Ash plume properties retrieved from infrared images: a forward and inverse modeling approach

We present a coupled fluid-dynamic and electromagnetic model for volcanic ash plumes. In a forward approach, the model is able to simulate the plume dynamics from prescribed input flow conditions and generate the corresponding synthetic thermal infrared (TIR) image, allowing a comparison with field-based observations. An inversion procedure is then developed to retrieve ash plume properties from TIR images. The adopted fluid-dynamic model is based on a one-dimensional, stationary description of a self-similar (top-hat) turbulent plume, for which an asymptotic analytical solution is obtained. The electromagnetic emission/absorption model is based on the Schwarzschild's equation and on Mie's theory for disperse particles, assuming that particles are coarser than the radiation wavelength and neglecting scattering. [...] Application of the inversion procedure to an ash plume at Santiaguito volcano (Guatemala) has allowed us to retrieve the main plume input parameters, namely the initial radius $b_0$, velocity $U_0$, temperature $T_0$, gas mass ratio $n_0$, entrainment coefficient $k$ and their related uncertainty. Moreover, coupling with the electromagnetic model, we have been able to obtain a reliable estimate of the equivalent Sauter diameter $d_s$ of the total particle size distribution. The presented method is general and, in principle, can be applied to the spatial distribution of particle concentration and temperature obtained by any fluid-dynamic model, either integral or multidimensional, stationary or time-dependent, single or multiphase. The method discussed here is fast and robust, thus indicating potential for applications to real-time estimation of ash mass flux and particle size distribution, which is crucial for model-based forecasts of the volcanic ash dispersal process.Comment: 41 pages, 13 figures, submitted pape

Simultaneous calculation of the helical pitch and the twist elastic constant in chiral liquid crystals from intermolecular torques

We present a molecular simulation method that yields simultaneously the equilibrium pitch wave number q and the twist elastic constant K2 of a chiral nematic liquid crystal by sampling the torque density. A simulation of an untwisted system in periodic boundary conditions gives the product K2q; a further simulation with a uniform twist applied provides enough information to separately determine the two factors. We test our new method for a model potential, comparing the results with K2q from a thermodynamic integration route, and with K2 from an order fluctuation analysis. We also present a thermodynamic perturbation theory analysis valid in the limit of weak chirality

Two Supervised Neural Networks for Classification ofSedimentary Organic Matter Images fromPalynological Preparations

An improvement in the supervised artificial neural network classification of sedimentary organic matter images from palynological preparations is presented. Sedimentary organic matter encompasses the entire acid-resistant organic micro-particles (typically with a diameter of 5-500μm) recovered from a sediment or sedimentary rock. Supervised neural networks are trained to recognize patterns within databases for which the correct classifications are already known. Once trained, they are verified on pre-classified samples not seen by the network, and then used for classification of samples whose class is not known. Such networks have an input, hidden and output layer. Typically, these networks determine what the output class is by adjusting weights associated with the layer interconnects, and by modifying the signals that propagate through the hidden layer by a non-linear transfer function. In this example, the inputs in each network are the salient features selected from an available set of 194, while the outputs are the sedimentary organic matter classifications which were formerly developed with the rationalization of descriptive terms from previous classification schemes. The author's past work tested the supervised back propagation neural network for the classification of sedimentary organic matter images. This gave an overall correct classification rate of 87%. However, because the back propagation network underperformed on two of the four classes, the radial basis function neural network was tested on the same databases initially used in an attempt to improve the recognition rate of these two classes. The difference between the back propagation and radial basis function networks lies in the non-linear transfer function applied in the hidden layer, which was modified by a Gaussian function in the latter. In the best-case scenario, this improved the recognition rate by 4% to just over 91%. This has also determined that a series of different supervised neural networks may be better for classification of sedimentary organic matter images. These results are encouraging enough to prompt further research that may result in a commercially viable syste

Summation of reinforcement rates when conditioned stimuli are presented in compound

Three experiments used delay conditioning of magazine approach in rats to examine the summation of responding when two conditioned stimuli (CSs) are presented together as a compound. The duration of each CS varied randomly from trial-to-trial around a mean that differed between the CSs. This meant that the rats’ response rate to each CS was systematically related to the reinforcement rate of that CS, but remained steady as time elapsed during the CS (Harris & Carpenter, in press; Harris, Gharaei, & Pincham, in press). When the rats were presented with a compound of two CSs that had been conditioned separately, they responded more during the compound than during either of the CSs individually. More significantly, however, in all three experiments, the rats responded to the compound at the same rate as they responded to a third CS that had been reinforced at a rate equal to the sum of the reinforcement rates of the two CSs in compound. We discuss the implications of this finding for associative models (e.g., Rescorla & Wagner, 1972) and rate-based models (Gallistel & Gibbon, 2000) of conditioning.Grant DP1092695 from the Australian Research Counci

HST Photometry for the Halo Stars in the Leo Elliptical NGC 3377

We have used the ACS camera on HST to obtain (V,I) photometry for 57,000 red-giant stars in the halo of the Leo elliptical NGC 3377. We use this sample of stars to derive the metallicity distribution function (MDF) for its halo field stars, and comment on its chemical evolution history compared with both larger and smaller E galaxies. Our ACS/WFC field spans a radial range extending from 4 to 18 kpc projected distance from the center of NGC 3377 and thus covers a significant portion of this galaxy's halo. We find that the MDF is broad, reaching a peak at [m/H] ~ -0.6$, but containing virtually no stars more metal-poor than log [m/H] = -1.5$. It may, in addition, have relatively few stars more metal-rich than [m/H] = -0.3$, although interpretation of the high-metallicity end of the MDF is limited by photometric completeness that affects the detection of the reddest, most metal-rich stars. NGC 3377 appears to have an enrichment history intermediate between those of normal dwarf ellipticals and the much larger giants. As yet, we find no clear evidence that the halo of NGC 3377 contains a significant population of ``young'' (< 3 Gy) stars.Comment: 40 pages, 17 figure

LSST: Comprehensive NEO Detection, Characterization, and Orbits

(Abridged) The Large Synoptic Survey Telescope (LSST) is currently by far the most ambitious proposed ground-based optical survey. Solar System mapping is one of the four key scientific design drivers, with emphasis on efficient Near-Earth Object (NEO) and Potentially Hazardous Asteroid (PHA) detection, orbit determination, and characterization. In a continuous observing campaign of pairs of 15 second exposures of its 3,200 megapixel camera, LSST will cover the entire available sky every three nights in two photometric bands to a depth of V=25 per visit (two exposures), with exquisitely accurate astrometry and photometry. Over the proposed survey lifetime of 10 years, each sky location would be visited about 1000 times. The baseline design satisfies strong constraints on the cadence of observations mandated by PHAs such as closely spaced pairs of observations to link different detections and short exposures to avoid trailing losses. Equally important, due to frequent repeat visits LSST will effectively provide its own follow-up to derive orbits for detected moving objects. Detailed modeling of LSST operations, incorporating real historical weather and seeing data from LSST site at Cerro Pachon, shows that LSST using its baseline design cadence could find 90% of the PHAs with diameters larger than 250 m, and 75% of those greater than 140 m within ten years. However, by optimizing sky coverage, the ongoing simulations suggest that the LSST system, with its first light in 2013, can reach the Congressional mandate of cataloging 90% of PHAs larger than 140m by 2020.Comment: 10 pages, color figures, presented at IAU Symposium 23

Independent inventors and inbound open innovation: using a resource-based approach to create a tool for screening inventor approaches in order to facilitate technology in-licensing

Open innovation literature identifies independent inventors as a source of novel external knowledge. This knowledge may be licensed into an organisation in order to supplement internal R&D activity, typically as part of an inbound open innovation strategy. In opening an organisation up to approaches from individuals the capacity of the core team to identify promising licensing opportunities is diminished by the sheer volume and variable quality of approaches received. Based on a survey of 202 UK independent inventors this paper utilises a resource-based approach to identifying the key resources possessed by successful independent inventors. Using this data, we devise a preliminary screening tool to facilitate technology in-licensing from independent inventors

The content of compound conditioning

In three experiments using Pavlovian conditioning of magazine approach, rats were trained with a compound stimulus, AB, and were concurrently trained with stimulus B on its own. The reinforcement rate of B, rB, was either ½, ⅔, or ⅖ of rAB. After extended training, the conditioning strength of A was assessed using probe trials in which A was presented alone. Responding during A was compared with that during AB, B, and a third stimulus, C, for which rC = rAB – rB. In each experiment, the rats’ response rate during A was almost identical to that during C (and during B, when rB = ½rAB). This suggests that, during AB conditioning, the rats had learned about rA as being equal to [rAB – rB], and implies that the content of their learning was a linear function of r. The findings provide strong support for rate-based models of conditioning (e.g., Gallistel & Gibbon, 2000). They are also consistent with the associative account of learning defined in the Rescorla-Wagner (1972) model, but only if the learning rate during reinforcement equals that during non-reinforcement.This work was supported by grant DP1092695 from the Australian Research Council