Particle creation in a colliding plane wave spacetime: Wave packet quantization

We use wave packet mode quantization to compute the creation of massless scalar quantum particles in a colliding plane wave spacetime. The background spacetime represents the collision of two gravitational shock waves followed by trailing gravitational radiation which focus into a Killing-Cauchy horizon. The use of wave packet modes simplifies the problem of mode propagation through the different spacetime regions which was previously studied with the use of monochromatic modes. It is found that the number of particles created in a given wave packet mode has a thermal spectrum with a temperature which is inversely proportional to the focusing time of the plane waves and which depends on the mode trajectory.

Multifractal wavelet filter of natural images

Natural images are characterized by the multiscaling properties of their contrast gradient, in addition to their power spectrum. In this Letter we show that those properties uniquely define an intrinsic wavelet and present a suitable technique to obtain it from an ensemble of images. Once this wavelet is known, images can be represented as expansions in the associated wavelet basis. The resulting code has the remarkable properties that it separates independent features at different resolution level, reducing the redundancy, and remains essentially unchanged under changes in the power spectrum. The possible generalization of this representation to other systems is discussed.

Space and momentum representation analysis of Hartman's effect in wave packet transmission

The Hartman effect is analyzed in both the position and momentum representations of the problem. The importance of Wigner tunneling and deep tunneling is singled out. It is shown quantitatively how the barrier acts as a filter for low momenta (quantum speed up) as the width increases, and a detailed mechanism is proposed. Superluminal transmission is also discussed.

Socio-economic inequalities in reported depression in Spain : a decomposition approach

Recent evidence questions some conventional view on the existence of income-related inequalities in depression suggesting in turn that other determinants might be in place, such as activity status and educational attainment. Evidence of socio-economic inequalities is especially relevant in countries such as Spain that have a limited coverage of mental health care and are regionally heterogeneous. This paper aims at measuring and explaining the degree of socio-economic inequality in reported depression in Spain. We employ linear probability models to estimate the concentration index and its decomposition drawing from 2003 edition of the Spanish National Health Survey, the most recent representative health survey in Spain. Our findings point towards the existence of avoidable inequalities in the prevalence of reported depression. However, besides ¿pure income effects¿ explaining 37% of inequality, economic activity status (28%), education (15%) and demographics (15%) play also a key encompassing role. Although high income implies higher resources to invest and cure (mental) illness, environmental factors influencing in peoples perceived social status act as indirect path as explaining the prevalence of depression. Finally, we find evidence of a gender effect, gender social-economic inequality in income is mainly avoidable.

Decomposition of Ruppia cirrhosa (Petagna) Grande in the sediment of a coastal lagoon

The decomposition process of Ruppia cirrhosa was studied in a Mediterranean coastal lagoon in the Delta of the River Ebro (NE Spain). Leaves and shoots of Ruppia were enclosed in 1 mm-mesh and 100 pm-mesh litter bags to ascertain the effect of detritivores, macroinvertebrates, and bacteria and fungi, respectively. Changes in biomass and carbon, and, nitrogen and phosphorus concentrations in the detritus were studied at the sediment-water interface and in the sediment. Significant differences in biomass decay were observed between the two bag types. Significant differences in decomposition were observed between the two experimental conditions studied using 100 pm-mesh bags. These differences were not significant when using the 1 mm-mesh bags. The carbon content in the detritus remained constant during the decomposition process. The percentage of nitrogen increased progressively from an initial 2.4 % to 3 %. The percentage of phosphorus decreased rapidly during the first two days of decomposition from an initial 0.26 % to 0.17 %. This loss is greater in the sediment than in the water column or at the sediment-water interface. From these results we deduce that the activity of microorganisms seems to be more important in the sediment than in the water-sediment interface, and that grazing by macroinvertebrates has less importance in the sediment than in the water column.

MMPBSA decomposition of the binding energy throughout a molecular dynamics simulation of Amyloid-Beta (Aß10-35) aggregation

Recent experiments with amyloid-beta (Aß) peptides indicate that the formation of toxic oligomers may be an important contribution to the onset of Alzheimer's disease. The toxicity of Aß oligomers depend on their structure, which is governed by assembly dynamics. However, a detailed knowledge of the structure of at the atomic level has not been achieved yet due to limitations of current experimental techniques. In this study, replica exchange molecular dynamics simulations are used to identify the expected diversity of dimer conformations of Aß10-35 monomers. The most representative dimer conformation has been used to track the dimer formation process between both monomers. The process has been characterized by means of the evolution of the decomposition of the binding free energy, which provides an energetic profile of the interaction. Dimers undergo a process of reorganization driven basically by inter-chain hydrophobic and hydrophilic interactions and also solvation/desolvation processes.

Empirics of the international inequality in CO2 emissions intensity: explanatory factors according to complementary decomposition methodologies

This paper analyses the international inequalities in CO2 emissions intensity for the period 1971–2009 and assesses explanatory factors. Multiplicative, group and additive methodologies of inequality decomposition are employed. The first allows us to clarify the separated role of the carbonisation index and the energy intensity in the pattern observed for inequalities in CO2 intensities; the second allows us to understand the role of regional groups; and the third allows us to investigate the role of different fossil energy sources (coal, oil and gas). The results show that, first, the reduction in global emissions intensity has coincided with a significant reduction in international inequality. Second, the bulk of this inequality and its reduction are attributed to differences between the groups of countries considered. Third, coal is the main energy source explaining these inequalities, although the growth in the relative contribution of gas is also remarkable. Fourth, the bulk of inequalities between countries and its decline are explained by differences in energy intensities, although there are significant differences in the patterns demonstrated by different groups of countries. JEL codes: D39; Q43; Q56. Key words: CO2 international distribution, inequality decomposition, CO2 emissions intensity

Real-time monitoring of the silicidation process of tungsten filaments at high temperature used as catalysers for silane decomposition

The scope of this work is the systematic study of the silicidation process affecting tungsten filaments at high temperature (1900ºC) used for silane decomposition in the hot-wire chemical vapour deposition technique (HWCVD). The correlation between the electrical resistance evolution of the filaments, Rfil(t), and the different stages of the their silicidation process is exposed. Said stages correspond to: the rapid formation of two WSi2 fronts at the cold ends of the filaments and their further propagation towards the middle of the filaments; and, regarding the hot central portion of the filaments: a initial stage of silicon dissolution into the tungsten bulk, with a random duration for as-manufactured filaments, followed by the inhomogeneous nucleation of W5Si3 (which is later replaced by WSi2) and its further growth towards the filaments core. An electrical model is used to obtain real-time information about the current status of the filaments silicidation process by simply monitoring their Rfil(t) evolution during the HWCVD process. It is shown that implementing an annealing pre-treatment to the filaments leads to a clearly repetitive trend in the monitored Rfil(t) signatures. The influence of hydrogen dilution of silane on the filaments silicidation process is also discussed.

Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts

EEG recordings are usually corrupted by spurious extra-cerebral artifacts, which should be rejected or cleaned up by the practitioner. Since manual screening of human EEGs is inherently error prone and might induce experimental bias, automatic artifact detection is an issue of importance. Automatic artifact detection is the best guarantee for objective and clean results. We present a new approach, based on the time–frequency shape of muscular artifacts, to achieve reliable and automatic scoring. The impact of muscular activity on the signal can be evaluated using this methodology by placing emphasis on the analysis of EEG activity. The method is used to discriminate evoked potentials from several types of recorded muscular artifacts—with a sensitivity of 98.8% and a specificity of 92.2%. Automatic cleaning ofEEGdata are then successfully realized using this method, combined with independent component analysis. The outcome of the automatic cleaning is then compared with the Slepian multitaper spectrum based technique introduced by Delorme et al (2007 Neuroimage 34 1443–9).

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition

Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%.

The effect of pesticides and alien invesive species on soil biota and litter decomposition rates in a Mediterrean-climate ecosystem of Western Australia

Ecosystems are complex systems and changing one of their components can alter their whole functioning. Decomposition and biodiversity are two factors that play a role in this stability, and it is vital to study how these two factors are interrelated and how other factors, whether of human origin or not, can affect them. This study has tested different hypotheses regarding the effects of pesticides and invasive species on the biodiversity of the soil fauna and litter decomposition rate. Decomposition was measured using the litterbags technique. Our results indicate that pesticides had a negative effect on decomposition whereas invasive species increased decomposition rate. At the same time, the diversity of the soil biota was unaffected by either factor. These results allow us to better understand the response of important ecosystem functions to human‐induced alterations, in order to mitigate harmful effects or restore them wherever necessary.

Disentangling water usage in the European Union: A decomposition analysis

The Water Framework Directive (WFD) defines common objectives for water resources throughout the European Union (EU). Given this general approach to water preservation and water policy, the objective of this paper is to analyse whether common patterns of water consumption exist within Europe. In particular, our study uses two methods to reveal the reasons behind sectoral water use in all EU countries. The first method is based on an accounting indicator that calculates the water intensity of an economy as the sum of sectoral water intensities. The second method is a subsystem input‐output model that divides total water use into different income channels within the production system. The application uses data for the years 2005 and 2009 on water consumption in the production system of the 27 countries of the EU. From our analysis it emerges that EU countries are characterized by very different patterns of water consumption. In particular water consumption by the agriculture sector is extremely high in Central/Eastern Europe, relative to the rest of Europe. In most countries, the water used by the fuel, power and water sector is consumed to satisfy domestic final demand. However, our analysis shows that for some countries exports from this sector are an important driver of water consumption. Focusing on the agricultural sector, the decomposition analysis suggests that water usage in Mediterranean countries is mainly driven by final demand for, and exports of, agricultural products. In Central/Eastern Europe domestic final demand is the main driver of water consumption, but in this region the proportion of water use driven by demand for exports is increasing over time. Given these heterogeneous water consumption patterns, our analysis suggests that Mediterranean and Central/Eastern European countries should adopt specific water policies in order to achieve efficient levels of water consumption in the European Union. JEL codes: N5; C67 Keywords: Water use, Subsystem input–output model; Water intensity, European Union.

SAR Interferometric phase noise reduction using wavelet transform

The problem of synthetic aperture radar interferometric phase noise reduction is addressed. A new technique based on discrete wavelet transforms is presented. This technique guarantees high resolution phase estimation without using phase image segmentation. Areas containing only noise are hardly processed. Tests with synthetic and real interferograms are reported.