Spontaneous polarization and piezoelectricity in boron nitride nanotubes

Ab initio calculations of the spontaneous polarization and piezoelectric properties of boron nitride nanotubes show that they are excellent piezoelectric systems with response values larger than those of piezoelectric polymers. The intrinsic chiral symmetry of the nanotubes induces an exact cancellation of the total spontaneous polarization in ideal, isolated nanotubes of arbitrary indices. Breaking of this symmetry by inter-tube interaction or elastic deformations induces spontaneous polarization comparable to those of wurtzite semiconductors.Comment: 5 pages in PRB double column format, 3 figure

Surface Polar Phonon Dominated Electron Transport in Graphene

The effects of surface polar phonons on electronic transport properties of monolayer graphene are studied by using a Monte Carlo simulation. Specifically, the low-field electron mobility and saturation velocity are examined for different substrates (SiC, SiO2, and HfO2) in comparison to the intrinsic case. While the results show that the low-field mobility can be substantially reduced by the introduction of surface polar phonon scattering, corresponding degradation of the saturation velocity is not observed for all three substrates at room temperature. It is also found that surface polar phonons can influence graphene electrical resistivity even at low temperature, leading potentially to inaccurate estimation of the acoustic phonon deformation potential constant

First principle theory of correlated transport through nano-junctions

We report the inclusion of electron-electron correlation in the calculation of transport properties within an ab initio scheme. A key step is the reformulation of Landauer's approach in terms of an effective transmittance for the interacting electron system. We apply this framework to analyze the effect of short range interactions on Pt atomic wires and discuss the coherent and incoherent correction to the mean-field approach.Comment: 5 pages, 3 figure

Answering to physics teachers' needs in professional development

The IDIFO project conducted at the University of Udine, in collaboration with 18 Italian universities, is an example of integration and collaboration between schools and universities proposals on innovation in physics education. The aspects of the project relating to the professional development of teachers are discussed, presenting the various implementation methods designed and activated, also answering to the formative needs of schools relating to laboratory-based scientific teaching/learning

From one slit to diffraction grating: Optical physics lab by means of computer on-line sensors

Diffraction is a crucial phenomenon and its interpretation bridges from geometrical to wave optics and from wave optics to Quantum mechanics. Becoming familiar with the characteristics of the diffraction in the cases of one, two, many slits is an important experience for students not only from the subject point of view, but also on the methodological plan. The exploration of the relative interpretation by means of simulation and modelling offers to the students the opportunity to experience the typical methodological work in physics. An educational proposal was developed for the study of optical diffraction: from the analysis of a single slit diffraction, to a double slit and to a diffraction grating. It is based on a USB acquisition system designed and developed for experimental data acquisition in an educational lab, correlating position and light intensity measurements in one direction. Data can be exported in text format and data fitting can be done by means of an electronic sheet. The phenomenon laws obtained by data are interpreted under the wave nature of light by students and these laws are used for spectroscopic analysis of different light sources. Simulation software allows to build models of interpretation of phenomenology based on the first principles

A Machine Learning-Based Approach for Audio Signals Classification using Chebychev Moments and Mel-Coefficients

This paper proposes a machine learning-based architecture for audio signals classification based on a joint exploitation of the Chebychev moments and the Mel-Frequency Cepstrum Coefficients. The procedure starts with the computation of the Mel-spectrogram of the recorded audio signals; then, Chebychev moments are obtained projecting the Cadence Frequency Diagram derived from the Mel-spectrogram into the base of Chebychev moments. These moments are then concatenated with the Mel-Frequency Cepstrum Coefficients to form the final feature vector. By doing so, the architecture exploits the peculiarities of the discrete Chebychev moments such as their symmetry characteristics. The effectiveness of the procedure is assessed on two challenging datasets, UrbanSound8K and ESC-50

MAC Europe 1991 campaign: AIRSAR/AVIRIS data integration for agricultural test site classification

During summer 1991, multi-sensor data were acquired over the Italian test site 'Otrepo Pavese', an agricultural flat area in Northern Italy. This area has been the Telespazio pilot test site for experimental activities related to agriculture applications. The aim of the investigation described in the following paper is to assess the amount of information contained in the AIRSAR (Airborne Synthetic Aperture Radar) and AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) data, and to evaluate classification results obtained from each sensor data separately and from the combined dataset. All classifications are examined by means of the resulting confusion matrices and Khat coefficients. Improvements of the classification results obtained by using the integrated dataset are finally evaluated