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Results for “CAUSALITY”
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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Quantizing Hořava-Lifshitz gravity via causal dynamical triangulations
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Assessing the Impact of Supplier Group Stability on Public Procurement Performance in Slovakia: A Causal Forest Analysis of Collusion Signals
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Discrete-Time Causal Control of a Wave Energy Converter With Finite Stroke in Stochastic Waves
We consider feedback control design for a wave energy converter (WEC) for which the power takeoff (PTO) system has a finite stroke limit. Stationary stochastic wave loading is assumed, with a known spectrum, and the plant dynamics are assumed to be linear. We develop a technique for the design of a discrete-time controller, which has three design stages. In the first stage, a linear controller is optimized while imposing a relaxation of the maximum stroke constraint on the design. In the second stage, a tandem nonlinear feedback loop is designed for the purpose of stroke protection. In the third stage, the two designs (linear and nonlinear) are fused in a manner that preserves the stability of the overall system. The technique is demonstrated in a simulation of a simple cylindrical buoy. We show that the controller may be tuned through the adjustment of scalar design parameters, which adjust the tradeoff between the mean generated power and the force levels required to protect the stroke.
Cross Modality Bias in Visual Question Answering: A Causal View With Possible Worlds VQA
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Apple Bitter Rot: Biology, Ecology, Omics, Virulence Factors, and Management of Causal Colletotrichum Species
ABSTRACT Apple bitter rot is caused by various Colletotrichum spp. that threaten apple production globally resulting in millions of dollars in damage annually. The fungus causes a decline in fruit quality and yield, eventually rotting the fruit and rendering it inedible. The pathogen is difficult to keep out of orchards because of its broad host range and transmissibility by rain splash and insects. Once the disease manifests, pathogen identification is difficult due to evolving taxonomy and similar morphology between species. Current management strategies are threatened by an increase in fungicide resistance and regulations on many multisite fungicides, leading to a pressing need for new management options for control. This review aims to summarise the most current knowledge regarding the biology, virulence factors, ecology, omics and emerging management strategies for Colletotrichum species that cause apple bitter rot. Taxonomy Colletotrichum species—Domain Eukaryota, Kingdom Fungi, Phylum Ascomycota, Class Sordariomycetes, Order Glomerellales, Family Glomerellaceae, Genus Colletotrichum . Biology Hemibiotrophic pathogen with a wide host range that establishes a biotrophic interaction where it penetrates host plants using appressoria followed by a switch to necrotrophy causing rot symptoms. Toxins Cercosporin, colletotrichins, colletotric acid, ferricrocin. Host Range The host range varies by species but largely occurs on dicotyledonous plants and is less prevalent on monocots as well as gymnosperms, ferns, mosses and animals (e.g., insects). Disease Symptoms Symptoms often manifest as flat to sunken necrotic areas on fruit. Lesions on leaves and fruit can have concentric rings with abundant pathogen sporulation. Disease Control Colletotrichum spp. are primarily managed by single‐site quinone outside inhibitor (Qol), methyl benzimidazole carbamate (MBC), demethylation inhibitor (DMI) fungicides, and multisite dithiocarbamate and phthalimide fungicides. Susceptibility may vary with species, strain specificity, or geographic region. Other management options include clean stock production, cultural practices, resistance breeding, and biological control through the introduction of protective or competing microorganisms.
Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data
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Digital Analytics, Causal Knowledge Acquisition and Reasoning for Technical Language Processing
Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.
Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers
Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. Here we demonstrate that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide data-analytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.
Detection of long-term trends in carbon accumulation by forests in Northeastern U.S. and determination of causal factors (Final Report)
The overall project goal was to quantify the trends and variability for Net ecosystem exchange of CO 2 , H 2 O, and energy by northeastern forests, with particular attention to the role of succession, differences in species composition, legacies of past land use, and disturbances. Measurements included flux measurements and observations of biomass accumulation using ecosystem modeling as a framework for data interpretation. Continuation of the long-term record at the Environmental Measurement Site (EMS) Tower was a priority. The final quality-assured CO 2 -flux data now extend through 2010. Data through 2011 are collected but not yet finalized. Biomass observations on the plot array centered on the tower are extended to 2011. Two additional towers in a hemlock stand (HEM) and a younger deciduous stand (LPH) complement the EMS tower by focusing on stands with different species composition or age distribution and disturbance history, but comparable climate and soil type. Over the period since 1993 the forest has added 24.4 Mg-C ha -1 in the living trees. Annual net carbon uptake had been increasing from about 2 Mg-C ha -1 y -1 in the early 1990s to nearly 6 Mg-C ha -1 y -1 by 2008, but declined in 2009-2010. We attribute the increasing carbon uptake to a combination of warmer temperatures, increased photosynthetic efficiency, and increased influence by subcanopy hemlocks that are active in the early spring and late autumn when temperatures are above freezing but the deciduous canopy is bare. Not all of the increased carbon accumulation was found in woody biomass. Results from a study using data to optimize parameters in an ecosystem process model indicate that significant changes in model parameters for photosynthetic capacity and shifts in allocation to slow cycling soil organic matter are necessary for the model to match the observed trends. The emerging working hypothesis is that the pattern of increasing carbon uptake over the early 2000's represents a transient pulse that will eventually end as decomposition of the accumulated carbon catches up.
Causal Analysis of the Unanticipated Extremity Exposure at HFEF
This report covers the unintended extremity exposure to an operator while handling a metallurgical mount sample of irradiated fuel following an off-scale high beta radiation reading of the sample. The decision was made to continue working after the meter indicated high off-scale by the HPT Supervisor, which resulted in the operator at the next operation being exposed.
Causal Analysis: LLNL Software Quality Assurance Program Does Not Meet DOE O 414.1D Standards and Procedures Requirements
Abstract not provided.
Causal Analysis of the FASB Uranium Silicide Compact Combustion Event
Abstract not provided.