introduction: the aim of this in vitro study was to evaluate the transportation of mesiobuccal canals of mandibular first molars prepared with either biorace or mtwo rotary instruments or hand k-flexofile, by means of cone-beam computed tomography (cbct). methods and materials: forty-five mandibular molars were selected and randomly divided into three groups ( n =15). mesiobuccal roots of these...

in the restructured environment of electricity market, firstly the generating companies and the customers are looking for maximizing their profit and secondly independent system operator is looking for the stability of the power network and maximizing social welfare. in this paper, a one way auction in the electricity market for the generator companies is considered in both perfect and imperfec...

Kernel density estimators are the basic tools for density estimation in non-parametric statistics.  The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. In this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

Abstract— This paper presents the modeling and control back stepping of Double-Fed Induction Generator (DFIG) integrated into a wind energy system. The goal is to apply this technique to control independently the active and reactive power generated by DFIG. Finally, the system performance was tested and compared by simulation in terms of following instructions and robustness with respect to par...

We report on a complete experimental implementation of a quantum key distribution protocol through a free space link using polarization-entangled photon pairs from a compact parametric down-conversion source. Over 10 hours of uninterrupted communication between sites 1.5 km apart, we observe average key generation rates of 630 per second after error correction and privacy amplification. Our sch...

Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion. A careful look at the geometries induced by these distances on the space of probability measures reveals interesting differen...