The academic language has certain features that do not occur in typical informal interaction about everyday things. The texts studied and produced in academic disciplines have different functions, and are structured in different ways. The linguistic features play an important role in the realization of different types of meanings. Some are important for their role in the expression of content (e.g. types of lexis, prepositional phrases or markers of logical relations between clauses). Others are involved in the role of the writer (e.g. informing, questioning or evaluating) or the organization of the content in the text.

The following paper provides an outline of the research on Academic Key Words studied in the texts of university students taken from the written corpora: the International Corpus of Learner English (the Polish and Turkish component of ICLE). Starting with a brief insight into the features of academic language, the article focuses on the analysis of chosen academic nouns, nouns, adjectives and adverbs as well as some basic clauses used by the Turkish and Polish university students of English as a Foreign language.

In this article, we propose a data-driven method for short-circuit fault detection in transmission lines that exploits the capabilities of convolutional neural networks (CNNs). CNNs, a class of deep feedforward neural networks, can autonomously detect different features from data, eliminating the need for manual intervention. To mitigate the effects of noise and increase network robustness, we present a CNN architecture with six convolutional layers. The study uses a single busbar power system model developed with the PSCAD simulation program to evaluate the performance of the proposed method. The proposed CNN method is also compared with machine learning methods such as LSTM, SVM and ELM. Our results show a high success rate of 98.4% across all fault impedances, confirming the effectiveness of the proposed CNN methods in accurately detecting short-circuit faults based on current and voltage measurements.