Religious attitudes and home bias: theory and evidence from a pilot study

This paper examines the relationship between religion and home bias. We propose a simple theoretical framework that suggests that countries interacting via their representative individuals might show a certain degree of religion-driven international altruism that in turn affects trade. We test these predictions exploiting data from a survey on religious attitudes and individuals' preferences over consumption of home-produced versus foreign goods that we designed and carried out in 15 different countries. We find evidence that religious openness and home bias are negatively correlated. This appears to provide some support to the hypothesis that religious openness, through trust and altruism, may have a pro-trade effect.

Absolute determination of D_s branching ratios and f_{D_s} extraction at a neutrino factory

A method for a direct measurement of the exclusive D_s branching ratios and of the decay constant f_{D_s} with a systematical error better than 5% is presented. The approach is based on the peculiar vertex topology of the anti-neutrino induced diffractive charm events. The statistical accuracy achievable with a neutrino factory is estimated

Equivalent-voltage approach for modeling low-frequency dispersive effects in microwave FETs

In this paper, a simple and efficient approach for the modeling of low-frequency dispersive phenomena in FETs is proposed. The method is based on the definition of a virtual, nondispersive associated device controlled by equivalent port voltages and it is justified on the basis of a physically-consistent, charge-controlled description of the device. Dispersive effects in FETs are accounted for by means of an intuitive circuit solution in the framework of any existing nonlinear dynamic model. The new equivalent-voltage model is identified on the basis of conventional measurements carried out under static and small signal dynamic operating conditions. Nonlinear experimental tests confirm the validity of the proposed approach

Pattern recognition of acoustic emission signal during the mode I fracture mechanisms in carbon- epoxy composite

The aim of the paper is to use Acoustic Emission technique to distinguish the micro/macro failure mechanisms of carbon-epoxy composite laminates during Double Cantilever Beam (DCB) tests. In order to recognize and detect different damage mechanisms, Self-Organizing Map (SOM) method has been used to cluster the AE signals according with the fracture mode that originated them. In addition, most significate Learning vector quantization (LVQ) program has been applied to verify the signals. Five AE features were selected as main parameters: Rise-time, Counts, Energy, Duration and Amplitude. The results highlighted that different signals can be recognized and classified related to their origin. The failure mechanisms detected are Matrix cracking, delamination, and fiber breakage. Scanning Electron Microscopy (SEM) images validate the results. Mathematics data and experimental results confirmed a good converging of AE dat

Supervised and non-supervised AE data classification of nanomodified CFRP during DCB tests

Aim of the paper is to use acoustic emissions to study the effect of electrospun nylon 6,6 Nanofibrous mat on carbon-epoxy composites during Double Cantilever beam (DCB) tests. In order to recognize the effect of the nanofibres and to detect different damage mechanisms, k-means clustering of acoustic emission signals applied to rise time, count, energy, duration and amplitude of the events is used. Supervised neural network (NN) is then applied to verify clustered signals. Results showed that clustered acoustic emission signals are a reliable tool to detect different damage mechanisms; neural network showed the method has a 99% of accuracy

Clinical relevance of biological variation of B-Type Natriuretic Peptide

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