Compression of video data by adaptive nonlinear prediction
Computer simulation of adaptive nonlinear predictor for video data compression
COMPUTER SIMULATION↗
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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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Stability theory of multistep methods
Numerical solution of differential equations using predictor-corrector multistep methods for stability theory
STABILITY↗
Numerical integration of orbits in multirevolution steps
Predictor-corrector algorithm applicable to numerical integration of satellite orbits in multirevolution steps
SATELLITE ORBIT↗
Adaptive Stress Testing of Trajectory Predictions in Flight Management Systems
To find failure events and their likelihoods in flight-critical systems, we investigate the use of an advanced black-box stress testing approach called adaptive stress testing. We analyze a trajectory predictor from a developmental commercial flight management system which takes as input a collection of lateral waypoints and en-route environmental conditions. Our aim is to search for failure events relating to inconsistencies in the predicted lateral trajectories. The intention of this work is to find likely failures and report them back to the developers so they can address and potentially resolve shortcomings of the system before deployment. To improve search performance, this work extends the adaptive stress testing formulation to be applied more generally to sequential decision-making problems with episodic reward by collecting the state transitions during the search and evaluating at the end of the simulated rollout. We use a modified Monte Carlo tree search algorithm with progressive widening as our adversarial reinforcement learner. The performance is compared to direct Monte Carlo simulations and to the cross-entropy method as an alternative importance sampling baseline. The goal is to find potential problems otherwise not found by traditional requirements-based testing. Results indicate that our adaptive stress testing approach finds more failures and finds failures with higher likelihood relative to the baseline approaches.
adaptive stress testing↗
Isolating the anthropogenic component of Arctic warming
Structural equation modeling is used in statistical applications as both confirmatory and exploratory modeling to test models and to suggest the most plausible explanation for a relationship between the independent and the dependent variables. Although structural analysis cannot prove causation, it can suggest the most plausible set of factors that influence the observed variable. Here, we apply structural model analysis to the annual mean Arctic surface air temperature from 1900 to 2012 to find the most effective set of predictors and to isolate the anthropogenic component of the recent Arctic warming by subtracting the effects of natural forcing and variability from the observed temperature. We also find that anthropogenic greenhouse gases and aerosols radiative forcing and the Atlantic Multidecadal Oscillation internal mode dominate Arctic temperature variability. Finally, our structural model analysis of observational data suggests that about half of the recent Arctic warming of 0.64 K/decade may have anthropogenic causes.
54 ENVIRONMENTAL SCIENCES↗
Environmental drivers of methane fluxes from an urban temperate wetland park
Abstract Methane (CH 4 ) emissions were measured at the Wilma H. Schiermeier Olentangy River Wetland Research Park (ORWRP) over three summers and two winters using an eddy covariance system. We used an empirical model to determine the main environmental drivers of methane emissions. Methane emissions covary strongly with water vapor fluxes, CO 2 fluxes, and soil temperature. We adjust our models to account for the heterogeneous environment of the wetland by including the flux footprint distribution among different microsites as a predictive variable in the methane model. We used a forward linear stepwise model in combination with an Akaike information criteria‐based model selection process and neural network modeling to determine which environmental variables are most effective in modeling methane emissions in our site. Different models and environmental variables best represented methane fluxes in the winter and summer and also during the day or night within each season. We parameterized an optimal empirical model for methane emissions from the ORWRP that is used for gap filling of site‐level methane fluxes over 2 years. Some of the most effective variables for modeling methane were carbon, water vapor, and heat fluxes, all of which typically have the same data gaps as the time series of methane flux. In order to determine if these variables were useful for modeling methane despite the additional gap‐filling error, we determined through an error propagation experiment that eddy covariance gap‐filling models for methane may be best developed by including other gap‐filled fluxes as predictors, despite the high level of shared gaps and subsequent gap‐fill error propagation.
Morin, T. H.↗
Catchment influence on nitrate and dissolved organic matter in Alaskan streams across a latitudinal gradient
Spatial patterns in carbon (C) and nitrogen (N) cycles of high-latitude catchments have been linked to climate and permafrost and used to infer potential changes in biogeochemical cycles under climate warming. However, inconsistent spatial patterns across regions indicate that factors in addition to permafrost and regional climate may shape responses of C and N cycles to climate change. In this paper, we hypothesized that physical attributes of catchments modify responses of C and N cycles to climate and permafrost. We measured dissolved organic C (DOC) and nitrate (NO 3 ¯ ) concentrations, and composition of dissolved organic matter (DOM) in 21 streams spanning boreal to arctic Alaska, and assessed permafrost, topography, and attributes of soils and vegetation as predictors of stream chemistry. Multiple regression analyses indicated that catchment slope is a primary driver, with lower DOC and higher NO 3 ¯ concentration in streams draining steeper catchments, respectively. Depth of the active layer explained additional variation in concentration of DOC and NO 3 ¯ . Vegetation type explained regional variation in concentration and composition of DOM, which was characterized by optical methods. Composition of DOM was further correlated with attributes of soils, including moisture, temperature, and thickness of the organic layer. Finally, regional patterns of DOC and NO 3 ¯ concentrations in boreal to arctic Alaska were driven primarily by catchment topography and modified by permafrost, whereas composition of DOM was driven by attributes of soils and vegetation, suggesting that predicting changes to C and N cycling from permafrost-influenced regions should consider catchment setting in addition to dynamics of climate and permafrost.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models
The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. In order to provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraft were used as the predictors. Furthermore, we selected model explanatory parameters from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L≥4.8 and L ≥5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≤ L ≤ 6, while for the GEO flux prediction, the K P index is better than Dst. Finally, a test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.
58 GEOSCIENCES↗
Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes
Ecosystem models often perform poorly in reproducing interannual variability in carbon and water fluxes, resulting in considerable uncertainty when estimating the land-carbon sink. While many aggregated variables (growing season length, seasonal precipitation, or temperature) have been suggested as predictors for interannual variability in carbon fluxes, their explanatory power is limited and uncertainties remain as to their relative contributions. Recent results show that the annual count of hours where evapotranspiration (ET) is larger than its 95th percentile is strongly correlated with the annual variability of ET and gross primary production (GPP) in an ecosystem model. This suggests that the occurrence of favorable conditions has a strong influence on the annual carbon budget. Here we analyzed data from eight forest sites of the AmeriFlux network with at least 7 years of continuous measurements. We show that for ET and the carbon fluxes GPP, ecosystem respiration (RE), and net ecosystem production, counting the “most active hours/days” (i.e., hours/days when the flux exceeds a high percentile) correlates well with the respective annual sums, with correlation coefficients generally larger than 0.8. Phenological transitions have much weaker explanatory power. By exploiting the relationship between most active hours and interannual variability, we classify hours as most active or less active and largely explain interannual variability in ecosystem fluxes, particularly for GPP and RE. Our results suggest that a better understanding and modeling of the occurrence of large values in high-frequency ecosystem fluxes will result in a better understanding of interannual variability of these fluxes.
54 ENVIRONMENTAL SCIENCES↗
The Response of Atlantic Tropical Cyclones to Suppression of African Easterly Waves
Atlantic tropical cyclone (TC) genesis is strongly linked with African easterly waves (AEWs) on the synoptic time scale. However, the TC–AEW relationship is unclear on interannual to climate time scales, and it is unknown whether AEWs are necessary to maintain climatological TC frequency, that is, whether TCs are limited by AEWs. We investigated the impact of AEW suppression on seasonal Atlantic TC activity using a 10–member ensemble of regional climate model simulations in which AEWs were either prescribed or removed through the lateral boundary condition. The climate model experiments produced no significant change in seasonal Atlantic TC number, indicating that AEWs are not necessary to maintain climatological basin–wide TC frequency even though TCs readily originate from these types of disturbances. This suggests that the specific type of “seedling” disturbance is unimportant for determining basin–wide seasonal Atlantic TC number and that in the absence of AEWs, TCs will generate by other mechanisms. Furthermore, the results imply that changes in the presence of AEWs may not be reliable predictors of seasonal variability and future change in Atlantic TC frequency.
58 GEOSCIENCES↗
Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information
Reservoirs are fundamental human–built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast–informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month–ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Here, the results show three methods are capable of providing monthly reservoir inflows with satisfactory statistics; the results obtained by Random Forest have the best statistical performances compared with the other two methods; another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.
54 ENVIRONMENTAL SCIENCES↗
A Panoramic View of MXenes via an Atomic Coordination‐Based Design Strategy
Two‐dimensional (2D) transition metal carbides and nitrides, known as MXenes, possess unique physical and chemical properties, enabling diverse applications in fields ranging from energy storage to communication, catalysis, sensing, healthcare, and beyond. Despite extensive research and notable advancements, a fundamental understanding of MXenes’ phase diversity and its connection to their hierarchical precursors, including the intermediate MAX phases and the ancestral bulk phases, remains limited. Here, in this study, it is hypothesized that the atomic coordination environments adopted by transition metal and nonmetallic atoms in their three‐dimensional (3D) bulk precursors may persist in 2D MXenes to govern their phase diversity. Using high‐throughput modeling based on first‐principles density functional theory, a wide range of MXene phases is unveiled and comprehensively evaluate their relative stabilities across a large chemical space. The key to the approach lies in considering various atomic coordination environments drawn from four types of ancestral bulk phases. Through this comprehensive structural library of MXenes, general guiding principles are uncovered, such as a close alignment between the phase stability of MXenes and that of their 3D precursors. These findings introduce a new design strategy in which the atomic coordination environments in bulk phases can serve as reliable predictors for accessing the diverse structural landscape of MXenes.
MXenes↗
Towards the Ultimate Strength of Medium‐Entropy Alloys Through Pulsed Lasers
The tensile strength of metals at extreme strain rates is a key predictor of their performance in ballistic and structural impact applications. An important experimental method to reach these extreme strain rates is the use of high-amplitude, short-duration pulsed lasers. The Jupiter Laser Facility at the Lawrence Livermore National Laboratory enabled probing for the first time the mechanical response of several promising High Entropy Alloys at times on the order of nanoseconds (strain rates of ∼10 7 and ∼10 9 s −1 ). The measured strength is in the range of 6 to 10 GPa, ten times the quasistatic value. The mechanisms of plastic deformation and failure were identified and quantified through analysis and molecular dynamics simulation. The reflected wave amplitudes, obtained by VISAR, were used to determine the tensile (spall) stress. The high tensile strength obtained is due to two factors: the strain-rate dependence of plastic flow and the kinetics of void nucleation, growth, and coalescence. The experimental results are compared with an analytical prediction considering both grain-interior and grain-boundary void initiation. Molecular dynamics simulations, conducted at strain rates of 10 8 and 10 9 s −1 , rationalize the experimental results. In conclusion, they provide valuable information about the process of failure evolution, and reveal that grain boundary separation plays a pivotal role in spalling.
Materials science↗
Machine Learning Guided Synthesis of Flash Graphene
Advances in nanoscience have enabled the synthesis of nanomaterials, such as graphene, from low-value or waste materials through flash Joule heating. Though this capability is promising, the complex and entangled variables that govern nanocrystal formation in the Joule heating process remain poorly understood. In this work, machine learning (ML) models are constructed to explore the factors that drive the transformation of amorphous carbon into graphene nanocrystals during flash Joule heating. An XGBoost regression model of crystallinity achieves an r 2 score of 0.8051 ± 0.054. Feature importance assays and decision trees extracted from these models reveal key considerations in the selection of starting materials and the role of stochastic current fluctuations in flash Joule heating synthesis. Furthermore, partial dependence analyses demonstrate the importance of charge and current density as predictors of crystallinity, implying a progression from reaction-limited to diffusion-limited kinetics as flash Joule heating parameters change. Finally, a practical application of the ML models is shown by using Bayesian meta-learning algorithms to automatically improve bulk crystallinity over many Joule heating reactions. Furthermore, these results illustrate the power of ML as a tool to analyze complex nanomanufacturing processes and enable the synthesis of 2D crystals with desirable properties by flash Joule heating.
01 COAL, LIGNITE, AND PEAT↗
Materials Genomics Search for Possible Helium‐Absorbing Nano‐Phases in Fusion Structural Materials
Abstract Civilian fusion demands structural materials that can withstand the harsh environments imposed inside fusion plasma reactors. The structural materials often transmute under 14.1 MeV fast neutrons, producing helium (He), which embrittles the grain boundary (GB) network. Here, it is shown that neutron‐friendly and mechanically strong nano‐phases with atomic‐scale free volume can have low He‐embedding energy and >10 at.% He‐absorbing capacity, and can be especially advantageous for soaking up He on top of resisting radiation damage and creep, provided they have thermodynamic compatibility with the matrix phase, satisfactory equilibrium wetting angle, as well as a high enough melting point. The preliminary experimental demonstration proves that is a good ab initio predictor of He shielding potency in nano‐heterophase materials, and thus, is used as a key feature for computational screening. In this context, a list of viable compounds expected to be good He‐absorbing nano‐phases is presented, taking into account , the neutron absorption and activation cross‐sections, the elastic moduli, melting temperature, the thermodynamic compatibility, and the equilbrium wetting angle of the nano‐phases with the Fe matrix as an example.
36 MATERIALS SCIENCE↗
A case-control study of airways obstruction among construction workers: Airways Obstruction Among Construction Workers
Background: While smoking is the major cause of chronic obstructive pulmonary disease (COPD), occupational exposures to vapors, gases, dusts, and fumes (VGDF) increase COPD risk. This case-control study estimated the risk of COPD attributable to occupational exposures among construction workers. Methods: The study population included 834 cases and 1243 controls participating in a national medical screening program for older construction workers between 1997 and 2013. Qualitative exposure indices were developed based on lifetime work and exposure histories. Results: Approximately 18% (95%CI=2–24%) of COPD risk can be attributed to construction-related exposures, which are additive to the risk contributed by smoking. A measure of all VGDF exposures combined was a strong predictor of COPD risk. Conclusions: Construction workers are at increased risk of COPD as a result of broad and complex effects of many exposures acting independently or interactively. Control methods should be implemented to prevent worker exposures, and smoking cessation should be promoted
60 APPLIED LIFE SCIENCES↗
Global brain activity and its coupling with cerebrospinal fluid flow is related to tau pathology
Abstract INTRODUCTION Factors responsible for the deposition of pathological tau in the brain are incompletely understood. This study links macroscale tau deposition in the human brain to cerebrospinal fluid (CSF) flow dynamics using resting‐state functional magnetic resonance imaging (rsfMRI). METHODS Low‐frequency (< 0.1 Hz) resting‐state global brain activity is coupled with CSF flow and potentially reflects CSF dynamics‐related clearance. We examined the correlation between rsfMRI measures of CSF inflow and global activity (gBOLD–CSF coupling) as a predictor, interacting with amyloid beta (Aβ), of tau and cortical thickness (dependent variables) across Alzheimer's Disease Neuroimaging Initiative (ADNI) participants from cognitively unimpaired through mild cognitive impairment (MCI) and Alzheimer's disease (AD). RESULTS Tau deposition in Aβ+ participants, accompanied by cortical thinning and cognitive decline, is associated with decreased gBOLD–CSF coupling. Tau mediates the relationship between coupling and thickness. DISCUSSION Findings suggest that resting‐state global brain activity and CSF movements comodulate Alzheimer's tau deposition, presumably related to CSF clearance. Highlights A non‐invasive functional magnetic resonance imaging (fMRI) assessment of a CSF clearance‐related process is carried out. Global brain activity is coupled with CSF inflow in human fMRI during resting state. Global fMRI–CSF coupling is correlated with tau in Alzheimer's disease (AD). This coupling measure is also associated with cortical thickness, mediated by tau.
Neurosciences & Neurology↗