The aim of this study was to examine the changes in the chemical composition of shallow groundwater and its quality that have occurred in the last decade in an agriculturally used, heavily populated and characterized by a complex geological structure, catchment of the Stara Rzeka river, located in the flysch part of the Outer Carpathians. Water samples were collected during 2013 from 19 still operating wells. Analyses of pH, electrolytic conductivity and chemical composition by ion chromatography were conducted. The obtained results were compared with the results of studies conducted in 2003 for the same wells. The quality of groundwater and its suitability for consumption was assessed based on the regulations currently existing in Poland. 21% of the wells still do not meet the requirements for drinking water in terms of at least one component. However, there was a decrease in the concentration of mineral forms of nitrogen and phosphorus in most of the wells and their mean concentration as compared to 2003 was reduced. In terms of physical and chemical characteristics groundwater of this region is typical of the hypergenic zone of the temperate climate. The highest concentrations were observed for Ca²⁺ and HCO₃^- ions, while K⁺ and Cl^- were characterized by the largest variability. Principal Component Analysis (PCA) demonstrated that the factors determining the quality and chemical composition of the analyzed waters include the composition of bedrock (mineralogy of the rock environment) and human economic activity, and that they have not been significantly changed over the past decade.

The results of statistical analysis applied in order to evaluate the effect of the high melting point elements to pressure die cast silumin on its tensile strength Rm, unit elongation A and HB were discussed. The base alloy was silumin with the chemical composition similar to ENAC 46000. To this silumin, high melting point elements such as Cr, Mo, V and W were added. All possible combinations of the additives were used. The content of individual high melting point additives ranged from 0.05 to 0.50%. The tests were carried out on silumin with and without above mentioned elements. The values of Rm, A and HB were determined for all the examined chemical compositions of the silumin. The conducted statistical analysis showed that each of the examined high melting point additives added to the silumin in an appropriate amount could raise the values of Rm, A and HB. To obtain the high tensile strength of Rm = 291 MPa in the tested silumin, the best content of each of the additives should be in the range of 0.05-0.10%. To obtain the highest possible elongation A of about 6.0%, the best content of the additives should be as follows: chromium in the range of 0.05-0.15%, molybdenum 0.05% or 0.15%, vanadium 0.05% and tungsten 0.15%. To obtain the silumin with hardness of 117 HB, chromium, molybdenum and vanadium content should be equal to about 0.05%, and tungsten to about 0.5%.

The dynamic development of wind power in recent years has generated the demand for production forecasting tools in wind farms. The data obtained from mathematical models is useful both for wind farm owners and distribution and transmission system operators. The predictions of production allow the wind farm operator to control the operation of the turbine in real time or plan future repairs and maintenance work in the long run. In turn, the results of the forecasting model allow the transmission system operator to plan the operation of the power system and to decide whether to reduce the load of conventional power plants or to start the reserve units.

The presented article is a review of the currently applied methods of wind power generation forecasting. Due to the nature of the input data, physical and statistical methods are distinguished. The physical approach is based on the use of data related to atmospheric conditions, terrain, and wind farm characteristics. It is usually based on numerical weather prediction models (NWP). In turn, the statistical approach uses historical data sets to determine the dependence of output variables on input parameters. However, the most favorable, from the point of view of the quality of the results, are models that use hybrid approaches. Determining the best model turns out to be a complicated task, because its usefulness depends on many factors. The applied model may be highly accurate under given conditions, but it may be completely unsuitable for another wind farm.

The paper presents the results of the application of a statistical analysis to evaluate the effect of the chemical composition of the die casting Al-Si alloys on its basic mechanical properties. The examinations were performed on the hypoeutectic Al-Si alloy type EN AC-46000 and, created on its basis, a multi-component Al-Si alloy containing high-melting additions Cr, Mo, W and V. The additions were introduced into the base Al-Si alloy in different combinations and amounts (from 0,05% to 0,50%). The tensile strength Rm; the proof stress Rp0,2; the unit elongation A and the hardness HB of the examined Al-Si alloys were determined. The data analysis and the selection of Al-Si alloy samples without the Cr, Mo, W and V additions were presented; a database containing the independent variables (Al-Si alloy's chemical composition) and dependent variables (Rm; Rp0,2; A and HB) for all the considered variants of Al-Si alloy composition was constructed. Additionally, an analysis was made of the effect of the Al-Si alloy's component elements on the obtained mechanical properties, with a special consideration of the high-melting additions Cr, Mo, V and W. For the optimization of the content of these additions in the Al-Si alloy, the dependent variables were standardized and treated jointly. The statistical tools were mainly the multivariate backward stepwise regression and linear correlation analysis and the analysis of variance ANOVA. The statistical analysis showed that the most advantageous effect on the jointly treated mechanical properties is obtained with the amount of the Cr, Mo, V and W additions of 0,05 to 0,10%.

Conducting reliable and credible evaluation and statistical interpretation of empirical results related to the operation of production systems

in foundries is for most managers complicated and labour-intensive. Additionally, in many cases, statistical evaluation is either ignored

and considered a necessary evil, or is completely useless because of improper selection of methods and subsequent misinterpretation of the

results. In this article, after discussing the key elements necessary for the proper selection of statistical methods, a wide spectrum of these

methods has been presented, including regression analysis, uni- and multivariate correlation, one-way analysis of variance for factorial

designs, and selected forecasting methods. Each statistical method has been illustrated with numerous examples related to the foundry

practice.

To achieve better precision of features generated using the micro-electrical discharge machining (micro-EDM), there is a necessity to minimize the wear of the tool electrode, because a change in the dimensions of the electrode is reflected directly or indirectly on the feature. This paper presents a novel modeling and analysis approach of the tool wear in micro-EDM using a systematic statistical method exemplifying the influences of capacitance, feed rate and voltage on the tool wear ratio. The association between tool wear ratio and the input factors is comprehended by using main effect plots, interaction effects and regression analysis. A maximum variation of four-fold in the tool wear ratio have been observed which indicated that the tool wear ratio varies significantly over the trials. As the capacitance increases from 1 to 10 nF, the increase in tool wear ratio is by 33%. An increase in voltage as well as capacitance would lead to an increase in the number of charged particles, the number of collisions among them, which further enhances the transfer of the proportion of heat energy to the tool surface. Furthermore, to model the tool wear phenomenon, a egression relationship between tool wear ratio and the process inputs has been developed.