary application to Mars; (3) develop exploratory models to assess the convective stability of various Martian core states as relevant to the history of dynamo action; and (4) develop models of long-wavelength relaxation of crustal thickness anomalies to potentially explain the degree-1 structure of the Martian crust.

Zuber, Maria↗