Properties of rough interfaces in superconductors with d-wave pairing

Theoretical model of a rough interface in a superconductor with d-wave symmetry of the order parameter is proposed. The surface roughness is introduced by means of a surface layer with small electronic mean free path. The proximity effect between such a layer and a bulk d-wave superconductor is studied theoretically in the framework of the quasiclassical Eilenberger theory. It is shown that as a result of strong scattering in the interlayer the d-wave component of the order parameter near the interface is reduced while the s-wave component localized near the interface is generated. Angular and spatial structure of the pair potential and the electronic density of states near the interface is calculated. The interplay of the zero-energy (midgap) and finite-energy bound states leads to peculiarities in the energy dependence of the angle-averaged density of states. We argue that the model is relevant for the description of rough interfaces in high Tc superconductors. In the framework of the present approach we calculate the Josephson critical current for several types of junctions with rough interface

Understanding the Humanitarian Consequences and Risks of Nuclear Weapons : New findings from recent scholarship

Publisher PD

Methods for improving the performance data processing on an example of the task of constructing routes in DTN networks

The article considers how to speed up data processing in the construction route in DTN network, in particular by applying multi-thread processing using the PHP library “pthreads”, selecting the noSQL MongoDB database and applying additional mathematical methods (fuzzy logic) to reduce the amount of data. The results of testing the PHP library “pthreads” on the obtained data set are presented. And also, it is shown the results of comparing the execution time of queries in the DBMS MySQL and MongoDB

New approaches to pattern discovery in signals via empirical mode decomposition

Empirical mode decomposition (EMD) is an adaptive, data-driven technique for processing and analyzing various types of non-stationary vibrational signals. EMD is a powerful and effective tool for signal preprocessing (denoising, detrending, regularity estimation) and time-frequency analysis. This paper discusses pattern discovery in signals via EMD. New approaches to this problem are introduced. In addition, the methods expounded here may be considered as a way of denoising and coping with the redundancy problem of EMD. A general classification of intrinsic mode functions (IMFs) in accordance with their physical interpretation is offered and an attempt is made to perform classification on the basis of the regression theory, special classification statistics and a clustering algorithm. The main advantage of the suggested techniques is their capability of working automatically. Simulation studies have been undertaken on multiharmonic vibrational signals

Smart collection of measurement from moving objects

This article describes dynamic management’s approach of measurement data streams from moving objects. It allows reducing network traffic and distributing computing all around the measurement acquisition environment. For this purpose, as integration technology of measuring devices, conception of a fog computing is being used. In order to make decisions for switching streams, machine learning methods are being implemented. Experiments proved network traffic’s great reduction of transmissible measurements

Signal denoising based on empirical mode decomposition

The present paper discusses the empirical mode decomposition technique relative to signal denoising, which is often included in signal preprocessing. We provide some basics of the empirical mode decomposition and introduce intrinsic mode functions with the corresponding illustrations. The problem of denoising is described in the paper and we illustrate denoising using soft and hard thresholding with the empirical mode decomposition. Furthermore, we introduce a new approach to signal denoising in the case of heteroscedastic noise using a classification statistics. Our denoising procedure is shown for a harmonic signal and a smooth curve corrupted with white Gaussian heteroscedastic noise. We conclude that empirical mode decomposition is an efficient tool for signal denoising in the case of homoscedastic and heteroscedastic noise. Finally, we also provide some information about denoising applications in vibrational signal analysis

Superconducting weak bonds at grain boundaries in MgB2

The possibility of preparing bicrystalline Josephson junctions and bolometers based on superconducting MgB2 on specially prepared bicrystalline MgO substrates is investigated. Microbridges 0.85–6.00 μm in width, intersecting the bicrystalline interface, are formed in epitaxial bicrystalline MgB2 films grown on these substrates. It is found that annealing of bicrystalline samples in oxygen leads to a systematic decrease in the critical current, an increase in the temperature width of the superconducting transition region, and to an improvement of the current-voltage (IV) characteristic, which becomes close in shape to the IV characteristic of a Josephson junction. The response of such a junction to radiation at a frequency of 110 GHz with an amplitude attaining 0.5 mV is measured