In this interpretation of Juliusz Słowacki's ‘Snycerz był zatrudniony Dyjanny lepieniem…’ [The carver was busy shaping Diana's statue] the discussion focuses on his attitude to matter, especially as the material of art. The article argues that Słowacki elevates and even sacralises mud, the most lowly of raw materials, and thus exposes the falseness of the popular view that he despises matter, the base opposite of the spirit. However, it would be more accurate to say that in his vision, which is part of his Genesis from the Spirit philosophy, the path to salvation leads through the reconciliation of spirit and matter rather than a triumph of one over the other.

The prediction of rock cuttability to produce the lignite deposits in underground mining is important in excavation. Moreover, the certain geographic locations of rock masses for cuttability tests are also significant to apply and compare the rock cuttability parameters. In this study, sediment samples of two boreholes (Hole-1 and Hole-2) from the Sagdere Formation (Denizli Molasse Basin) were applied to find out the cerchar abrasivity index (CAI), rock quality designations (RQD), uniaxial compressive strengths, Brazilian tensile strengths and Shore hardnesses. The Sagdere Formation deposited in the terrestrial to shallow marine conditions consists mainly of conglomerates, sandstones, shales, lignites as well as reefal limestones coarse to fine grained. A dataset from the fine grained sediments (a part of the Sagdere Formation) have been created using rock parameters mentioned in the study. Dataset obtained were utilized to construct the best fitted statistical model for predicting CAI on the basis of multiple regression technique. Additionally, the relationships among the rock parameters were evaluated by fuzzy logic inference system whether the rock parameters used in the study can be correlated or not. When comparing the two statistical techniques, multiple regression method is more accurate and reliable than fuzzy logic inference method for the dataset in this study. Furthermore, CAI can be predicted by using UCS, BTS, SH and RQD values based on this study.