Microscopic dynamics of glycerol in its crystalline and glassy states

The dynamics of crystalline glycerol are studied by means of Raman spectroscopy and lattice dynamics calculations employing a semiflexible model to represent the low-lying molecular vibrations. The latter is validated against structural, thermodynamic, and spectroscopic data. The results serve to set an absolute frequency scale for glassy glycerol, which is also studied by Raman and incoherent inelastic-neutron scattering. Some implications of the present findings regarding ensuing discussions on glassy dynamics are finally commented on.Dirección General de Investigaciones Científicas y Técnicas PB92- 0114-C0

Thermal transport in glassy selenium: The role of low-frequency librations

The experimental curves giving the temperature dependence of the thermal conductivity of glassy selenium are considered in detail. The observed behavior can be taken into account quantitatively if the densities of states for short-wavelength phonons as well as for low-energy librations arising from computer simulations are used for the calculations. In particular, it is shown that the lowest frequency excitations of a chain of selenium atoms can give due account of the plateau observed at temperatures about 2-10 K. The implications of the present findings for the current debate regarding the mechanisms for thermal transport in glasses are finally discussedDirección General de Investigación Científica y Técnica PB92-0114-C0

Low-frequency excitations in glassy selenium: A comparison of neutron-scattering and molecular-dynamics results

The microscopic low-frequency dynamics of glassy selenium is investigated by means of the concurrent use of neutron inelastic scattering and computer simulations. A separation of the dynamic response in terms of intra- and interchain processes is achieved from the analysis of the simulation results. The S(Q,E) dynamic structure factors are analyzed in terms of the frequency moments or from a model scattering law, and the wave-vector dependence of the relevant quantities is established. Finally, the anomalous behavior of the heat capacity at moderately low temperatures is shown to be originated by mostly interchain interactions.Dirección General de Investigación Científica y Técnica PB89-0037-C

Rotational dynamics in the plastic-crystal phase of ethanol: Relevance for understanding the dynamics during the structural glass transition

The reorientational dynamics within the rotationally disordered cubic plastic phase of solid ethanol is investigated by means of the concurrent use of computer molecular dynamics and quasielastic neutron scattering. Motions involving widely different time scales are shown to take place above the calorimetric "glass transition" which is centered at Tg≈97 K. These correspond to well-defined reorientations belonging to the cubic point group. The dynamics of this solid exhibits features remarkably close to those of the supercooled liquid that can exist at the same temperature. Such similitude of dynamic behavior serves to provide some clues for the understanding of the nature of molecular motions at temperatures close to the canonical liquid→glass transitio

Role of low-frequency vibrations on sound propagation in glasses at intermediate temperature

We report measurements of the temperature dependence of the sound attenuation and the fractional change in sound velocity for the glass (G) and orientational-glass (OG) phases of polymorphic ethanol. Strikingly similar behaviors are found for both phases despite the OG's underlying crystal (bcc) lattice. Such similarity, which is also revealed in dielectric spectroscopy and inelastic neutron scattering measurements, suggests whole molecule small-angle librations as a common microscopic origin for a wide variety of "glassy" phenomena.Dirección General de Investigación Científica y Técnica PB95-0075-C03-0

Identification of tidal features in deep optical galaxy images with Convolutional Neural Networks

Interactions between galaxies leave distinguishable imprints in the form of tidal features which hold important clues about their mass assembly. Unfortunately, these structures are difficult to detect because they are low surface brightness features so deep observations are needed. Upcoming surveys promise several orders of magnitude increase in depth and sky coverage, for which automated methods for tidal feature detection will become mandatory. We test the ability of a convolutional neural network to reproduce human visual classifications for tidal detections. We use as training $\sim$6000 simulated images classified by professional astronomers. The mock Hyper Suprime Cam Subaru (HSC) images include variations with redshift, projection angle and surface brightness ($\mu_{lim}$ =26-35 mag arcsec$^{-2}$). We obtain satisfactory results with accuracy, precision and recall values of Acc=0.84, P=0.72 and R=0.85, respectively, for the test sample. While the accuracy and precision values are roughly constant for all surface brightness, the recall (completeness) is significantly affected by image depth. The recovery rate shows strong dependence on the type of tidal features: we recover all the images showing shell features and 87% of the tidal streams; these fractions are below 75% for mergers, tidal tails and bridges. When applied to real HSC images, the performance of the model worsens significantly. We speculate that this is due to the lack of realism of the simulations and take it as a warning on applying deep learning models to different data domains without prior testing on the actual data.Comment: 13 pages, 10 figures, accepted for publication in MNRA

Identification of tidal features in deep optical galaxy images with convolutional neural networks

Interactions between galaxies leave distinguishable imprints in the form of tidal features, which hold important clues about their mass assembly. Unfortunately, these structures are difficult to detect because they are low surface brightness features, so deep observations are needed. Upcoming surveys promise several orders of magnitude increase in depth and sky coverage, for which automated methods for tidal feature detection will become mandatory. We test the ability of a convolutional neural network to reproduce human visual classifications for tidal detections. We use as training ∼6000 simulated images classified by professional astronomers. The mock Hyper Suprime Cam Subaru (HSC) images include variations with redshift, projection angle, and surface brightness (μlim = 26-35 mag arcsec-2). We obtain satisfactory results with accuracy, precision, and recall values of Acc = 0.84, P = 0.72, and R = 0.85 for the test sample. While the accuracy and precision values are roughly constant for all surface brightness, the recall (completeness) is significantly affected by image depth. The recovery rate shows strong dependence on the type of tidal features: we recover all the images showing shell features and 87 per cent of the tidal streams; these fractions are below 75 per cent for mergers, tidal tails, and bridges. When applied to real HSC images, the performance of the model worsens significantly. We speculate that this is due to the lack of realism of the simulations, and take it as a warning on applying deep learning models to different data domains without prior testing on the actual data

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Re-crystallization of MNA under a strong dc electric field

Crystals of 2-methyl-4-nitroaniline were grown from solution while subjected to a dc electric field of 9.8×10[exponent4] Vm−1. This resulted in an alteration of the unit cell lattice parameters while the space group was modified from Cc to one of its subgroups Pc. The modified crystals display an increased second harmonic generation efficiency accompanied by a decrease in the dielectric permittivity as well as changes in the internal and external vibrational modes