In this paper we present the first occurrence of the Diabrotica virgifera Le Conte in Poland and the measures taken against this pest. The specimens of western corn rootworm were found in South-Eastern Poland (Podkarpackie voivodeship) at the end of August and in September of 2005.
General meteorological conditions in the Admiralty Bay in 1978 did not deviate from those of many years. The data for 1978 were used to analyse the co-occurrence of four most perceptible meteorological parameters: temperature, air humidity, wind speed and precipitation. In summer these elements occurred simultaneously only in 1 — 2 intervals of values, in winter their occurrence within individual intervals was less numerous, but covered more of them.
Knowledge of the way in which minor and trace elements occur in coal is one of the most important geochemical indicators of coal quality. The differences between the methods of binding elements in coal in each coal seam and the variability of this feature of coal in the basin profile have not been discussed so far. These coal features were identified in a group of selected coal seams (209, 401, 405, 407, 501, 504, 510, 615, 620) in the Upper Silesian Coal Basin (USCB). At the same time, the differences in the role of identified mineral and maceral groups in concentrating specific elements in coal is highlighted. Identical or similar tendencies of changes in the way in which As and V, Ba and Rb, Co and Pb, Co and Zn, Mn and Pb, Pb and Zn, Co and Rb, and for Cr and Cu occur in the coal seams in the USCB profile was found. Changes in the mode of occurrence of As and Pb in coal in the USCB profile were probably influenced by carbonate mineralization. The changes in the mode of occurrence of Mni and Pb in the coal were probably determined by dia and epigenetic sulfide mineralization, while the content of Ba, Cr, Rb, Sr, and V in coal from these deposits was affected by clay minerals. It was observed that the greater the degree of the carbonization of the organic matter of coal, the lower the content of As, Mn and Pb in coal and the higher the content of Ba and Sr in coal.
In the paper the issue connected with water network failure regarding the soil conditions was presented. Water pipes constitute a large part of water company asset. Therefore the analysis concerning the influence of soil conditions into failure occurrence of water pipe is crucial for proper functioning of water supply systems (WSS). In the performed studies the real data from the operation of the exemplary WSS was obtained. The following properties of the ground conditions were taken into consideration among others: the chemical composition and ground phase, based on analysis performed through the following equipment, as the electron microscope with X-ray spectrometer detector and backscattered electrons (BSE) using the powder Debye-Sherrer’s method and X-ray diffractometer. The analysis indicate dependence between soil conditions and corrosivity occurrence, what indicate the importance of performed analysis.
The petrographic composition of coal has a significant impact on its technological and sorption properties. That composition is most frequently determined by means of microscope quantitative analyses. Thus, aside from the purely scientific aspect, such measurements have an important practical application in the industrial usage of coal, as well as in issues related to the safety in underground mining facilities. The article discusses research aiming at analyzing the usefulness of selected parameters of a digital image description in the process of automatic identification of macerals of the inertinite group using neural networks. The description of the investigated images was based on statistical parameters determined on the basis of a histogram and co-occurrence matrix (Haralick parameters). Each of the studied macerals was described by means of a 20-element feature vector. An analysis of its principal components (PCA) was conducted, along with establishing the relationship between the number of the applied components and the effectiveness of the MLP network. Based on that, the optimum number of input variables for the investigated classification task was chosen, which resulted in reduction of the size of the network’s hidden layer. As part of the discussed research, the authors also analyzed the process of classification of macerals of the inertinite group using an algorithm based on a group of MLP networks, where each network possessed one output. As a result, average recognition effectiveness of 80.9% was obtained for a single MLP network, and of 93.6% for a group of neural networks. The obtained results indicate that it is possible to use the proposed methodology as a tool supporting microscopic analyses of coal.