Fine-grained structural steel of the S690QL designation is widely used due to its very good mechanical properties and good weldability and bending, resistance to abrasion and corrosion. It is most often made in the form of plates of different thicknesses. S690QL steel plate has excellent performance characteristics and is used when low temperature resistance and important strength components are required. It is used in addition to applications for construction machines and structures, mining machines and in the energy sector, highways, railway bridges, etc. Since this steel is used in very demanding conditions such as low temperature, it is very important to test the mechanical properties of the impact toughness at these temperatures. Toughness is an important property that can indicate a material's tendency towards brittle fracture. In this work, a large number of samples were tested for this property, i.e. impact toughness at a temperature of -40°C taken from plates of different thicknesses made of S690QL steel. In addition to this property, the grain size of this steel after heat treatment is also taken into account. These characteristics and their mutual dependence provide directions for correcting the production of semi-finished products or finished products.

<p>U ovom radu ćemo pomoću planarnih grafova napraviti klasifikaciju pravilnih poledara i vidjeti&nbsp;<br />jo&scaron; neke mogućnosti primjene grafova u geometriji.</p>

This paper describes an R package LeArEst that can be used for estimating object dimensions from a noisy image. The package is based on a simple parametric model for data that are drawn from uniform distribution contaminated by an additive error. Our package is able to estimate the length of the object of interest on a given straight line that intersects it, as well as to estimate the object area when it is elliptically shaped. The input data may be a numerical vector or an image in JPEG format. In this paper, background statistical models and methods for the package are summarized, and the algorithms and key functions implemented are described. Also, examples that demonstrate its usage are provided. Availability: LeArEst is available on CRAN.