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Interpretation of Dynamic Strain Aging in an Intercritical Annealed Steel by Dislocation Multiplicat
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What Does It Take to Produce Interpretation? Informational, Peircean, and Code-Semiotic Views on Biosemiotics
This paper presents a critical analysis of code-semiotics, which we see as the latest attempt to create paradigmatic foundation for solving the question of the emergence of life and consciousness. We view code semiotics as a an attempt to revise the empirical scientific Darwinian paradigm, and to go beyond the complex systems, emergence, self-organization, and informational paradigms, and also the selfish gene theory of Dawkins and the Peircean pragmaticist semiotic theory built on the simultaneous types of evolution. As such it is a new and bold attempt to use semiotics to solve the problems created by the evolutionary paradigm’s commitment to produce a theory of how to connect the two sides of the Cartesian dualistic view of physical reality and consciousness in a consistent way.
Assessing arrhythmic risk with {sup 123}I-mIBG and analogous tracers: Image interpretation from a different viewpoint
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Systolic wall thickening and prone acquisition as tools to reduce false positives in the interpretation of the SPECT myocardial perfusion study
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A Semi-Empirical Framework for Interpreting Traveling Wave Ion Mobility Arrival Time Distributions
The inherent structural heterogeneity of biomolecules is an important biophysical property that is essential to their function, but is challenging to characterize experimentally. We present a workflow that rapidly and quantitatively assesses the conformational heterogeneity of peptides and proteins in the gas phase using traveling wave ion mobility (TWIM) arrival time distributions (ATDs). We have established a set of semi-empirical equations that model the TWIM ATD peak width and resolution across a wide range of wave amplitudes (V) and wave velocities (v). In addition, a conformational broadening parameter, δ, can be extracted from this analysis that reports on the contribution of conformational heterogeneity to the broadening of TWIM ATD peak width during ion mobility separation. We use this δ value to evaluate the conformational heterogeneity of a set of helical peptides, and our analysis correlates well with previous peak width observations reported for these ions. Furthermore, we use molecular dynamics simulations to independently investigate the general flexibility of these peptides in the gas phase, and generate similar trends found in experimental TWIM data. Finally, we extended our analysis to Avidin, a 64-kDa homotetramer, and quantify the structural heterogeneity of this intact complex using TWIM ATD data as a function of cross-linking. We observe an initial reduction in δ values as a function of cross-linker concentration, demonstrating the sensitivity of our δ value analysis to changes in flexibility of the assembly. .
Activation product interpretation of structural material for fast critical assemblies
This work evaluates whether activation products in stainless steel components can be used to infer initial conditions (i.e. alloy composition, position within the fluence, etc.) and the associated uncertainties in a fast neutron irradiation. Activation measurements of stainless steel and elemental components were obtained through a Flattop critical assembly irradiation experiment. A forward model was developed, utilizing MCNP6.1 and FISPACT ii, to estimate activation product concentrations from several input parameters: alloy composition, source specifications, and foil positioning. While some of the calculated activation product concentrations agreed fairly well with measurements, some isotopics (i.e. 57 Co) had large discrepancies. It became clear that uncertainties in both measurements and nuclear data drove this divergence for select isotopics, and the quantification of this impact was needed. By treating both experimental measurement and nuclear data uncertainty in a Bayesian framework, it was possible to infer unknowns from given activation ratios. The developed approach showed promise in constraining mass parameters, but degeneracy existed when considering position within the neutron flux. Higher threshold reactions could potentially break the degeneracy, such as 57 Co, but uncertainty estimation needs to be better resolved for those isotopes.
New results from a number operator interpretation of the compositeness of bound and resonant states
A novel theoretical approach to the problem of the compositeness (X) of a resonance or bound state is developed on the basis of the expectation values of the number operators of the free particles in the continuum. This formalism is specially suitable for effective field theories in which the bare elementary states are integrated out but that give rise to resonance and bound states when implemented in nonperturbative calculations. We demonstrate that X=1 for finite-range energy-independent potentials, either regular or singular. A non-trivial example for an energy-dependent potential is discussed where it is shown that X is independent of any type of cutoff regulator employed. The generalization of these techniques to relativistic states is developed. We also explain how to obtain a meaningful compositeness with respect to the open channels for resonances, even if it is complex in a first turn, by making use of suitable phase-factor transformations. Defining elementariness as X=0, we derive a new universal criterion for the elementariness of a bound state. Along the same lines, a necessary condition for a resonance to be qualified as elementary is given. The application of the formalism here developed might be of considerable practical interest.
Quantification of mineral reactivity using machine learning interpretation of micro-XRF data
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Interpreting Temperature Strain Data from Meso-Scale Clathrate Experiments
Gas hydrates are important in global climate change, carbon sequestra- tion, and seafloor stability. Currently, formation and dissociation pathways are poorly defined. We present a new approach for processing large amounts of data from meso-scale experiments, such as the LUNA distributed sensing system (DSS) in the seafloor process simulator (SPS) at Oak Ridge National Laboratory. The DSS provides a proxy for temperature measurement with a high spatial resolution allowing the heat of reaction during gas hydrate formation/dissociation to aid in locating clathrates in the vessel. The DSS fibers are placed in the sediment following an Archimedean spiral design and then the position of each sensor is solved by iterating over the arc length formula with Newtons method. The data is then gridded with 1 a natural neighbor interpolation algorithm to allow contouring of the data. The solution of the sensor locations is verified with hot and cold stimulus in known locations. An experiment was preformed with a vertically split column of sand and silt. The DSS system clearly showed hydrate forming in the sand first, then slowly creeping into the silt. Similar systems and data processing techniques could be used for monitoring of hydrates in natural environments or in any situation where a hybrid temperature/strain index is useful. Further ad- vances in fiber technology allow the fiber to be applied in any configuration and the position of each sensor to be precisely determined making practical applications easier.
A computer vision algorithm for interpreting lacustrine carbonate textures at Searles Valley, USA
Investigations of the paleohydrologies of pluvial lake systems have often employed lake carbonate deposits called “tufa” that grow subaqueously and can be preserved long after the drying of the lake. For this reason, tufa have been used as a proxy for minimum lake level. However, they exhibit a variety of textures that hold the potential to reveal richer paleoclimatological information. With the goal of determining if tufa texture can be used as a proxy for lake environment, this study investigates the textures of tufa at Mono Lake, California in comparison to the fossil tufa in Searles Valley, California. While observations in the last century suggest that the tufa in the Mono basin grew in waters similar to the modern, the tufa at Searles formed during the last glacial period, when the Great Basin contained a system of pluvial lakes on the scale of the modern Great Lakes. The tufa at both basins have been observed to have a range of classifiable textures, and new methods of inspecting visual data could be informative about what factors control these textures. To this end, a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is used to project images of the tufa at Searles and Mono into a coordinate space, allowing for simple, quantitative comparisons of the visual similarity of textures. In this work, the textures of tufa at Searles are compared to each other, as well as to the tufa at Mono. This study performs a robust assessment of the feasibility of Mono Lake as a modern analogue for Searles Valley. It finds that there is a justifiable basis for the comparison of certain fossil facies at Searles to the tufa at Mono, significant progress towards the goal of using texture as a metric for the environment in which tufa formed.
Interpreting mixing relationships in energetic melts to estimate vapor contribution and composition
Heterogeneous, glassy debris can be produced by energetic events such as near surface nuclear explosions and asteroid impacts. In these events, the resultant debris comprises a rapidly-cooled mixture of the projectile or bomb material with proximate materials such as soil and anthropogenic structures. Glasses formed in these events preserve inter and/or intra sample compositional variation reflecting complex mixtures of the precursor materials or their chemically fractionated products. Understanding formation mechanisms and chemical processes relating to energetic glass formation based on study of the end products requires an objective method for calculating the precursor components that mixed together to produce the final product. Here, we compare a set of multivariate linear-least squares approaches to address the unmixing problem of heterogeneous glasses including classical least squares (CLS), principal component analysis (PCA) and two multivariate curve resolution – alternating least squares (MCR-ALS) approaches. We generate a synthetic data set representing spatially resolved, compositional data in a set of heterogeneously mixed glasses to understand model sensitivity to precursor abundance and composition. We explore the impact of incomplete knowledge of the system prior to the formation of the melts on the resulting confidence and accuracy of the solution (i.e., the number, composition, and abundances of the precursor materials). We conclude that a closure and equality constrained MCR-ALS approach succeeds in identifying unrecognized precursor components and is a suitable alternative to CLS approaches when the investigator has incomplete knowledge of the starting precursor chemical compositions. Finally, we demonstrate application of this MCR-ALS approach to measurements of Trinitite.
$\text{GPLaSDI}$: Gaussian Process-based interpretable Latent Space Dynamics Identification through deep autoencoder
Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the latent space dynamics. By interpolating and solving the ODE system in the reduced latent space, fast and accurate ROM predictions can be made by feeding the predicted latent space dynamics into the decoder. In this paper, we introduce GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the ROM predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. Here we demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error.