The Problem of Scheduling for the Linear Section of a Single-Track Railway with Independent Edges Orientations

The paper is devoted to the problem of scheduling for the linear section of a single-track railway: how to organize the ow in both directions in the most efficient way. In this paper, the authors propose an algorithm for scheduling with independent edges orientations, examine the properties of this algorithm and perform the computational experiments

Optimal Scheduling for the Linear Section of a Single-Track Railway with Independent Edges Orientations

The paper is devoted to the problem of organizing the flow in both directions, in the most efficient way, for the linear section of a single-track railway. The authors propose an algorithm for scheduling with independent orientations of edges, investigate the properties of this algorithm and perform computational experiments. The authors also present some estimates for the track capacity of the section

The problem of scheduling for the linear section of a single-track railway

The paper is devoted to the problem of scheduling for the linear section of a single-track railway: how to organize the flow in both directions in the most efficient way. In this paper, the authors propose an algorithm for scheduling, examine the properties of this algorithm and perform the computational experiments. © 2016 Author(s)

A survey on software defect prediction using deep learning

Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability. The problem in this area is to properly identify the defective source code with high accuracy. Developing a fault prediction model is a challenging problem, and many approaches have been proposed throughout history. The recent breakthrough in machine learning technologies, especially the development of deep learning techniques, has led to many problems being solved by these methods. Our survey focuses on the deep learning techniques for defect prediction. We analyse the recent works on the topic, study the methods for automatic learning of the semantic and structural features from the code, discuss the open problems and present the recent trends in the field. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Phase selection and nanocrystallization in Cu-free soft magnetic FeSiNbB amorphous alloy upon rapid annealing

Nucleation of soft magnetic Fe3Si nanocrystals in Cu-free Fe74.5Si15.5Nb3B7 alloy, upon rapid (10 s) and conventional (30 min) annealing, was investigated using x-ray diffraction, transmission electron microscopy, Mössbauer spectroscopy, and atom probe tomography. By employing rapid annealing, preferential nucleation of Fe3Si nanocrystals was achieved, whereas otherwise there is simultaneous nucleation of both Fe3Si and undesired Fe-B compound phases. Analysis revealed that the enhanced Nb diffusivity, achieved during rapid annealing, facilitates homogeneous nucleation of Fe3Si nanocrystals while shifting the secondary Fe-B crystallization to higher temperatures resulting in pure soft magnetic nanocrystallization with very low coercivities of ∼10 A/