Shape and Rotation Modeling and Thermophysical Analysis of Near-Earth Asteroid (1917) Cuyo

We are conducting an ESO Large Program that includes optical photometry, thermal-IR observations, and optical-NIR spectroscopy of selected NEAs. Among the principal goals of the program are shape and spin-state modeling, and searching for YORP-induced changes in rotation periods. One of our targets is asteroid (1917) Cuyo, a near-Earth asteroid from the Amor group. We carried out an extensive observing campaign on Cuyo between April 2010 and April 2013, operating primarily at the ESO 3.6m NTT for optical photometry, and the 8.2m VLT at Paranal for thermal-IR imaging. Further optical observations were acquired at the ESO 2.2m telescope, the Palomar 200" Hale telescope (California), JPL’s Table Mountain Observatory (California) and the Faulkes Telescope South (Australia). We obtained optical imaging data for rotational lightcurves throughout this period, as the asteroid passed through a wide range of observational geometries, conducive to producing a good shape model and spin state solution. The preliminary shape and spin state model indicates a nearly spherical shape and a rotation pole at ecliptic longitude λ = 53° ± 20° and latitude β = -37° ± 10° (1-sigma error bars are approximate). The sidereal rotation period was measured to be 2.6899522 ± (3 × 10^-7) hours. Linkage with earlier lightcurve data shows possible evidence of a small change in rotation rate during the period 1989-2013. We applied the NEATM thermal model (Harris A., Icarus 131, 291, 1998) to our VLT thermal-IR measurements (8-19.6 μm), obtained in September and December 2011. The derived effective diameter ranges from 3.4 to 4.2 km, and the geometric albedo is 0.16 (+0.07, -0.04). Using the shape model and thermal fluxes we will perform a detailed thermophysical analysis using the new Advanced Thermophysical Model (Rozitis, B. & Green, S.F., MNRAS 415, 2042, 2011; Rozitis, B. & Green, S.F., MNRAS 423, 367, 2012). This work was performed in part at the Jet Propulsion Laboratory under a contract with NASA.

Structure and thermal properties of salicylate-based-protic ionic liquids as new heat storage media. COSMO-RS structure characterization and modeling of heat capacities

During this research, we present a study on the thermal properties, such as the melting, cold crystallization, and glass transition temperatures as well as heat capacities from 293.15 K to 323.15 K of nine in-house synthesized protic ionic liquids based on the 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate ([H-Im-C₁OC_n][Sal]) with n = 3–11. The 3D structures, surface charge distributions and COSMO volumes of all investigated ions are obtained by combining DFT calculations and the COSMO-RS methodology. The heat capacity data sets as a function of temperature of the 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate are then predicted using the methodology originally proposed in the case of ionic liquids by Ge et al. 3-(Alkoxymethyl)-1H-imidazol-3-ium salicylate based ionic liquids present specific heat capacities higher in many cases than other ionic liquids that make them suitable as heat storage media and in heat transfer processes. It was found experimentally that the heat capacity increases linearly with increasing alkyl chain length of the alkoxymethyl group of 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate as was expected and predicted using the Ge et al. method with an overall relative absolute deviation close to 3.2% for temperatures up to 323.15 K.

Modeling study of woody biomass:Interactions of cellulose, hemicellulose, and lignin

Lignocellulosic biomass pretreatment and the subsequent thermal conversion processes to produce solid, liquid, and gas biofuels are attractive solutions for today's energy challenges. The structural study of the main components in biomass and their macromolecular complexes is an active and ongoing research topic worldwide. The interactions among the three main components, cellulose, hemicellulose, and lignin, are studied in this paper using electronic structure methods, and the study includes examining the hydrogen bond network of cellulose-hemicellulose systems and the covalent bond linkages of hemicellulose-lignin systems. Several methods (semiempirical, Hartree-Fock, and density functional theory) using different basis sets were evaluated. It was shown that theoretical calculations can be used to simulate small model structures representing wood components. By comparing calculation results with experimental data, it was concluded that B3LYP/6-31G is the most suitable basis set to describe the hydrogen bond system and B3LYP/6-31G(d,p) is the most suitable basis set to describe the covalent system of woody biomass. The choice of unit model has a much larger effect on hydrogen bonding within cellulose-hemicellulose system, whereas the model choice has a minimal effect on the covalent linkage in the hemicellulose-lignin system. © 2011 American Chemical Society.

Bayesian network data imputation with application to survival tree analysis

Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation–maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).

Meanline Modeling of Inlet Recirculation in Automotive Turbocharger Centrifugal Compressors

This study provides a novel meanline modeling approach for centrifugal compressors. All compressors analyzed are of the automotive turbocharger variety and have typical upstream geometry with no casing treatments or preswirl vanes. Past experience dictates that inducer recirculation is prevalent toward surge in designs with high inlet shroud to outlet radius ratios; such designs are found in turbocharger compressors due to the demand for operating range. The aim of the paper is to provide further understanding of impeller inducer flow paths when operating with significant inducer recirculation. Using three-dimensional (3D) computational fluid dynamics (CFD) and a single-passage model, the flow coefficient at which the recirculating flow begins to develop and the rate at which it grows are used to assess and correlate work and angular momentum delivered to the incoming flow. All numerical modeling has been fully validated using measurements taken from hot gas stand tests for all compressor stages. The new modeling approach links the inlet recirculating flow and the pressure ratio characteristic of the compressor. Typically for a fixed rotational speed, between choke and the onset of impeller inlet recirculation the pressure ratio rises gradually at a rate dominated by the aerodynamic losses. However, in modern automotive turbocharger compressors where operating range is paramount, the pressure ratio no longer changes significantly between the onset of recirculation and surge. Instead the pressure ratio remains relatively constant for reducing mass flow rates until surge occurs. Existing meanline modeling techniques predict that the pressure ratio continues to gradually rise toward surge, which when compared to test data is not accurate. A new meanline method is presented here which tackles this issue by modeling the direct effects of the recirculation. The result is a meanline model that better represents the actual fluid flow seen in the CFD results and more accurately predicts the pressure ratio and efficiency characteristics in the region of the compressor map affected by inlet recirculation.

Assessment of Atomic Data: Problems and Solutions

For the reliable analysis and modeling of astrophysical, laser-produced, and fusion plasmas, atomic data are required for a number of parameters, including energy levels, radiative rates, and electron impact excitation rates. Such data are desired for a range of elements (H to W) and their many ions. However, measurements of atomic data, mainly for radiative and excitation rates, are not feasible for many species, and therefore, calculations are needed. For some ions (such as of C, Fe, and Kr), there is a variety of calculations available in the literature, but often, they differ significantly from one another. Therefore, there is a great demand from the user community to have data "assessed" for accuracy so that they can be confidently applied to the modeling of plasmas. In this paper we highlight the difficulties in assessing atomic data and offer some solutions for improving the accuracy of calculated results.

Three-dimensional modeling of the human fallopian tube fimbriae

OBJECTIVE: Ovarian cancer is the most lethal gynecological malignancy that affects women. Recent data suggests that the disease may originate in the fallopian fimbriae; however, the anatomical origin of ovarian carcinogenesis remains unclear. This is largely driven by our lack of knowledge regarding the structure and function of normal fimbriae and the relative paucity of models that accurately recapitulate the in vivo fallopian tube. Therefore, a human three-dimensional (3D) culture system was developed to examine the role of the fallopian fimbriae in serous tumorigenesis.

METHODS: Alginate matrix was utilized to support human fallopian fimbriae ex vivo. Fimbriae were cultured with factors hypothesized to contribute to carcinogenesis, namely; H2O2 (1mM) a mimetic of oxidative stress, insulin (5μg/ml) to stimulate glycolysis, and estradiol (E2, 10nM) which peaks before ovulation. Cultures were evaluated for changes in proliferation and p53 expression, criteria utilized to identify potential precursor lesions. Further, secretory factors were assessed after treatment with E2 to identify if steroid signaling induces a pro-tumorigenic microenvironment.

RESULTS: 3D fimbriae cultures maintained normal tissue architecture up to 7days, retaining both epithelial subtypes. Treatment of cultures with H2O2 or insulin significantly induced proliferation. However, p53 stabilization was unaffected by any particular treatment, although it was induced by ex vivo culturing. Moreover, E2-alone treatment significantly induced its canonical target PR and expression of IL8, a factor linked to poor outcome.

CONCLUSIONS: 3D alginate cultures of human fallopian fimbriae provide an important microphysiological model, which can be further utilized to investigate serous tumorigenesis originating from the fallopian tube.

The Type IIP Supernova 2012aw in M95: Hydrodynamical Modeling of the Photospheric Phase from Accurate Spectrophotometric Monitoring

We present an extensive optical and near-infrared photometric and spectroscopic campaign of the Type IIP supernova SN 2012aw. The data set densely covers the evolution of SN 2012aw shortly after the explosion through the end of the photospheric phase, with two additional photometric observations collected during the nebular phase, to fit the radioactive tail and estimate the ⁵⁶Ni mass. Also included in our analysis is the previously published Swift UV data, therefore providing a complete view of the ultraviolet-optical- infrared evolution of the photospheric phase. On the basis of our data set, we estimate all the relevant physical parameters of SN 2012aw with our radiation-hydrodynamics code: envelope mass M _env ∼ 20 M _⊙, progenitor radius R ∼ 3 × 10¹³ cm (∼430 R_⊙), explosion energy E ∼ 1.5 foe, and initial ⁵⁶Ni mass ∼0.06 M_⊙. These mass and radius values are reasonably well supported by independent evolutionary models of the progenitor, and may suggest a progenitor mass higher than the observational limit of 16.5 ± 1.5 M _⊙of the Type IIP events. 

Concrete damage plasticity model for modeling FRP-to-concrete bond behavior

The technique of externally bonding fiber-reinforced polymer (FRP) composites has become very popular worldwide for retrofitting existing reinforced concrete (RC) structures. Debonding of FRP from the concrete substrate is a typical failure mode in such strengthened structures. The bond behavior between FRP and concrete thus plays a crucial role in these structures. The FRP-to-concrete bond behavior has been extensively investigated experimentally, commonly using a single or double shear test of the FRP-to-concrete bonded joint. Comparatively, much less research has been concerned with numerical simulation, chiefly due to difficulties in the accurate modeling of the complex behavior of concrete. This paper presents a simple but robust finite-element (FE) model for simulating the bond behavior in the entire debonding process for the single shear test. A concrete damage plasticity model is proposed to capture the concrete-to-FRP bond behavior. Numerical results are in close agreement with test data, validating the model. In addition to accuracy, the model has two further advantages: it only requires the basic material parameters (i.e., no arbitrary user-defined parameter such as the shear retention factor is required) and it can be directly implemented in the FE software ABAQUS.

Solutions of fourth-order parabolic equation modeling thin film growth

In this paper we study the well-posedness for a fourth-order parabolic equation modeling epitaxial thin film growth. Using Kato's Method [1], [2] and [3] we establish existence, uniqueness and regularity of the solution to the model, in suitable spaces, namelyC⁰([0,T];L^p(Ω)) where  with 1<α<2, n∈N and n≥2. We also show the global existence solution to the nonlinear parabolic equations for small initial data. Our main tools are L_p–L_q-estimates, regularization property of the linear part of e^−tΔ2 and successive approximations. Furthermore, we illustrate the qualitative behavior of the approximate solution through some numerical simulations. The approximate solutions exhibit some favorable absorption properties of the model, which highlight the stabilizing effect of our specific formulation of the source term associated with the upward hopping of atoms. Consequently, the solutions describe well some experimentally observed phenomena, which characterize the growth of thin film such as grain coarsening, island formation and thickness growth.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

Detecting localized homogeneous anomalies over spatio-temporal data

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as – weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases – i) discovering homogeneous regions, and ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.

Evidence of syntactic working memory usage in MEG data

While reading times are often used to measure working memory load, frequency effects (such as surprisal or n-gram frequencies) also have strong confounding effects on reading times. This work uses a naturalistic audio corpus with magnetoencephalographic (MEG) annotations to measure working memory load during sentence processing. Alpha oscillations in posterior regions of the brain have been found to correlate with working memory load in non-linguistic tasks (Jensen et al., 2002), and the present study extends these findings to working memory load caused by syntactic center embeddings. Moreover, this work finds that frequency effects in naturally-occurring stimuli do not significantly contribute to neural oscillations in any frequency band, which suggests that many modeling claims could be tested on this sort of data even without controlling for frequency effects.

Non-equilibrium modeling of the FE XVII 3C/3D Line Ratio in an Intense X-ray Free-electron Laser Excited Plasma

Recent measurements using an X-ray Free Electron Laser (XFEL) and an Electron Beam Ion Trap at the Linac Coherent Light Source facility highlighted large discrepancies between the observed and theoretical values for the Fe XVII 3C/3D line intensity ratio. This result raised the question of whether the theoretical oscillator strengths may be significantly in error, due to insufficiencies in the atomic structure calculations. We present time-dependent spectral modeling of this experiment and show that non-equilibrium effects can dramatically reduce the predicted 3C/3D line intensity ratio, compared with that obtained by simply taking the ratio of oscillator strengths. Once these non-equilibrium effects are accounted for, the measured line intensity ratio can be used to determine a revised value for the 3C/3D oscillator strength ratio, giving a range from 3.0 to 3.5. We also provide a framework to narrow this range further, if more precise information about the pulse parameters can be determined. We discuss the implications of the new results for the use of Fe XVII spectral features as astrophysical diagnostics and investigate the importance of time-dependent effects in interpreting XFEL-excited plasmas.