The author analyses the structure of the predicative expression, i.e. a mor-phosyntactic construction functioning as the so-called constitutive predicate of the proposition in question. She illustrates her analysis with examples from Polish and from Macedonian – two languages whose grammatical systems are maximally typologicaly opposed in the frame of the Slavic linguistic group. She states that the predicative expression is composed of 1) constitutive form of verbum finitum and 2) its adjuncts formalized as adverbia and/or adverbialia responding to the questions: when? how long? how? in which mood? in which way? and carrying supplementary information from the semantic domains of the grammatical categories characteristic of the finite verb, such as ‘time’, ‘aspect’, or ‘mood’; most numerous are those informing (a) on the location of the referred to event on the time-axis or (b) on the procedure leading to the realization of that event.

In search of the invariant semantics of the preposition “da”: a cognitive analysis of the predicative context – The purpose of this article is to verify whether the semantic invariant of the preposition da [starting point allowing physical or mental movement] in the nominal context remains valid in the context of the verb. The analysis of the content of predicates that link to the preposition da will help to answer the question of the extent to which the choice of a preposition is determined by the knowledge of the experienced activities and/or the predicate itself (its selective features) or if it is the result of convention.

To investigate the effect of different proximate index on minimum ignition temperature(MIT) of coal dust cloud, 30 types of coal specimens with different characteristics were chosen. A two-furnace automatic coal proximate analyzer was employed to determine the indexes for moisture content, ash content, volatile matter, fixed carbon and MIT of different types of coal specimens. As the calculated results showed that these indexes exhibited high correlation, a principal component analysis (PCA) was adopted to extract principal components for multiple factors affecting MIT of coal dust, and then, the effect of the indexes for each type of coal on MIT of coal dust was analyzed. Based on experimental data, support vector machine (SVM) regression model was constructed to predicate the MIT of coal dust, having a predicating error below 10%. This method can be applied in the predication of the MIT for coal dust, which is beneficial to the assessment of the risk induced by coal dust explosion (CDE).