A viscoelastic Rivlin-Ericksen material model applicable to glacier ice

We present a viscoelastic constitutive relation which describes transient creep of a modified second grade fluid enhanced with elastic properties of a solid. The material law describes a Rivlin-Ericksen material and is a generalization of existing material laws applied to study the viscoelastic properties of ice. The intention is to provide a formulation tailored to reproduce the viscoelastic behaviour of ice ranging from the instantaneous elastic response, to recoverable deformation, to viscous, stationary flow at the characteristic minimum creep rate associated with the deformation of polycrystalline ice. We numerically solve the problem of a slab of material shearing down a uniformly inclined plate. The equations are made dimensionless in a form in which elastic effects and/or the influence of higher order terms (i.e., strain accelerations) can be compared with viscous creep at the minimum creep rate by means of two dimensionless parameters. We discuss the resulting material behaviour and the features exhibited at different parameter combinations. Also, a viable range of the non-dimensional parameters is estimated in the scale analysis

Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties

Glyphosate in waters and soils from genetically modified canola cultivation in Parkes, NSW, Australia

Investigations were conducted of farmland from the Parkes region of New South Wales, Australia, cultivated with genetically modified canola, involving the determination of glyphosate (N-(phosphonomethyl)glycine) concentrations in water and soils, and its sorption. The soils are classified as loam under the USDA system (clay 13.8-15.8%, silt 39-43%, sand 41.2-47.2%). Firstly, a low-cost fluorometric method was developed for the analysis of glyphosate in waters and soils, calibrated against analytical standards and spectrophotometric and enzyme-linked immunosorbent assay (ELISA) methods. Soil and water samples were then collected using the NEPM sampling protocol into glass containers, chilled and analysed within two weeks. The samples were collected in multiple episodes, taking account of glyphosate and pesticide crop applications. The soil and water physical and chemical properties were characterised, and glyphosate levels were determined. Field concentrations of glyphosate ranged between 0.01 - 0.067 mg/L in water and 0.10 - 0.575 mg/kg in soil. The aqueous levels lie below Australian and international drinking water guidelines, but reach a Canadian freshwater guideline. Glyphosate levels varied with time of application and rainfall events. Glyphosate sorption isotherms were also constructed by batch tests on several soils, and were fitted with Freundlich and Langmuir isotherms. Desorption tests indicated 25% to 58% of soil glyphosate is extractable by 0.1M KH2PO4

Discriminative prototype selection methods for graph embedding

Graphs possess a strong representational power for many types of patterns. However, a main limitation in their use for pattern analysis derives from their difficult mathematical treatment. One way of circumventing this problem is that of transforming the graphs into a vector space by means of graph embedding. Such an embedding can be conveniently obtained by using a set of prototype graphs and a dissimilarity measure. However, when we apply this approach to a set of class-labelled graphs, it is challenging to select prototypes capturing both the salient structure within each class and inter-class separation. In this paper, we introduce a novel framework for selecting a set of prototypes from a labelled graph set taking their discriminative power into account. Experimental results showed that such a discriminative prototype selection framework can achieve superior results in classification compared to other well-established prototype selection approaches. © 2012 Elsevier Ltd

Automatic human action recognition in videos by graph embedding

The problem of human action recognition has received increasing attention in recent years for its importance in many applications. Yet, the main limitation of current approaches is that they do not capture well the spatial relationships in the subject performing the action. This paper presents an initial study which uses graphs to represent the actor's shape and graph embedding to then convert the graph into a suitable feature vector. In this way, we can benefit from the wide range of statistical classifiers while retaining the strong representational power of graphs. The paper shows that, although the proposed method does not yet achieve accuracy comparable to that of the best existing approaches, the embedded graphs are capable of describing the deformable human shape and its evolution along the time. This confirms the interesting rationale of the approach and its potential for future performance. © 2011 Springer-Verlag

Penicillium verrucosum occurrence and Ochratoxin A contents in organically cultivated grain with special reference to ancient wheat types and drying practice

This study addresses the relationship between the ochratoxigenic strains of Penicillium verrucosum and ochratoxin A (OTA) contents in organically cultivated grain. It included 37 combined, non-dried grain samples from farmers with no drying facilities as well as 19 non-dried and 22 dried samples from six farms with on-farm drying facilities (Case studies 1-6). The study focused on the ancient wheat type spelt but also included samples of wheat, rye, barley, oats, triticale, emmer, and einkorn. All 78 samples were analysed for moisture content (MC) and occurrence of P. verrucosum. The latter was assessed by plating non-disinfected kernels on DYSG agar and counting those contaminated by the fungus. Fiftyfive samples were analysed for OTA. Most of the combine harvested samples (82%) were contaminated with P. verrucosum prior to drying. This was ascribed to difficult harvest conditions and many samples of spelt, which was significantly more contaminated by P. verrucosum than oats, wheat and barley. Though not statistically significant, the results also indicated that spelt was more contaminated than rye, which is usually regarded the most sensitive small grain cereal. No correlation was found between number of kernels contaminated by P. verrucosum and OTA content. Despite many non-dried samples being contaminated by P. verrucosum, only two exceeded the EU maximum limit for grain (5 ng OTA g-1), both being spring spelt with 18 and 92 ng g-1, respectively. The problems were most likely correlated to a late harvest and high MC of the grain. The case studies showed exceedings of the maximum limit in a batch of dried oats and spring wheat, respectively, probably to be explained by insufficient drying of late harvested grain with high MC. Furthermore, our results clearly indicate that OTA is not produced in significant amounts in samples with MCs below 17%. All dried samples with MCs above 18% exceeded the 5 ng OTA g-1 limit in grain. However, no correlation between MC and the amount of OTA produced was found

Levodopa‐induced dyskinesia are mediated by cortical gamma oscillations in experimental Parkinsonism

Background Levodopa is the most efficacious drug in the symptomatic therapy of motor symptoms in Parkinson's disease (PD); however, long‐term treatment is often complicated by troublesome levodopa‐induced dyskinesia (LID). Recent evidence suggests that LID might be related to increased cortical gamma oscillations. Objective The objective of this study was to test the hypothesis that cortical high‐gamma network activity relates to LID in the 6‐hydroxydopamine model and to identify new biomarkers for adaptive deep brain stimulation (DBS) therapy in PD. Methods We recorded and analyzed primary motor cortex (M1) electrocorticogram data and motor behavior in freely moving 6‐OHDA lesioned rats before and during a daily treatment with levodopa for 3 weeks. The results were correlated with the abnormal involuntary movement score (AIMS) and used for generalized linear modeling (GLM). Results Levodopa reverted motor impairment, suppressed beta activity, and, with repeated administration, led to a progressive enhancement of LID. Concurrently, we observed a highly significant stepwise amplitude increase in finely tuned gamma (FTG) activity and gamma centroid frequency. Whereas AIMS and FTG reached their maximum after the 4th injection and remained on a stable plateau thereafter, the centroid frequency of the FTG power continued to increase thereafter. Among the analyzed gamma activity parameters, the fraction of longest gamma bursts showed the strongest correlation with AIMS. Using a GLM, it was possible to accurately predict AIMS from cortical recordings. Conclusions FTG activity is tightly linked to LID and should be studied as a biomarker for adaptive DBS