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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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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

A Modeling Study of Hydrodynamic Circulation in a Fjord of the Pacific Northwest

Increased eutrophication and degraded water quality in estuarine and coastal waters have been a worldwide environmental concern. While it is commonly accepted that anthropogenic impact plays a major role in many emerging water quality issues, natural conditions such as restricted water circulations controlled by geometry may also substantially contribute to unfavorable water quality in certain ecosystems. To elucidate the contributions from different factors, a hydrodynamic-water quality model that integrates both physical transport and pollutant loadings is particularly warranted. A preliminary modeling study using the Environmental Fluid Dynamic Code (EFDC) is conducted to investigate hydrodynamic circulation and low dissolved oxygen (DO) in Hood Canal, a representative fjord in the U.S. Pacific Northwest. Because the water quality modeling work is still ongoing, this paper focuses on the progress in hydrodynamic modeling component. The hydrodynamic model has been set up using the publicly available forcing data and was calibrated against field observations or NOAA predictions for tidal elevation, current, salinity and temperature. The calibrated model was also used to estimate physical transport timescales such as residence time in the estuary. The preliminary model results demonstrate that the EFDC Hood Canal model is capable of capturing the general circulation patterns in Hood Canal, including weak tidal current and strong vertical stratification. The long residence time (i.e., on the order of 100 days for the entire estuary) also indicates that restricted water circulation could contribute to low DO in the estuary and also makes the system especially susceptible to anthropogenic disturbance, such as excess nutrient input.

circulation↗
DOE OSTI2025

A Machine Learning Approach for Hourly Traffic Prediction Used in EV-Charging Sites

Reliable forecasting of hourly traffic volumes on highways is critical for planning and operating electric-vehicle charging infrastructure without overloading the grid. In this work, we develop and evaluate a station-specific machine-learning approach based on NeuralProphet, enhanced with conditional seasonality to better distinguish weekday, weekend, and holiday patterns. For each station, the model automatically retrieves the same calendar day from the prior years as an AR-Net initialization, fits trend and Fourier-based seasonality components, and then applies short-term auto-regressive corrections. We train and test on 2021 and 2022 TMAS data, respectively, and validate performance over the whole year. We chose to demonstrate how the model performs on a typical weekday (3/15/2022), weekend (3/27/2022), and a special holiday (12/25/2022). Our results yield MAPE of 7.4%, 23.6%, and 32.0%, respectively. Over the entire year 2022, the overall MAPE was 17%. This demonstrates that station-specific models with conditional seasonality can achieve accurate, scalable hourly forecasts for EV-charging load planning.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS2012

Large-Scale Covariability Between Aerosol and Precipitation Over the 7-SEAS Region: Observations and Simulations

One of the seven scientific areas of interests of the 7-SEAS field campaign is to evaluate the impact of aerosol on cloud and precipitation (http://7-seas.gsfc.nasa.gov). However, large-scale covariability between aerosol, cloud and precipitation is complicated not only by ambient environment and a variety of aerosol effects, but also by effects from rain washout and climate factors. This study characterizes large-scale aerosol-cloud-precipitation covariability through synergy of long-term multi ]sensor satellite observations with model simulations over the 7-SEAS region [10S-30N, 95E-130E]. Results show that climate factors such as ENSO significantly modulate aerosol and precipitation over the region simultaneously. After removal of climate factor effects, aerosol and precipitation are significantly anti-correlated over the southern part of the region, where high aerosols loading is associated with overall reduced total precipitation with intensified rain rates and decreased rain frequency, decreased tropospheric latent heating, suppressed cloud top height and increased outgoing longwave radiation, enhanced clear-sky shortwave TOA flux but reduced all-sky shortwave TOA flux in deep convective regimes; but such covariability becomes less notable over the northern counterpart of the region where low ]level stratus are found. Using CO as a proxy of biomass burning aerosols to minimize the washout effect, large-scale covariability between CO and precipitation was also investigated and similar large-scale covariability observed. Model simulations with NCAR CAM5 were found to show similar effects to observations in the spatio-temporal patterns. Results from both observations and simulations are valuable for improving our understanding of this region's meteorological system and the roles of aerosol within it. Key words: aerosol; precipitation; large-scale covariability; aerosol effects; washout; climate factors; 7- SEAS; CO; CAM5

Huang, Jingfeng↗
DOE OSTI2019

Anisotropic breakage mechanics: From stored energy to yielding in transversely isotropic granular rocks

The microstructure of geological solids is affected by their deposition history, which generates complex systems of grain contacts and damage patterns able to induce mechanical anisotropy. Here this paper aims to disclose the connection between energy storage and the yield surface of cross-anisotropic granular rocks, with the goal to simplify the representation of their mechanical anisotropy. For this purpose, a reformulation of Continuum Breakage Mechanics (CBM) is proposed to introduce material symmetries associated with energy storage processes. It is shown that, due to the thermodynamic consistency of the selected approach, the energy release resulting from comminution is influenced by the anisotropic characteristics of the elastic energy potential. As a result, the model is able to capture naturally and without additional fitting parameters the dependence of the yielding envelope on the relative orientation between bedding planes and loading direction. The performance of the new CBM model has been tested by performing parametric analyses which elucidate the role of cross-anisotropic elastic properties on the yield surface of a granular rock. Furthermore, its accuracy has been assessed against laboratory results available for two sandstones exhibiting dependence of the yield stress on the orientation of the bedding planes. Despite the simplicity of the selected model, the results emphasize that the proposed approach captures the salient features of the deformation response of anisotropic granular rocks, thereby disclosing an intimate connection between grain-scale energy release and cataclastic yielding which greatly simplifies the mathematical description of intrinsic inelastic anisotropy.

58 GEOSCIENCES↗
DOE OSTI2014

Synthesis and characterization of pharmaceutical surfactant templated mesoporous silica: Its application to controlled delivery of duloxetine

Graphical abstract: - Highlights: • Usefulness of dual pharmaceutical surfactants in silica synthesis was evaluated. • Effects of concentration of secondary template (Tween-40) were studied. • Effects of fixed solvothermal condition on mesostructure formation were studied. • Duloxetine drug loading capability was studied. • Sustained release of duloxetine was evaluated. - Abstract: A new group of mesoporous silica nanoparticles (MSNs) were synthesized using combination pharmaceutical surfactants, Triton X-100 and Tween-40 as template and loaded with duloxetine hydrochloride (DX), for improving the sustained release of DX and patterns with high drug loading. Agglomerated spherical silica MSNs were synthesized by sol–gel and solvothermal methods. The calcined and drug loaded MSNs were characterized using X-ray diffraction (XRD), Braunner–Emmett–Teller (BET), thermogravimetric analysis (TGA), Fourier-transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), diffuse reflectance ultraviolet–visible (DRS-UV–vis) spectroscopy. MSNs with high surface area and pore volume were selected and studied for their DX loading and release. The selected MSNs can accommodate a maximum of 34% DX within it. About 90% was released at 200 h and hence, the synthesized MSNs were capable of engulfing DX and sustain its release. Further form the Ritger and Peppas, Higuchi model for mechanism drug release from all the MSN matrices follows anomalous transport or Non-Fickian diffusion with the ‘r’ and ‘n’ value 0.9 and 0.45 < n < 1, respectively. So, from this study it could be concluded that the MSNs synthesized using pharmaceutical templates were better choice of reservoir for the controlled delivery of drug which requires sustained release.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Design, Detection, and Countermeasure of Frequency Spectrum Attack and Its Impact on Long Short-Term Memory Load Forecasting and Microgrid Energy Management

This paper introduces a frequency-domain false data injection attack called Frequency Spectrum Attack (FSA) and explores its effects on load forecasting and the energy management system (EMS) in a microgrid. The FSA analyzes time-series signals in the frequency domain to identify patterns in their frequency spectrum. It learns the distribution of dominant frequencies in a dataset of healthy signals. Subsequently, it manipulates the amplitudes of dominant frequencies within this healthy distribution, ensuring a stealthy attack against statistical analysis of the signal spectrum. We evaluated the performance of FSA on LSTM, a state-of-the-art network for load forecasting. The results show that FSA can triple the Mean Absolute Error (MAE) of predictions compared to the normal case and increase it by 70% compared to noise injection attacks. Furthermore, FSA indirectly enhances battery utilization in the EMS by 45%. We then proposed a detection method that combines statistical analysis and machine-learning-based classification techniques with features. The model effectively distinguishes FSA from healthy and noisy signals, achieving an accuracy of 98.7% and an F1-score of 98.1% on a load dataset, covering healthy, FSA, and noisy load data. Finally, a countermeasure was introduced based on the statistical analysis of the frequency spectrum of healthy signals to mitigate the impact of FSA. This countermeasure successfully reduces the MAE of the attacked model from 0.135 to 0.053, validating its effectiveness in mitigating FSA.

Nazeri, Amirhossein↗
NASA NTRS2001

Effects of different activity and inactivity paradigms on myosin heavy chain gene expression in striated muscle

The goal of this mini-review is to summarize findings concerning the role that different models of muscular activity and inactivity play in altering gene expression of the myosin heavy chain (MHC) family of motor proteins in mammalian cardiac and skeletal muscle. This was done in the context of examining parallel findings concerning the role that thyroid hormone (T(3), 3,5,3'-triiodothyronine) plays in MHC expression. Findings show that both cardiac and skeletal muscles of experimental animals are initially undifferentiated at birth and then undergo a marked level of growth and differentiation in attaining the adult MHC phenotype in a T(3)/activity level-dependent fashion. Cardiac MHC expression in small mammals is highly sensitive to thyroid deficiency, diabetes, energy deprivation, and hypertension; each of these interventions induces upregulation of the beta-MHC isoform, which functions to economize circulatory function in the face of altered energy demand. In skeletal muscle, hyperthyroidism, as well as interventions that unload or reduce the weight-bearing activity of the muscle, causes slow to fast MHC conversions. Fast to slow conversions, however, are seen under hypothyroidism or when the muscles either become chronically overloaded or subjected to intermittent loading as occurs during resistance training and endurance exercise. The regulation of MHC gene expression by T(3) or mechanical stimuli appears to be strongly regulated by transcriptional events, based on recent findings on transgenic models and animals transfected with promoter-reporter constructs. However, the mechanisms by which T(3) and mechanical stimuli exert their control on transcriptional processes appear to be different. Additional findings show that individual skeletal muscle fibers have the genetic machinery to express simultaneously all of the adult MHCs, e.g., slow type I and fast IIa, IIx, and IIb, in unique combinations under certain experimental conditions. This degree of heterogeneity among the individual fibers would ensure a large functional diversity in performing complex movement patterns. Future studies must now focus on 1) the signaling pathways and the underlying mechanisms governing the transcriptional/translational machinery that control this marked degree of plasticity and 2) the morphological organization and functional implications of the muscle fiber's capacity to express such a diversity of motor proteins.

NASA Discipline Musculoskeletal↗
DOE OSTI2020

DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models

In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. One common pattern emerging in such applications is frequent checkpointing of the state of the learning model during training, needed in a variety of scenarios: analysis of intermediate states to explain features and correlations with training data, exploration strategies involving alternative models that share a common ancestor, knowledge transfer, resilience, etc. However, with increasing size of the learning models and popularity of distributed data-parallel training approaches, simple checkpointing techniques used so far face several limitations: low serialization performance, blocking I/O, stragglers due to the fact that only a single process is involved in checkpointing. This paper proposes a checkpointing technique specifically designed to address the aforementioned limitations, introducing efficient asynchronous techniques to hide the overhead of serialization and I/O, and distribute the load over all participating processes. Experiments with two deep learning applications (CANDLE and ResNet) on a pre-Exascale HPC platform (Theta) shows significant improvement over state-of-art, both in terms of checkpointing duration and runtime overhead.

checkpointing↗
NASA NTRSDate not supplied

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
NASA NTRSDate not supplied

Capturing Multivariate Time Series Interactions to Detect High‑Risk Instability During Approach

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
DOE OSTI2026

High Pressure X-ray Diffraction and Equation of State of Hydrazine

Synchrotron X-ray diffraction has been used to investigate the structure and equation of state (EOS) of hydrazine (N 2 H 4 ) up to 54.3 GPa at 298 K. The diffraction patterns could be fit to a monoclinic unit-cell structure and put strong constraints on previously reported phase transitions documented by vibrational spectroscopy over this pressure range. Pressure–volume ( P–V ) data were fit using a Vinet EOS, yielding parameters: V 0 = 45.2 Å 3 /molecule (fixed), K 0 = 11.8(7) GPa, and K 0 ′ = 6.5(2). Previously measured high-pressure vibrational frequency shifts were used to estimate the vibrational free energy and model P–V–T isotherms from 0 to 1200 K. The results of the P–V–T isotherms are compared to existing shock Hugoniot data on hydrazine and 298 K isotherms for assemblages of possible decomposition products. This comparison suggests dissociation at high density under shock loading. Good correspondence was found between the static lattice EOS as calculated by the model and the previously reported EOS as calculated by density functional theory. Finally, these results resolve existing uncertainties about the EOS and crystal symmetry of hydrazine at high pressure and provide valuable baseline information on this important energetic material.

diffraction↗
DOE OSTI2016

Evaluation of Graph Pattern Matching Workloads in Graph Analysis Systems

Graph analysis has emerged as a powerful method for data scientists to represent, integrate, query, and explore heterogeneous data sources. As a result, graph data management and mining became a popular area of research, and led to the development of plethora of systems in recent years. Unfortunately, the number of emerging graph analysis systems and the wide range of applications, coupled with a lack of apples-to-apples comparisons, make it difficult to understand the trade-offs between different systems and the graph operations for which they are designed. A fair comparison of these systems is a challenging task for the following reasons: multiple data models, non-standardized serialization formats, various query interfaces to users, and diverse environments they operate in. To address these key challenges, in this paper we present a new benchmark suite by extending the Lehigh University Benchmark (LUBM) to cover the most common capabilities of various graph analysis systems. We provide the design process of the benchmark, which generalizes the workflow for data scientists to conduct the desired graph analysis on different graph analysis systems. Equipped with this extended benchmark suite, we present performance comparison for nine subgraph pattern retrieval operations over six graph analysis systems, namely NetworkX, Neo4j, Jena, Titan, GraphX, and uRiKA. Through the proposed benchmark suite, this study reveals both quantitative and qualitative findings in (1) implications in loading data into each system; (2) challenges in describing graph patterns for each query interface; and (3) different sensitivity of each system to query selectivity. We envision that this study will pave the road for: (i) data scientists to select the suitable graph analysis systems, and (ii) data management system designers to advance graph analysis systems.

Hong, Seokyong↗
NASA NTRS1983

Visual scanning behavior and pilot workload

Sophisticated man machine interaction often requires the human operator to perform a stereotyped scan of various instruments in order to monitor and/or control a system. For situations in which this type of stereotyped behavior exists, such as certain phases of instrument flight, scan pattern was shown to be altered by the imposition of simultaneous verbal tasks. A study designed to examine the relationship between pilot visual scan of instruments and mental workload is described. It was found that a verbal loading task of varying difficulty causes pilots to stare at the primary instrument as the difficulty increases and to shed looks at instruments of less importance. The verbal loading task also affected the rank ordering of scanning sequences. By examining the behavior of pilots with widely varying skill levels, it was suggested that these effects occur most strongly at lower skill levels and are less apparent at high skill levels. A graphical interpretation of the hypothetical relationship between skill, workload, and performance is introduced and modelling results are presented to support this interpretation.

Tole, J. R.↗
DOE OSTI2025

Data for: Climatic Imprint on Interfacially-Controlled Platinum-Palladium Resources

Data package for manuscript "Climatic Imprint on Interfacially-Controlled Platinum-Palladium Resources" by Emily G. Wright, Ivey Wang, Yihang Fang, Elaine D. Flynn, and Jeffrey G. Catalano. This dataset contains adsorption results from experiments designed to investigate the effect of chloride on Pd(II) adsorption to goethite and Pt(II) adsorption to hematite and goethite, including lab experiments, X-ray absorption fine structure spectroscopy, and models of retention within a laterite. See the associated manuscript for full methods information. The file "Wright2025_PtAds_data.csv" contains the target starting Pt concentration (uM), final aqueous Pt and associated error (in uM), calculated adsorbed Pt and associated error (in umol/m2), target and measured aqueous chloride (mM), target aqueous nitrate (mM), final pH, and mineral concentration/loading (g/L). Associated mineral-free controls (mineral loading = 0 g/L) are included; the aqueous Pd error was not calculated and chloride was not measured in every sample. These data appear in Figures 1, S3, S4, S5, S20, and S22 in the associated manuscript. The file "Wright2025_PdAds_data.csv" contains the target starting Pd concentration (uM), final aqueous Pd and associated error (in uM), calculated adsorbed Pd and associated error (in umol/m2), target and measured aqueous chloride (mM), and mineral concentration/loading (g/L). Associated mineral-free controls (mineral loading = 0 g/L) are included; the aqueous Pd error was not calculated and chloride was not measured in every sample. These data appear in Figures 1, S3, S4, S5, and S20 in the associated manuscript. The file "Wright2025_MineralBatches_data.csv" contains the mineral identity and BET specific surface area (m2/g) for every mineral batch synthesized and used in experiments. The annealing time used is listed for hydrothermally annealed goethite. These data appear in Table S2 in the associated manuscript. The file "Wright2025_XRD_data.csv" contains the XRD patterns for every mineral batch synthesized as the counts as a function of two theta (in degrees). See "Wright2025_MineralBatches_data.csv" for more details on specific mineral batches. These data appear in Figure S2 in the associated manuscript. The file "Wright 2025_ZetaPotential_data.csv" contains the measured zeta potentials for samples of goethite (batch G2) at pH 4 the presence of varying amounts of sodium chloride. These data appear in Table S3 in the associated manuscript. The file "Wright2025_XAFSSamples_data.csv" contains the specific mineral batch, measured final aqueous Pd or Pt (uM), measured final aqueous chloride (mM), and estimated adsorbed Pd or Pt (umol/m2) of all XAFS samples. These data appear in Tables S4, S7, S8, and S10 in the associated manuscript. The files "Wright2025_PdXAFS_data.csv" and "Wright2025_PtXAFS_data.csv" contain the normalized spectra of Pd and Pt, respectively, adsorbed to minerals at varying chloride concentrations. See "Wright2025_XAFSSamples_data.csv" for a guide to sample names. Note that "05" in a sample name is equivalent to "0.5". These data appear in Figures 2, S6, S7, S8, S12, S13, and S14 in the associated manuscript. The file "Wright2025_LateriteProfileProfileModelParameters_data.csv" include the ratio of hematite to hematite and goethite in two synthetic, modeled profiles, as well as the modeled surface areas of goethite and hematite as a function of relative depth within the modeled weathering zone. These data were used, in conjunction with equations presented in the paper, to calculate the theoretical concentrations of Pd and Pt (and the resulting Pt/Pd ratio) within the profiles. These data appear in Figure 3 in the associated manuscript. The file "Wright2025_Imagery_data.zip" is a zipped folder containing the TEM and STEM images appear in Figures S18 and S19. Individual files are labeled as either STEM (Fig. S18) or TEM (Fig. S19) with a letter representing the part of the multipart figure.

58 GEOSCIENCES↗
DOE OSTI2026

Consumer safety-oriented scheduling of rotating power outages during heat waves

Extreme heat events have widespread effects on power systems, reducing available generation capacity, limiting transmission capabilities, and causing unusual demand patterns on the consumer side. As these combined effects expose bulk transmission systems to potential large-scale blackouts, utilities may be required to schedule and apply rotating outages, by temporarily and alternately disconnecting distribution substations to reduce overload. However, utilities lack mechanisms to inform these events, exacerbating the negative effects of heat waves on affected communities. This paper introduces a novel framework for scheduling rotating outages during heat waves while considering impacts on consumers’ safety. Instead of random sequential load shedding, we propose a methodology to rotate power outages considering a metric that quantifies the indoor overheating risk of groups of consumers during a power outage. The overheating risk is derived from a detailed building simulation using CityBES, where the buildings are modeled based on available data—use type, year built, floor area, number of stories, location—while presence of air conditioning and occupancy are calibrated from smart meter data. Based on the metric, an algorithm to schedule the rotating outages is applied to prioritize feeders for disconnection at each hour according to their overheating risk to meet a utility load reduction target. Applied to two substations and seven feeders in the Portland General Electric territory, the results show that this approach effectively leads to the lowest overheating risk during the resulting outage schedules, with an average 10.1% lower overheating compared to uninformed schedules.

Building thermal simulation↗
NASA NTRS1993

Temporal and spatial mapping of surface albedo and atmospheric dust opacity on Mars

The Mariner 9 and Viking provided abundant evidence that eolian processes are active over much of the surface of Mars. Past studies have demonstrated that variations in regional albedo and wind-streak patterns are indicative of sediment transport through a region, while thermal inertia data (derived from the Viking Infrared Thermal Mapper (IRTM) dataset) are indicative of the degree of surface mantling by dust deposits. The visual and thermal data are therefore diagnostic of whether net erosion or deposition of dust-storm fallout is taking place currently and whether such processes have been active in a region over the long term. These previous investigations, however, have not attempted to correct for the effects of atmospheric dust loading on observations of the martian surface, so quantitative studies of current sediment transport rates have included large errors due to uncertainty in the magnitude of this 'atmospheric component' of the observations. We have developed a radiative transfer model that allows the atmospheric dust opacity to be determined from IRTM thermal observations. Corrections for the effects of atmospheric dust loading on observations of surface albedo can also be modeled. This approach to determining 'dust-corrected surface albedo' incorporates the atmospheric dust opacity, the single-scattering albedo and particle phase function of atmospheric dust, and the bidirectional reflectance of the surface, and it accounts for variable lighting and viewing geometry.

Lee, S. W.↗
DOE OSTI2013

Birds Shed RNA-Viruses According to the Pareto Principle

A major challenge in disease ecology is to understand the role of individual variation of infection load on disease transmission dynamics and how this influences the evolution of resistance or tolerance mechanisms. Such information will improve our capacity to understand, predict, and mitigate pathogen-associated disease in all organisms. In many hostpathogen systems, particularly macroparasites and sexually transmitted diseases, it has been found that approximately 20% of the population is responsible for approximately 80% of the transmission events. Although host contact rates can account for some of this pattern, pathogen transmission dynamics also depend upon host infectiousness, an area that has received relatively little attention. Therefore, we conducted a meta-analysis of pathogen shedding rates of 24 host (avian) – pathogen (RNA-virus) studies, including 17 bird species and five important zoonotic viruses. We determined that viral count data followed the Weibull distribution, the mean Gini coefficient (an index of inequality) was 0.687 (0.036 SEM), and that 22.0% (0.90 SEM) of the birds shed 80% of the virus across all studies, suggesting an adherence of viral shedding counts to the Pareto Principle. The relative position of a bird in a distribution of viral counts was affected by factors extrinsic to the host, such as exposure to corticosterone and to a lesser extent reduced food availability, but not to intrinsic host factors including age, sex, and migratory status. These data provide a quantitative view of heterogeneous virus shedding in birds that may be used to better parameterize epidemiological models and understand transmission dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2009

2D and 3D Numerical Simulations of Flux Cancellation

Cancellation of magnetic flux in the solar photosphere and chromosphere has been linked observationally and theoretically to a broad range of solar activity, from filament channel formation to CME initiation. Because this phenomenon is typically measured at only a single layer in the atmosphere, in the radial (line of sight) component of the magnetic field, the actual processes behind this observational signature are ambiguous. It is clear that reconnection is involved in some way, but the location of the reconnection sites and associated connectivity changes remain uncertain in most cases. We are using numerical modeling to demystify flux cancellation, beginning with the simplest possible configuration: a subphotospheric Lundquist flux tube surrounded by a potential field, immersed in a gravitationally stratified atmosphere, spanning many orders of magnitude in plasma beta. In this system, cancellation is driven slowly by a 2-cell circulation pattern imposed in the convection zone, such that the tops of the cells are located around the beta=1 level (i.e., the photosphere) and the flows converge and form a downdraft at the polarity inversion line; note however that no flow is imposed along the neutral line. We will present the results of 2D and 3D MHD-AMR simulations of flux cancellation, in which the flux at the photosphere begins in either an unsheared or sheared state. In all cases, a low-lying flux rope is formed by reconnection at the polarity inversion line within a few thousand seconds. The flux rope remains stable and does not rise, however, in contrast to models which do not include the presence of significant mass loading.

Karpen, Judith T.↗