Shear bands and cracking of metallic glass plates in bending

The thickness dependence of yielding and fracture of metallic glass plates subjected to bending is considered in terms of the shear band processes responsible for these properties. We argue that the shear band spacing (and length) scales with the thickness of the plate because of strain relaxation in the vicinity of the shear band at the surface. This is consistent with recent measurements of shear band spacing versus sample size. We also argue that the shear displacements in the shear band scale with the shear band length and plate thickness, thus causing cracks to be initiated in thicker plates at smaller bending strains. This leads to fracture bending strains that decrease markedly with increasing plate thickness, consistent with recent experiments. These results suggest that amorphous metals in the form of foams might have superior ductility and toughness

An Intercomparison of Ground-based Solar FTIR Measurements of Atmospheric Gases at Eureka, Canada

We report the results of an intercomparison of vertical column amounts of hydrogen chloride (HCl), hydrogen fluoride (HF), nitrous oxide (N2O), nitric acid (HNO3), methane (CH4), ozone (O3), carbon dioxide (CO2) and nitrogen (N2) derived from the spectra recorded by two ground-based Fourier transform infrared (FTIR) spectrometers operated side-by-side using the sun as a source. The procedure used to record spectra and derive vertical column amounts follows the format of previous instrument intercomparisons organised by the Network for Detection of Atmospheric Composition Change (NDACC), formerly known as the Network for Detection of Stratospheric Change (NDSC). For most gases the differences were typically around 3% and in about half of the results the error bars given by the standard deviation of the measurements from each instrument did not overlap. The worst level of agreement was for HF where differences of over 5% were typical. The level of agreement achieved during this intercomparison is a little worse than that achieved in previous intercomparisons between ground-based FTIR spectrometers

Trace Gas Emissions from Biomass Burning inferred from Aerosol Optical Depth

We have observed strong correlations between simultaneous and co-located measurements of aerosol optical depth and column amounts of carbon monoxide, hydrogen cyanide, formaldehyde and ammonia in bushfire smoke plumes over SE Australia during the Austral summers of 2001/2002 and 2002/2003. We show how satellite-derived aerosol optical depth maps may be used in conjunction with these correlations to determine the total amounts of these gases present in a fire-affected region. This provides the basis of a method for estimating total emissions of trace gases from biomass burning episodes using visible radiances measured by satellite

How to increase earthquake and home fire preparedness: the fix-it intervention

Published, evaluated community intervention studies concerning natural hazard preparedness are rare. Most lack a rigorous methodology, thereby hampering the development of evidence-based interventions. This paper describes the rationale and methodology of a cross-cultural, longitudinal intervention study on earthquake and home fire preparedness, termed fix-it. The aim is to evaluate whether and how the intervention brings about behaviour change in the targeted communities in two coastal cities with high seismic risk: Seattle, USA and Izmir, Turkey. Participants are adult residents of these cities. The intervention group attends a 6-h workshop, which focuses on securing items in the household. The control group does not attend the workshop. All participants complete baseline and post-intervention, as well as 3- and 12-month follow-up assessments. The primary outcome measure is an observational measure of nine preparedness items for earthquake and fire in participants’ homes. This is evaluated alongside participants’ self-reports concerning their preparedness levels. Secondary outcomes are changes in levels of self-efficacy, perceived outcome, trust, corruption, empowerment, anxiety and social cohesion. Results from the first of the studies, conducted in Seattle in September 2015, indicate that while the fix-it intervention is effective, in the longer term, multi-hazard preparedness is increased by the mere act of going into people’s homes to observe their preparedness levels along with assessing self-reported preparedness and sociopsychological orientation towards natural hazards. This protocol and study aim to augment the empirical literature on natural hazard preparedness, informing national and international policy on delivery of evidence-based community interventions to promote multi-hazard preparedness in households

A chemotactic-based model for spatial activity recognition

Spatial activity recognition in everyday environments is particularly challenging due to noise incorporated during video-tracking. We address the noise issue of spatial recognition with a biologically inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to survive by changing motile behaviour in relation to environmental dynamics. Using chemotactic principles, we propose the chemotactic model and evaluate its classification performance in a smart house environment. The model exhibits high classification accuracy (99%) with a diverse 10 class activity dataset and outperforms the discrete hidden Markov model (HMM). High accuracy (>89%) is also maintained across small training sets and through incorporation of varying degrees of artificial noise into testing sequences. Importantly, unlike other bottom–up spatial activity recognition models, we show that the chemotactic model is capable of recognizing simple interwoven activities

A classification of tasks for the systematic study of immune response using functional genomics data

A full understanding of the immune system and its responses to infection by different pathogens is important for the development of anti-parasitic vaccines. A growing number of large-scale experimental techniques, such as microarrays, are being used to gain a better understanding of the immune system. To analyse the data generated by these experiments, methods such as clustering are widely used. However, individual applications of these methods tend to analyse the experimental data without taking publicly available biological and immunological knowledge into account systematically and in an unbiased manner. To make best use of the experimental investment, to benefit from existing evidence, and to support the findings in the experimental data, available biological information should be included in the analysis in a systematic manner. In this review we present a classification of tasks that shows how experimental data produced by studies of the immune system can be placed in a broader biological context. Taking into account available evidence, the classification can be used to identify different ways of analysing the experimental data systematically. We have used the classification to identify alternative ways of analysing microarray data, and illustrate its application using studies of immune responses in mice to infection with the intestinal nematode parasites Trichuris muris and Heligmosomoides polygyrus

Explicit BCJ Numerators from Pure Spinors

We derive local kinematic numerators for gauge theory tree amplitudes which manifestly satisfy Jacobi identities analogous to color factors. They naturally emerge from the low energy limit of superstring amplitudes computed with the pure spinor formalism. The manifestation of the color--kinematics duality is a consequence of the superstring computation involving no more than (n-2)! kinematic factors for the full color dressed n-point amplitude. The bosonic part of these results describe gluon scattering independent on the number of supersymmetries and captures any N^kMHV helicity configuration after dimensional reduction to D=4 dimensions.Comment: 32 pages, harvma

Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus

Identifying when past exposure to an infectious disease will protect against newly emerging strains is central to understanding the spread and the severity of epidemics, but the prediction of viral cross-protection remains an important unsolved problem. For foot-and-mouth disease virus (FMDV) research in particular, improved methods for predicting this cross-protection are critical for predicting the severity of outbreaks within endemic settings where multiple serotypes and subtypes commonly co-circulate, as well as for deciding whether appropriate vaccine(s) exist and how much they could mitigate the effects of any outbreak. To identify antigenic relationships and their predictors, we used linear mixed effects models to account for variation in pairwise cross-neutralization titres using only viral sequences and structural data. We identified those substitutions in surface-exposed structural proteins that are correlates of loss of cross-reactivity. These allowed prediction of both the best vaccine match for any single virus and the breadth of coverage of new vaccine candidates from their capsid sequences as effectively as or better than serology. Sub-sequences chosen by the model-building process all contained sites that are known epitopes on other serotypes. Furthermore, for the SAT1 serotype, for which epitopes have never previously been identified, we provide strong evidence - by controlling for phylogenetic structure - for the presence of three epitopes across a panel of viruses and quantify the relative significance of some individual residues in determining cross-neutralization. Identifying and quantifying the importance of sites that predict viral strain cross-reactivity not just for single viruses but across entire serotypes can help in the design of vaccines with better targeting and broader coverage. These techniques can be generalized to any infectious agents where cross-reactivity assays have been carried out. As the parameterization uses pre-existing datasets, this approach quickly and cheaply increases both our understanding of antigenic relationships and our power to control disease