78 research outputs found
ΠΠ½Π°Π»ΠΈΠ· ΡΡΠΈΠ»Ρ ΡΡΡΡΠΊΠΎΡΠ·ΡΡΠ½ΡΡ ΡΠ΅ΠΊΡΡΠΎΠ² Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠΈΡΠΌΠΈΡΠ΅ΡΠΊΠΈΡ Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ
Π ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΡ ΡΡΡΡΠΊΠΎΡΠ·ΡΡΠ½ΡΡ
ΡΠ΅ΠΊΡΡΠΎΠ² ΡΠ°Π·Π½ΡΡ
ΠΆΠ°Π½ΡΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΈΡΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ², ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΡ
Π½Π° ΠΏΠΎΠ²ΡΠΎΡΠ΅Π½ΠΈΠΈ ΡΠ»ΠΎΠ² ΠΈ ΡΡΠ°Π·. ΠΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²Π΅ΠΊΡΠΎΡΠΎΠ² ΠΈ Π²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° umap
ΠΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΡΡΡΠΊΠΎΡΠ·ΡΡΠ½ΡΡ ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΡΠ΅ΠΊΡΡΠΎΠ² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ ΠΆΠ°Π½ΡΠΎΠ²
Π ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΡΡΡΠΊΠΎΡΠ·ΡΡΠ½ΡΡ
ΡΠ΅ΠΊΡΡΠΎΠ² ΠΏΠΎ ΠΆΠ°Π½ΡΠ°ΠΌ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ. Π’Π΅ΠΊΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΡΠ΅ΡΡΡ Π²Π΅ΠΊΡΠΎΡΠΎΠΌ ΡΠΈΡΠ»ΠΎΠ²ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΡΠΎΠ²Π½Ρ ΡΠΈΠΌΠ²ΠΎΠ»ΠΎΠ², ΡΠ»ΠΎΠ² ΠΈ ΡΠΈΡΠΌΠ°. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ Π½Π΅ΠΉΡΠΎΠ½Π½Π°Ρ ΡΠ΅ΡΡBi-LSTM. ΠΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΠΆΠ°Π½ΡΠΎΠ² ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ Π²ΡΡΠΎΠΊΠΎΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°ΡΠΈ ΡF-ΠΌΠ΅ΡΠΎΠΉ ΠΎΡ 90 % Π΄ΠΎ98 %
ΠΠ΅ΡΠΎΠ΄ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π°ΡΠΏΠ΅ΠΊΡΡ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ Π΄Π»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ ΡΠ΅Π·Π°ΡΡΡΡΠΎΠ² ΠΈ ΠΈΡ ΠΎΡΠ΅Π½ΠΊΠΈ
The paper is devoted to analysis of methods for automatic generation of a specializedΒ thesaurus. The main algorithm of generation consists of three stages: selection and preprocessing ofΒ a text corpus, recognition of thesaurus terms, and extraction of relations among terms. Our work isΒ focused on exploring methods for semantic relation extraction. We developed a test bench that allow toΒ test well-known algorithms for extraction of synonyms and hypernyms. These algorithms are based onΒ diο¬erent relation extraction techniques: lexico-syntactic patterns, morpho-syntactic rules, measurementΒ of term information quantity, general-purpose thesaurus WordNet, and Levenstein distance. For analysisΒ of the result thesaurus we proposed a complex assessment that includes the following metrics: precisionΒ of extracted terms, precision and recall of hierarchical and synonym relations, and characteristics of theΒ thesaurus graph (the number of extracted terms and semantic relationships of diο¬erent types, the numberΒ of connected components, and the number of vertices in the largest component). The proposed set ofΒ metrics allows to evaluate the quality of the thesaurus as a whole, reveal some drawbacks of standardΒ relation extraction methods, and create more eο¬cient hybrid methods that can generate thesauri withΒ better characteristics than thesauri generated by using separate methods. In order to illustrate this fact,Β one of such hybrid methods is considered in the paper. It combines the best standard algorithms forΒ hypernym and synonym extraction and generates a specialized medical thesaurus. The hybrid methodΒ leaves the thesaurus quality on the same level and ο¬nds more relations between terms than well-knownΒ algorithms.Π Π°Π±ΠΎΡΠ° ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° Π°Π½Π°Π»ΠΈΠ·Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ΅Π·Π°ΡΡΡΡΠ°. ΠΡΠ½ΠΎΠ²Π½ΠΎΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΡΠΎΡΡΠΎΠΈΡ ΠΈΠ· ΡΡΠ΅Ρ
ΡΠ°Π³ΠΎΠ²: ΠΎΡΠ±ΠΎΡ ΠΈ ΠΏΡΠ΅Π΄Π²Π°ΡΠΈΡΠ΅Π»ΡΠ½Π°Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ° ΠΊΠΎΡΠΏΡΡΠ° ΡΠ΅ΠΊΡΡΠΎΠ², ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΡΠ΅ΡΠΌΠΈΠ½ΠΎΠ² Π΄Π»Ρ Π²ΠΊΠ»ΡΡΠ΅Π½ΠΈΡ Π² ΡΠ΅Π·Π°ΡΡΡΡ ΠΈΒ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΠ΅ ΡΠ²ΡΠ·Π΅ΠΉ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠ΅ΡΠΌΠΈΠ½Π°ΠΌΠΈ ΡΠ΅Π·Π°ΡΡΡΡΠ°. ΠΠ°Π½Π½ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠΎΠΊΡΡΠΈΡΠΎΠ²Π°Π½ΠΎ Π½Π° ΠΈΠ·ΡΡΠ΅Π½ΠΈΠΈΒ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²ΡΠ·Π΅ΠΉ, Π΄Π»Ρ ΡΠ΅Π³ΠΎ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ Π±ΡΠ» ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΡΠΉ ΡΡΠ΅Π½Π΄,Β ΠΊΠΎΡΠΎΡΡΠΉ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΡΠΎΡΠ΅ΡΡΠΈΡΠΎΠ²Π°ΡΡ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ Π³ΠΈΠΏΠ΅ΡΠΎΠ½ΠΈΠΌΠΎΠ² ΠΈ ΡΠΈΠ½ΠΎΠ½ΠΈΠΌΠΎΠ², ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΠΈΠ΅ Π² ΡΠ²ΠΎΠ΅ΠΉ ΡΠ°Π±ΠΎΡΠ΅ Π»Π΅ΠΊΡΠΈΠΊΠΎ-ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ°Π±Π»ΠΎΠ½Ρ, ΠΌΠΎΡΡΠΎ-ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠ΅Β ΠΏΡΠ°Π²ΠΈΠ»Π°, ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΡΠ΅ΡΠΌΠΈΠ½ΠΎΠ², ΡΠ΅Π·Π°ΡΡΡΡ ΠΎΠ±ΡΠ΅Π³ΠΎ Π½Π°Π·Π½Π°ΡΠ΅Π½ΠΈΡ WordNet ΠΈ ΡΠ°ΡΡΡΠΎΡΠ½ΠΈΠ΅Β ΠΠ΅Π²Π΅Π½ΡΡΠ΅ΠΉΠ½Π°. ΠΠ»Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ΅Π·ΡΠ»ΡΡΠΈΡΡΡΡΠ΅Π³ΠΎ ΡΠ΅Π·Π°ΡΡΡΡΠ°, ΡΠΎΠ·Π΄Π°Π½Π½ΠΎΠ³ΠΎ Π½Π° ΡΡΠ΅Π½Π΄Π΅, Π°Π²ΡΠΎΡΠ°ΠΌΠΈ Π±ΡΠ»Π° ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ°, ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠ°Ρ ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π°: ΡΠΎΡΠ½ΠΎΡΡΡ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅ΡΠΌΠΈΠ½ΠΎΠ², ΡΠΎΡΠ½ΠΎΡΡΡ ΠΈ ΠΏΠΎΠ»Π½ΠΎΡΠ° Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΡΠΈΠ½ΠΎΠ½ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ Π³ΠΈΠΏΠ΅ΡΠΎΠ½ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²ΡΠ·Π΅ΠΉ, Π°Β ΡΠ°ΠΊΠΆΠ΅ ΠΌΠ΅ΡΡΠΈΠΊΠΈ Π³ΡΠ°ΡΠ° ΡΠ΅Π·Π°ΡΡΡΡΠ° (ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ
ΡΠ΅ΡΠΌΠΈΠ½ΠΎΠ², ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Β ΡΠ²ΡΠ·Π΅ΠΉ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠΈΠΏΠΎΠ², ΡΠΈΡΠ»ΠΎ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ ΡΠ²ΡΠ·Π½ΠΎΡΡΠΈ ΠΈ ΡΠΈΡΠ»ΠΎ Π²Π΅ΡΡΠΈΠ½ Π² Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΠ΅ΠΉ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ΅).Β ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΡΠΉ Π½Π°Π±ΠΎΡ ΠΌΠ΅ΡΡΠΈΠΊ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΎΡΠ΅Π½ΠΈΡΡ ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ ΡΠ΅Π·Π°ΡΡΡΡΠ° Π² ΡΠ΅Π»ΠΎΠΌ, Π²ΡΡΠ²ΠΈΡΡ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΠ΅Β Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΊΠΈ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ²ΡΠ·Π΅ΠΉ ΠΈ ΠΏΠΎΡΡΡΠΎΠΈΡΡ Π±ΠΎΠ»Π΅Π΅ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠ΅ Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΠ΅Β ΠΌΠ΅ΡΠΎΠ΄Ρ, Π³Π΅Π½Π΅ΡΠΈΡΡΡΡΠΈΠ΅ ΡΠ΅Π·Π°ΡΡΡΡ Ρ Π»ΡΡΡΠΈΠΌΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌΠΈ ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΡΠ΅Π·Π°ΡΡΡΡΠ°ΠΌΠΈ, Π³Π΅Π½Π΅ΡΠΈΡΡΠ΅ΠΌΡΠΌΠΈ ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ². ΠΠ»Ρ ΠΈΠ»Π»ΡΡΡΡΠ°ΡΠΈΠΈ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ°ΠΊΡΠ° Π² ΡΡΠ°ΡΡΠ΅Β ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ ΠΎΠ΄ΠΈΠ½ ΠΈΠ· ΡΠ°ΠΊΠΈΡ
Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ². ΠΠ½ ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡΡΠ΅Ρ Π»ΡΡΡΠΈΠ΅ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΡΒ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ Π³ΠΈΠΏΠ΅ΡΠΎΠ½ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠΈΠ½ΠΎΠ½ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²ΡΠ·Π΅ΠΉ ΠΈ ΡΡΡΠΎΠΈΡ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉ ΡΠ΅Π·Π°ΡΡΡΡ Π²Β ΠΎΠ±Π»Π°ΡΡΠΈ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ Ρ ΡΠ΅ΠΌ ΠΆΠ΅ ΡΡΠΎΠ²Π½Π΅ΠΌ ΠΊΠ°ΡΠ΅ΡΡΠ²Π°, ΡΡΠΎ ΠΈ Π΄ΡΡΠ³ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ, Π½ΠΎ Ρ Π±ΠΎΠ»ΡΡΠΈΠΌ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎΠΌΒ ΡΠ²ΡΠ·Π΅ΠΉ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠ΅ΡΠΌΠΈΠ½Π°ΠΌΠΈ
ΠΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΠ΅ΠΊΡΡΠΎΠ² ΠΏΠΎ ΡΡΠΎΠ²Π½ΡΠΌ CEFR Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΈ ΡΠ·ΡΠΊΠΎΠ²ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ BERT
This paper presents a study of the problem of automatic classification of short coherent texts (essays) in English according to the levels of the international CEFR scale. Determining the level of text in natural language is an important component of assessing students knowledge, including checking open tasks in e-learning systems. To solve this problem, vector text models were considered based on stylometric numerical features of the character, word, sentence structure levels. The classification of the obtained vectors was carried out by standard machine learning classifiers. The article presents the results of the three most successful ones: Support Vector Classifier, Stochastic Gradient Descent Classifier, LogisticRegression. Precision, recall and F-score served as quality measures. Two open text corpora, CEFR Levelled English Texts and BEA-2019, were chosen for the experiments. The best classification results for six CEFR levels and sublevels from A1 to C2 were shown by the Support Vector Classifier with F-score 67 % for the CEFR Levelled English Texts. This approach was compared with the application of the BERT language model (six different variants). The best model, bert-base-cased, provided the F-score value of 69 %. The analysis of classification errors showed that most of them are between neighboring levels, which is quite understandable from the point of view of the domain. In addition, the quality of classification strongly depended on the text corpus, that demonstrated a significant difference in F-scores during application of the same text models for different corpora. In general, the obtained results showed the effectiveness of automatic text level detection and the possibility of its practical application.Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π·Π°Π΄Π°ΡΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΊΠΎΡΠΎΡΠΊΠΈΡ
ΡΠ²ΡΠ·Π½ΡΡ
ΡΠ΅ΠΊΡΡΠΎΠ² (ΡΡΡΠ΅) Π½Π° Π°Π½Π³Π»ΠΈΠΉΡΠΊΠΎΠΌ ΡΠ·ΡΠΊΠ΅ ΠΏΠΎ ΡΡΠΎΠ²Π½ΡΠΌ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΉ ΡΠΊΠ°Π»Ρ CEFR. ΠΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΡΡΠΎΠ²Π½Ρ ΡΠ΅ΠΊΡΡΠ° Π½Π° Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΌ ΡΠ·ΡΠΊΠ΅ ΡΠ²Π»ΡΠ΅ΡΡΡ Π²Π°ΠΆΠ½ΠΎΠΉ ΡΠΎΡΡΠ°Π²Π»ΡΡΡΠ΅ΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ Π·Π½Π°Π½ΠΈΠΉ ΡΡΠ°ΡΠΈΡ
ΡΡ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅ΡΠΊΠΈ ΠΎΡΠΊΡΡΡΡΡ
Π·Π°Π΄Π°Π½ΠΈΠΉ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΠ»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΡΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ Π±ΡΠ»ΠΈ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ Π²Π΅ΠΊΡΠΎΡΠ½ΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΠ΅ΠΊΡΡΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΡΠΈΠ»ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΠ»ΠΎΠ²ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΡΠΎΠ²Π½Ρ ΡΠΈΠΌΠ²ΠΎΠ»ΠΎΠ², ΡΠ»ΠΎΠ², ΡΡΡΡΠΊΡΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΡ. ΠΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
Π²Π΅ΠΊΡΠΎΡΠΎΠ² ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ»Π°ΡΡ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΠΌΠΈ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ°ΠΌΠΈ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΡΡΡ
Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΠΏΠ΅ΡΠ½ΡΡ
: Support Vector Classifier, Stochastic Gradient Descent Classifier, LogisticRegression. ΠΡΠ΅Π½ΠΊΠΎΠΉ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΏΠΎΡΠ»ΡΠΆΠΈΠ»ΠΈ ΡΠΎΡΠ½ΠΎΡΡΡ, ΠΏΠΎΠ»Π½ΠΎΡΠ° ΠΈ F"=ΠΌΠ΅ΡΠ°. ΠΠ»Ρ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ² Π±ΡΠ»ΠΈ Π²ΡΠ±ΡΠ°Π½Ρ Π΄Π²Π° ΠΎΡΠΊΡΡΡΡΡ
ΠΊΠΎΡΠΏΡΡΠ° ΡΠ΅ΠΊΡΡΠΎΠ² CEFR Levelled English Texts ΠΈ BEA"=2019. ΠΡΡΡΠΈΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠΎ ΡΠ΅ΡΡΠΈ ΡΡΠΎΠ²Π½ΡΠΌ ΠΈ ΠΏΠΎΠ΄ΡΡΠΎΠ²Π½ΡΠΌ CEFR ΠΎΡ A1 Π΄ΠΎ C2 ΠΏΠΎΠΊΠ°Π·Π°Π» Support Vector Classifier Ρ F"=ΠΌΠ΅ΡΠΎΠΉ 67 % Π΄Π»Ρ ΠΊΠΎΡΠΏΡΡΠ° CEFR Levelled English Texts. ΠΡΠΎΡ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΡΡΠ°Π²Π½ΠΈΠ²Π°Π»ΡΡ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ·ΡΠΊΠΎΠ²ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ BERT (ΡΠ΅ΡΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Π²Π°ΡΠΈΠ°Π½ΡΠΎΠ²). ΠΡΡΡΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ bert"=base"=cased ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ»Π° Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ F"=ΠΌΠ΅ΡΡ 69 %. ΠΠ½Π°Π»ΠΈΠ· ΠΎΡΠΈΠ±ΠΎΠΊ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠΎΠΊΠ°Π·Π°Π», ΡΡΠΎ Π±ΠΎΠ»ΡΡΠ°Ρ ΠΈΡ
ΡΠ°ΡΡΡ Π΄ΠΎΠΏΡΡΠ΅Π½Π° ΠΌΠ΅ΠΆΠ΄Ρ ΡΠΎΡΠ΅Π΄Π½ΠΈΠΌΠΈ ΡΡΠΎΠ²Π½ΡΠΌΠΈ, ΡΡΠΎ Π²ΠΏΠΎΠ»Π½Π΅ ΠΎΠ±ΡΡΡΠ½ΠΈΠΌΠΎ Ρ ΡΠΎΡΠΊΠΈ Π·ΡΠ΅Π½ΠΈΡ ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠ½ΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠΈΠ»ΡΠ½ΠΎ Π·Π°Π²ΠΈΡΠ΅Π»ΠΎ ΠΎΡ ΠΊΠΎΡΠΏΡΡΠ° ΡΠ΅ΠΊΡΡΠΎΠ², ΡΡΠΎ ΠΏΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°Π»ΠΎ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΠ΅ F"=ΠΌΠ΅ΡΡ Π² Ρ
ΠΎΠ΄Π΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΎΠ΄ΠΈΠ½Π°ΠΊΠΎΠ²ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠ΅ΠΊΡΡΠ° Π΄Π»Ρ ΡΠ°Π·Π½ΡΡ
ΠΊΠΎΡΠΏΡΡΠΎΠ². Π ΡΠ΅Π»ΠΎΠΌ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΡΠΎΠ²Π½Ρ ΡΠ΅ΠΊΡΡΠ° ΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ Π΅Π³ΠΎ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ
ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΠΌΠ΅Ρ Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π΄ΠΈΡΡΠ°Π½ΡΠ½ΠΎΠ³ΠΎ ΡΡΠΈΠΌΡΠ»ΠΈΡΡΡΡΠ΅Π³ΠΎ ΡΡΡΠ΅ΠΊΡΠ° Π°ΡΡΠΎΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ ΠΊΠΎΠΆΠ½ΠΎΠ³ΠΎ Π»ΠΎΡΠΊΡΡΠ° Π½Π° ΠΏΠ΅ΡΡΡΠ·ΠΈΡ ΠΌΠΈΠΊΡΠΎΡΠΈΡΠΊΡΠ»ΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΡΡΠ»Π° Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ½ΡΡ Π½Π°ΡΡΡΠ΅Π½ΠΈΠΉ
Objective: to study the characteristics of mechanisms of the distant stimulating effect of full-thickness skinautograft (FTSG) on microvascular perfusion in local and systemic microcirculation disorders. Materials andΒ methods. The experiment was carried out on 87 white male rats, divided into 5 groups: 1) control; 2) animals withΒ local microcirculation disorders induced by sciatic nerve transection and neuroraphy; 3) animals with systemicΒ microcirculation disorders caused by alloxan-induced diabetes; 4) animals that underwent FTSG after sciaticΒ nerve transection and neurography; 5) animals that underwent FTSG in alloxan-induced diabetes. Laser DopplerΒ flowmetry (LDF) was used to study microcirculation of the dorsal skin of the rear paw. Serum concentrations ofΒ vasoactive substances, including catecholamines (CA), histamine, and vasculoendothelial growth factor (VEGF)Β in the experimental animals were measured. A morphological study of the tissues of the autograft site was carriedΒ out on day 42 of the experiment. Results. On day 42 of the experiment, FTSG normalized perfusion in local andΒ systemic microcirculation disorders. FTSG decreases CA level in nerve injury, and to a greater extent in alloxaninduced diabetes. SerumΒ histamine increase under FTSG was more pronounced in rats with nerve injury. SerumΒ VEGF in rats with nerve injury and FTSG increased, which was not observed in alloxan-induced diabetes. Histological assay of theΒ autograft site revealed degenerative changes in the epidermis and dermis of the autotransplantΒ in both experimental models of microcirculatory disorders. Eosinophilic infiltration of the autograft site wasΒ more pronounced in nerve injury than in alloxan-induced diabetes. Conclusion. FTSG has a distant stimulatingΒ effect on microcirculation, which manifests itself in the same degree in both local and systemic microcirculationΒ disorders. The distant stimulating effect of FTSG on microcirculation is multicomponent in nature and includesΒ a set of regulatory reactions, whose severity differs in local and systemic microcirculatory disorders.Π¦Π΅Π»Ρ Π½Π°ΡΡΠΎΡΡΠ΅Π³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ β ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π΄ΠΈΡΡΠ°Π½ΡΠ½ΠΎΠ³ΠΎ ΡΡΠΈΠΌΡΠ»ΠΈΡΡΡΡΠ΅Π³ΠΎ Π΄Π΅ΠΉΡΡΠ²ΠΈΡ Π°ΡΡΠΎΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ ΠΏΠΎΠ»Π½ΠΎΡΠ»ΠΎΠΉΠ½ΠΎΠ³ΠΎ ΠΊΠΎΠΆΠ½ΠΎΠ³ΠΎ Π»ΠΎΡΠΊΡΡΠ° (ΠΠ’ΠΠΠ) Π½Π° ΠΏΠ΅ΡΡΡΠ·ΠΈΡ ΠΌΠΈΠΊΡΠΎΡΠΈΡΠΊΡΠ»ΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΡΡΠ»Π° ΠΏΡΠΈ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ
ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ½ΡΡ
ΠΌΠΈΠΊΡΠΎΡΠΈΡΠΊΡΠ»ΡΡΠΎΡΠ½ΡΡ
Π½Π°ΡΡΡΠ΅Π½ΠΈΡΡ
. ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ.Β ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ Π½Π° 87 Π±Π΅Π»ΡΡ
ΠΊΡΡΡΠ°Ρ
-ΡΠ°ΠΌΡΠ°Ρ
, ΡΠ°Π·Π΄Π΅Π»Π΅Π½Π½ΡΡ
Π½Π° 5 Π³ΡΡΠΏΠΏ: 1) ΠΊΠΎΠ½ΡΡΠΎΠ»ΡΠ½Π°Ρ; 2) ΠΆΠΈΠ²ΠΎΡΠ½ΡΠ΅ ΡΒ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΠΌΠΈ Π½Π°ΡΡΡΠ΅Π½ΠΈΡΠΌΠΈ ΠΌΠΈΠΊΡΠΎΡΠΈΡΠΊΡΠ»ΡΡΠΈΠΈ, Π²ΡΠ·Π²Π°Π½Π½ΡΠΌΠΈ ΠΏΠ΅ΡΠ΅ΡΠ΅Π·ΠΊΠΎΠΉ ΠΈ Π½Π΅ΠΉΡΠΎΡΠ°ΡΠΈΠ΅ΠΉ ΡΠ΅Π΄Π°Π»ΠΈΡΠ½ΠΎΠ³ΠΎ Π½Π΅ΡΠ²Π°;Β 3) ΠΆΠΈΠ²ΠΎΡΠ½ΡΠ΅ Ρ ΡΠΈΡΡΠ΅ΠΌΠ½ΡΠΌΠΈ Π½Π°ΡΡΡΠ΅Π½ΠΈΡΠΌΠΈ ΠΌΠΈΠΊΡΠΎΡΠΈΡΠΊΡΠ»ΡΡΠΈΠΈ, Π²ΡΠ·Π²Π°Π½Π½ΡΠΌΠΈ Π°Π»Π»ΠΎΠΊΡΠ°Π½ΠΎΠ²ΡΠΌΒ Π΄ΠΈΠ°Π±Π΅ΡΠΎΠΌ; 4) ΠΆΠΈΠ²ΠΎΡΠ½ΡΠ΅, ΠΊΠΎΡΠΎΡΡΠΌ Π²ΠΌΠ΅ΡΡΠ΅ Ρ Π½Π΅ΠΉΡΠΎΡΠ°ΡΠΈΠ΅ΠΉ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π° ΠΠ’ΠΠΠ; 5) ΠΆΠΈΠ²ΠΎΡΠ½ΡΠ΅,Β ΠΊΠΎΡΠΎΡΡΠΌ Π½Π° ΡΠΎΠ½Π΅ Π°Π»Π»ΠΎΠΊΡΠ°Π½ΠΎΠ²ΠΎΠ³ΠΎΒ Π΄ΠΈΠ°Π±Π΅ΡΠ° ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π° ΠΠ’ΠΠΠ. ΠΠΈΠΊΡΠΎΡΠΈΡΠΊΡΠ»ΡΡΠΈΡ ΠΊΠΎΠΆΠΈ ΡΡΠ»ΡΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ ΡΡΠΎΠΏΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π»ΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌΒ Π»Π°Π·Π΅ΡΠ½ΠΎΠΉ Π΄ΠΎΠΏΠ»Π΅ΡΠΎΠ²ΡΠΊΠΎΠΉ ΡΠ»ΠΎΡΠΌΠ΅ΡΡΠΈΠΈ (ΠΠΠ€). Π ΠΊΡΠΎΠ²ΠΈ ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ
ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»ΠΈ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅Β Π²Π°Π·ΠΎΠ°ΠΊΡΠΈΠ²Π½ΡΡ
Π²Π΅ΡΠ΅ΡΡΠ², Π²ΠΊΠ»ΡΡΠ°Ρ ΠΊΠ°ΡΠ΅Ρ
ΠΎΠ»Π°ΠΌΠΈΠ½Ρ (ΠΠ), Π³ΠΈΡΡΠ°ΠΌΠΈΠ½ ΠΈΒ Π²Π°ΡΠΊΡΠ»ΠΎΡΠ½Π΄ΠΎΡΠ΅Π»ΠΈΠ°Π»ΡΠ½ΡΠΉ ΡΠ°ΠΊΡΠΎΡ ΡΠΎΡΡΠ° (VEGF). ΠΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈΒ ΠΌΠΎΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΊΠ°Π½Π΅ΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ Π°ΡΡΠΎΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ Π½Π° 42-Π΅ ΡΡΡΠΊΠΈ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠ’ΠΠΠ Π½Π° 42-Π΅ ΡΡΡΠΊΠΈ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ° ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ Π½ΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠ΅ΡΡΡΠ·ΠΈΠΈ ΠΏΡΠΈ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ
ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ½ΡΡ
Β Π½Π°ΡΡΡΠ΅Π½ΠΈΡΡ
ΠΌΠΈΠΊΡΠΎΡΠΈΡΠΊΡΠ»ΡΡΠΈΠΈ. ΠΠ’ΠΠΠ ΠΏΡΠΈ ΠΏΠΎΠ²ΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΠΈ Π½Π΅ΡΠ²Π°, ΠΈ Π² Π±ΠΎΠ»ΡΡΠ΅ΠΉ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ ΠΏΡΠΈ Π°Π»Π»ΠΎΠΊΡΠ°Π½ΠΎΠ²ΠΎΠΌΒ Π΄ΠΈΠ°Π±Π΅ΡΠ΅, ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΡ ΡΡΠΎΠ²Π½Ρ ΠΠ. ΠΠΎΠ΄ Π²Π»ΠΈΡΠ½ΠΈΠ΅ΠΌ ΠΠ’ΠΠΠ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ Π³ΠΈΡΡΠ°ΠΌΠΈΠ½Π° Π² ΠΊΡΠΎΠ²ΠΈ Π±ΠΎΠ»Π΅Π΅Β Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎ ΡΠ²Π΅Π»ΠΈΡΠΈΠ²Π°Π»Π°ΡΡ Ρ ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ
Ρ ΡΡΠ°Π²ΠΌΠΎΠΉ Π½Π΅ΡΠ²Π°. Π£ ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ
Ρ ΡΡΠ°Π²ΠΌΠΎΠΉ Π½Π΅ΡΠ²Π° ΠΠ’ΠΠΠΒ ΡΠ²Π΅Π»ΠΈΡΠΈΠ²Π°Π»Π°Β ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅ VEGF Π² ΠΊΡΠΎΠ²ΠΈ, ΡΠ΅Π³ΠΎ Π½Π΅ ΠΎΡΠΌΠ΅ΡΠ°Π»ΠΎΡΡ Ρ ΠΊΡΡΡ Ρ Π°Π»Π»ΠΎΠΊΡΠ°Π½ΠΎΠ²ΡΠΌ Π΄ΠΈΠ°Π±Π΅ΡΠΎΠΌ. ΠΠΎΡΡΠΎΠ»ΠΎΠ³ΠΈΡ Π·ΠΎΠ½Ρ Π°ΡΡΠΎΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π»Π°ΡΡ Π΄Π΅Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠ²Π½ΡΠΌΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡΠΌΠΈ ΡΠΏΠΈΠ΄Π΅ΡΠΌΠΈΡΠ° ΠΈ Π΄Π΅ΡΠΌΡ Π°ΡΡΠΎΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠ°Β Π²Π½Π΅ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΌΠΈΠΊΡΠΎΡΠΈΡΠΊΡΠ»ΡΡΠΎΡΠ½ΡΡ
Π½Π°ΡΡΡΠ΅Π½ΠΈΠΉ. ΠΠΎΠ½Π° Π°ΡΡΠΎΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠ° ΠΈΠ½ΡΠΈΠ»ΡΡΡΠΈΡΡΠ΅ΡΡΡΒ ΡΠΎΠ·ΠΈΠ½ΠΎΡΠΈΠ»Π°ΠΌΠΈ Π±ΠΎΠ»Π΅Π΅ Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎ ΠΏΡΠΈ ΡΡΠ°Π²ΠΌΠ΅ Π½Π΅ΡΠ²Π°, ΡΠ΅ΠΌ Ρ ΠΊΡΡΡ Ρ Π°Π»Π»ΠΎΠΊΡΠ°Π½ΠΎΠ²ΡΠΌ Π΄ΠΈΠ°Π±Π΅ΡΠΎΠΌ. ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅.Β ΠΠ’ΠΠΠ ΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ Π΄ΠΈΡΡΠ°Π½ΡΠ½ΠΎΠ΅ ΡΡΠΈΠΌΡΠ»ΠΈΡΡΡΡΠ΅Π΅ Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ Π½Π°Β ΠΌΠΈΠΊΡΠΎΡΠΈΡΠΊΡΠ»ΡΡΠΈΡ, ΠΊΠΎΡΠΎΡΠΎΠ΅ ΠΏΡΠΎΡΠ²Π»ΡΠ΅ΡΡΡ Π²Β ΠΎΠ΄ΠΈΠ½Π°ΠΊΠΎΠ²ΠΎΠΌ ΠΎΠ±ΡΠ΅ΠΌΠ΅ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΊΠ°ΠΊ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ
, ΡΠ°ΠΊ ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ½ΡΡ
Π½Π°ΡΡΡΠ΅Π½ΠΈΠΉ ΠΌΠΈΠΊΡΠΎΠΊΡΠΎΠ²ΠΎΡΠΎΠΊΠ°. ΠΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΒ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π΄ΠΈΡΡΠ°Π½ΡΠ½ΠΎΠ³ΠΎ ΡΡΠΈΠΌΡΠ»ΠΈΡΡΡΡΠ΅Π³ΠΎ Π΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΠ’ΠΠΠ Π½Π° ΠΌΠΈΠΊΡΠΎΡΠΈΡΠΊΡΠ»ΡΡΠΈΡ ΠΌΠ½ΠΎΠ³ΠΎΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ½ΡΠΉΒ Β ΠΈ Π²ΠΊΠ»ΡΡΠ°Π΅Ρ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡ ΡΠ΅Π³ΡΠ»ΡΡΠΎΡΠ½ΡΡ
ΡΠ΅Π°ΠΊΡΠΈΠΉ, ΡΡΠ΅ΠΏΠ΅Π½ΡΒ Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎΡΡΠΈ ΠΊΠΎΡΠΎΡΡΡ
Π½Π΅ΠΎΠ΄ΠΈΠ½Π°ΠΊΠΎΠ²Π° Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
Β Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ
ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ½ΡΡ
Π½Π°ΡΡΡΠ΅Π½ΠΈΠΉ ΠΌΠΈΠΊΡΠΎΠΊΡΠΎΠ²ΠΎΡΠΎΠΊΠ°
Π Π°Π·ΡΠ°Π±ΠΎΡΠΊΠ° Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ°ΡΡΠΎΡΡ ΠΏΡΠ»ΡΡΠ° ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΊΠ°ΠΌΠ΅ΡΡ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Π»Π΅ΡΠΎΠ½Π°
Nowadays there exist many different ways to measure a personβs heart rate. One of them assumes the usage of a mobile phone built-in camera. This method is easy to use and does not require any additional skills or special devices for heart rate measurement. It requires only a mobile cellphone with a built-in camera and a flash. The main idea of the method is to detect changes in finger skin color that occur due to blood pulsation. The measurement process is simple: the user covers the camera lens with a finger and the application on the mobile phone starts catching and analyzing frames from the camera. Heart rate can be calculated by analyzing average red component values of frames taken by the mobile cellphone camera that contain images of an area of the skin.In this paper the authors review the existing algorithms for heart rate measurement with the help of a mobile phone camera and propose their own algorithm which is more efficient than the reviewed algorithms.Π Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ ΡΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ Π±ΠΎΠ»ΡΡΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠΏΠΎΡΠΎΠ±ΠΎΠ² ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ°ΡΡΠΎΡΡ ΠΏΡΠ»ΡΡΠ° ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°. ΠΠ΄ΠΈΠ½ ΠΈΠ· ΡΠ°ΠΊΠΈΡ
ΡΠΏΠΎΡΠΎΠ±ΠΎΠ² ΡΠ²ΡΠ·Π°Π½ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΊΠ°ΠΌΠ΅ΡΡ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Π»Π΅ΡΠΎΠ½Π°. ΠΠ½ ΡΠ΄ΠΎΠ±Π΅Π½ ΠΈ ΠΏΡΠΎΡΡ Ρ ΡΠΎΡΠΊΠΈ Π·ΡΠ΅Π½ΠΈΡ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΈ Π½Π΅ ΡΡΠ΅Π±ΡΠ΅Ρ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΡ
Π·Π½Π°Π½ΠΈΠΉ ΠΈΠ»ΠΈ ΠΏΠΎΠΊΡΠΏΠΊΠΈ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΡ
ΡΡΡΡΠΎΠΉΡΡΠ² Π΄Π»Ρ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΡΠ»ΡΡΠ°. ΠΡΠ΅, ΡΡΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ, β ΡΡΠΎ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΡΠΉ ΡΠ΅Π»Π΅ΡΠΎΠ½ ΡΠΎ Π²ΡΡΡΠΎΠ΅Π½Π½ΠΎΠΉ ΠΊΠ°ΠΌΠ΅ΡΠΎΠΉ ΠΈ Π²ΡΠΏΡΡΠΊΠΎΠΉ. ΠΡΠ½ΠΎΠ²Π½Π°Ρ ΠΈΠ΄Π΅Ρ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΡΠΏΠΎΡΠΎΠ±Π° Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΡΠΎΠΌ, ΡΡΠΎΠ±Ρ Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠΎΠ²Π°ΡΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ²Π΅ΡΠ° ΠΊΠΎΠΆΠΈ ΠΏΠ°Π»ΡΡΠ° ΡΡΠΊΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡ ΠΈΠ·-Π·Π° ΠΏΡΠ»ΡΡΠ°ΡΠΈΠΈ ΠΊΡΠΎΠ²ΠΈ. ΠΡΠΎΡΠ΅ΡΡ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π²ΡΠ³Π»ΡΠ΄ΠΈΡ ΠΎΡΠ΅Π½Ρ ΠΏΡΠΎΡΡΠΎ: ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΏΡΠΈΠΊΠ»Π°Π΄ΡΠ²Π°Π΅Ρ ΠΏΠ°Π»Π΅Ρ ΠΊ ΠΊΠ°ΠΌΠ΅ΡΠ΅, ΠΏΠΎΡΠ»Π΅ ΡΠ΅Π³ΠΎ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ Π½Π° ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠΌ ΡΠ΅Π»Π΅ΡΠΎΠ½Π΅ Π½Π°ΡΠΈΠ½Π°Π΅Ρ Π·Π°Ρ
Π²Π°ΡΡΠ²Π°ΡΡ ΠΈ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΠΊΠ°Π΄ΡΡ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ Ρ ΠΊΠ°ΠΌΠ΅ΡΡ. ΠΠ½Π°Π»ΠΈΠ·ΠΈΡΡΡ ΡΡΠ΅Π΄Π½ΠΈΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΊΡΠ°ΡΠ½ΠΎΠΉ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ ΠΊΠ°Π΄ΡΠΎΠ² Ρ ΠΊΠ°ΠΌΠ΅ΡΡ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Π»Π΅ΡΠΎΠ½Π°, ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ ΡΡΠ°ΡΡΠΊΠ° ΠΊΠΎΠΆΠΈ, ΠΌΠΎΠΆΠ½ΠΎ ΡΠ΄Π΅Π»Π°ΡΡ Π²ΡΠ²ΠΎΠ΄ ΠΎ ΡΠ°ΡΡΠΎΡΠ΅ ΠΏΡΠ»ΡΡΠ°.Π Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ Π°Π²ΡΠΎΡΡ Π΄Π΅Π»Π°ΡΡ ΠΎΠ±Π·ΠΎΡ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² Π΄Π»Ρ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ°ΡΡΠΎΡΡ ΠΏΡΠ»ΡΡΠ° Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Π»Π΅ΡΠΎΠ½Π° ΠΈ ΠΏΡΠ΅Π΄Π»Π°Π³Π°ΡΡ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ, ΠΊΠΎΡΠΎΡΡΠΉ Π±ΠΎΠ»Π΅Π΅ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π΅Π½, ΡΠ΅ΠΌ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Π½ΡΠ΅.
Lithological markers of protobazhenit mats splitting on sedimentary slope
Bazhenov abnormal sequences (BAS) are treated as result of protobazhenit plastic deformation by neocomian submarine slump on sedimentary slope. Protobazhenit mats had low bulk density (1.1-1.5 g/sm3) and positive buoyancy in silty-sandy mud of unconsolidated deposits (bulk density 1.7-1.8 g/sm3). Conceptual geomechanical model of BAS generation includes 6 studies: 1 - bedding (slipping) slide, breakage of under-achimovsky clay and protobazhenit, 2 - out-flow achimovsky sandy slump, 3 - slump pulp spreading under protobazhenit layer with its deformation and cracking, 4 - protobazhenit layer cracking due to local loading of growing sedimentary slope, 5 - secondary heaving sand injection through lateral protobazhenit brake side, 6 - burial stage. Up-floating of protobazhenit mats on semiliquid sedimentary slope occurred discretely with numerous subsidings, splittings and jumpings events. During sedimentary slope progradation mats had lack of Archimedes stability due to rising of sedimentary level, led to increment of hydraulic pressure on their side surfaces. The hydraulic fracturing conditions appeared when this pressure exceeded protobazhenit shear strength. Fracturing event was provoked by microseism or by hydraulic shock of gravity mass movement. Mat usually had splitted on two parts: lower part was fixed within sediments, upper one lifted to Archimedes equilibrium level. Splitting and up-lifting of mat produced debris flows, those were spreading on slope and enriched by protobazhenitβsinclasts. These outstanding debrit layers with bazhenitinclasts may be used as lithological markers of mats splitting events for achimovsky sequences. Theoretical model is illustrated by seismostratigraphic interpretation of achimovsky beds of Imilorskoe field of West Siberia. Two types of debrit layers with bazhenitinclasts was detected in well core. First type is generated byptotobazhenit layerβs breakage by non-uniform load of sedimentary slope (fixed in one well). The second type is associated with on-slope splitting and up-lifting of protobazhenit mats (traced in core of three wells)
ΠΠ»Π³ΠΎΡΠΈΡΠΌ Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π°Π³Π΅Π½ΡΠΎΠ² dataflow-ΡΠ΅ΡΠΈ Π½Π° ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ΅ Smart-M3
The paper presents an agent substitution algorithm for a dataflow network implemented on the Smart-M3 platform. Such a substitution allows to transfer control and computational context from an unexpectedly disconnected agent to a programmable substitute agent for the period of absence of the first agent in the network. It also guarantees integrity of the information flow, i.e. the functioning of all dependent services is not disrupted after the agent disconnection. When the agent returns to the network the reverse substitution occurs also with keeping integrity of the information flow. The paper gives a description of the dataflow network implementation and substitution mechanism structure on the Smart-M3 platform. The detailed description of the substitution algorithm including initialization, registration, and bidirectional substitution phases is given. The proposed substitution algorithm was implemented by the authors in the substitution mechanism as a part of the RedSIB semantic information broker on the Smart-M3 platform.Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π°Π³Π΅Π½ΡΠ° dataflow-ΡΠ΅ΡΠΈ, ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½ΠΎΠΉ Π½Π° ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ΅ Smart-M3. Π’Π°ΠΊΠΎΠ΅ Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΠ΅ΡΠ΅Π½Π΅ΡΡΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΠΈ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡ Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΠΉ ΠΎΡ ΠΏΡΠ΅ΠΆΠ΄Π΅Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎ ΠΎΡΠΊΠ»ΡΡΠΈΠ²ΡΠ΅Π³ΠΎΡΡ Π°Π³Π΅Π½ΡΠ° ΠΊ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΡΠ΅ΠΌΠΎΠΌΡ Π°Π³Π΅Π½ΡΡ-Π·Π°ΠΌΠ΅ΡΡΠΈΡΠ΅Π»Ρ Π½Π° Π²ΡΠ΅ΠΌΡ ΠΎΡΡΡΡΡΡΠ²ΠΈΡ ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ Π°Π³Π΅Π½ΡΠ° Π² ΡΠ΅ΡΠΈ. ΠΡΠΈ ΡΡΠΎΠΌ Π³Π°ΡΠ°Π½ΡΠΈΡΡΠ΅ΡΡΡ ΡΠ΅Π»ΠΎΡΡΠ½ΠΎΡΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠΎΡΠΎΠΊΠΎΠ², ΡΠΎ Π΅ΡΡΡ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΡΠ΅Ρ
Π·Π°Π²ΠΈΡΠΈΠΌΡΡ
ΡΠ΅ΡΠ²ΠΈΡΠΎΠ² Π½Π΅ Π½Π°ΡΡΡΠ°Π΅ΡΡΡ ΠΏΡΠΈ ΠΎΡΠΊΠ»ΡΡΠ΅Π½ΠΈΠΈ Π°Π³Π΅Π½ΡΠ°. ΠΡΠΈ Π²ΠΎΠ·Π²ΡΠ°ΡΠ΅Π½ΠΈΠΈ Π°Π³Π΅Π½ΡΠ° Π² ΡΠ΅ΡΡ ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΠΈΡ ΠΎΠ±ΡΠ°ΡΠ½ΠΎΠ΅ Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅ ΡΠ°ΠΊΠΆΠ΅ Ρ ΡΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ΅Π»ΠΎΡΡΠ½ΠΎΡΡΠΈ Π²ΡΠ΅Ρ
ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠΎΡΠΎΠΊΠΎΠ². ΠΡΠΈΠ²Π΅Π΄Π΅Π½ΠΎ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ dataflow-ΡΠ΅ΡΠΈ ΠΈ ΡΡΡΡΠΊΡΡΡΡ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠ° Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π°Π³Π΅Π½ΡΠΎΠ² Π΄Π»Ρ ΠΏΠ»Π°ΡΡΠΎΡΠΌΡ Smart-M3. ΠΠ°Π½ΠΎ Π΄Π΅ΡΠ°Π»ΡΠ½ΠΎΠ΅ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠ΅Π΅ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΠΈΠ½ΠΈΡΠΈΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ, ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΈ Π΄Π²ΡΠ½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΠΎΠ³ΠΎ Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π°Π³Π΅Π½ΡΠΎΠ². ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ Π² ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠ΅ Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π² Π±ΡΠΎΠΊΠ΅ΡΠ΅ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ RedSIB Π½Π° ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ΅ Smart-M3.
A clinical case of post-COVID-19 myoendocarditis and arrhythmic syndrome at the outpatient stage
Background: Infection with the SARS-CoV-2 virus entails the development of complications which affect the prognosis of the underlying disease. More than 40% of COVID-19 complications represent diseases of the cardiovascular system, most of which are the rhythm and conduction disturbances. In order to avoid these complications, it is necessary to detect cases of infection in a timely manner at the outpatient stage. Clinical case description: A 40-year-old patient came to the clinic with complaints of interruptions in the heart rhythm that appeared after the coronavirus infection. The laboratory examination (CBC) revealed signs of systemic inflammation (leukocytosis 12.6Γ109 U/l; erythrocyte sedimentation rate 18 mm/h, C-reactive protein 18 mg/l); the instrumental examination of the heart revealed the rhythm disturbances in the form of frequent ventricular ectopic activity and weakness of the SA node. The patient received propafenone (150 mg, 3 times a day) as a therapy with a positive effect. Against the background of improvement in the patientβs condition and despite the history of myocarditis and a positive result of enzyme immunoassay for antibodies to SARS-CoV-2 (IgG, 10 BAU/ml), the patient was prescribed immunization with the CoviVac vaccine. After the immunization, the condition worsening was observed in the form of an increase in the rhythm disturbances, which required an inpatient treatment. A clinical diagnosis of recurrent ventricular arrhythmia β ventricular extrasystole was established, and the therapy was corrected. The outcome was favorable. Conclusion: Myocarditis is one of the most common complications of SARS-CoV-2 and should be kept in mind at all stages of medical care. This clinical case demonstrates the importance of the correct diagnosis and treatment of post-COVID myocarditis, as well as the need to assess contraindications for SARS-CoV-2 vaccination in patients with cardiac complications
ΠΠ°ΠΊΡΡΡΠΈΠ΅ ΠΎΠ±ΡΠΈΡΠ½ΠΎΠ³ΠΎ Π΄Π΅ΡΠ΅ΠΊΡΠ° ΠΌΡΠ³ΠΊΠΈΡ ΡΠΊΠ°Π½Π΅ΠΉ ΠΎΠΏΠΎΡΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ Π½ΠΎΡΡΠΈ ΡΡΠΎΠΏΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π²Π°ΡΠΊΡΠ»ΡΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π°ΡΡΠΎΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠ° ΠΏΡΡΠΌΠΎΠΉ ΠΌΡΡΡΡ ΠΆΠΈΠ²ΠΎΡΠ°
In the given clinical example, where the patient is a child with an extensive degloving wound of the footplate of the right (not the left) foot, it is demonstrated the possibility of the rehab of the foot's support ability by the method of microsurgical muscle grafting combining with full-thickness skin autodermoplasty. The recovery of support ability of extremity with defects in the tissues of the plantar surface is an actual problem of modern surgery. When there are small and medium-sized defects, it can be used local and regional flaps, but when the defects are extensive, it is needed to create soft tissues anew, and they must be able to withstand multiple physical stresses. In the given clinical example, where the patient is a child with an extensive degloving wound of the footplate of the left foot, it is demonstrated the possibility of the rehab of the foot's support ability by the method of microsurgical muscle grafting combining with full-thickness skin autodermoplasty.Β Π ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Π½ΠΎΠΌ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΏΡΠΈΠΌΠ΅ΡΠ΅, Π³Π΄Π΅ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠΌ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ΅Π±Π΅Π½ΠΎΠΊ Ρ ΠΎΠ±ΡΠΈΡΠ½ΠΎΠΉ ΡΠΊΠ°Π»ΡΠΏΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠ°Π½ΠΎΠΉ ΠΎΠΏΠΎΡΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ ΠΏΡΠ°Π²ΠΎΠΉ, Π° Π½Π΅ Π»Π΅Π²ΠΎΠΉ ΡΡΠΎΠΏΡ, ΠΏΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°Π½Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ ΠΎΠΏΠΎΡΠ½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΡΡΠΎΠΏΡ ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΌΠΈΠΊΡΠΎΡ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠ΅ΡΠ΅ΡΠ°Π΄ΠΊΠΈ ΠΌΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°ΡΡΠΎΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠ° Π² ΡΠΎΡΠ΅ΡΠ°Π½ΠΈΠΈ Ρ ΠΏΠΎΠ»Π½ΠΎΡΠ»ΠΎΠΉΠ½ΠΎΠΉ Π°ΡΡΠΎΠ΄Π΅ΡΠΌΠΎΠΏΠ»Π°ΡΡΠΈΠΊΠΎΠΉ. ΠΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ ΠΎΠΏΠΎΡΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΠΈ ΡΡΠΎΠΏΡ ΠΏΡΠΈ Π΄Π΅ΡΠ΅ΠΊΡΠ°Ρ
ΡΠΊΠ°Π½Π΅ΠΉ ΠΏΠΎΠ΄ΠΎΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΎΠΉ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Ρ
ΠΈΡΡΡΠ³ΠΈΠΈ. ΠΡΠ»ΠΈ ΠΏΡΠΈ ΠΏΠΎΠ²ΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΡΡ
Π½Π΅Π±ΠΎΠ»ΡΡΠΈΡ
ΠΈ ΡΡΠ΅Π΄Π½ΠΈΡ
ΡΠ°Π·ΠΌΠ΅ΡΠΎΠ² Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΡΠ½ΡΡ
ΠΈ ΡΠ΅Π³ΠΈΠΎΠ½Π°ΡΠ½ΡΡ
Π»ΠΎΡΠΊΡΡΠΎΠ², ΡΠΎ Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΎΠ±ΡΠΈΡΠ½ΠΎΠ³ΠΎ Π΄Π΅ΡΠ΅ΠΊΡΠ° ΡΡΠ΅Π±ΡΠ΅Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½ΠΎΠ²ΡΡ
ΠΏΠΎΠ»Π½ΠΎΡΠ΅Π½Π½ΡΡ
ΠΌΡΠ³ΠΊΠΈΡ
ΡΠΊΠ°Π½Π΅ΠΉ, ΡΠΏΠΎΡΠΎΠ±Π½ΡΡ
Π²ΡΠ΄Π΅ΡΠΆΠ°ΡΡ ΠΏΠΎΡΡΠΎΡΠ½Π½ΡΠ΅ ΠΌΠ΅Ρ
Π°Π½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π½Π°Π³ΡΡΠ·ΠΊΠΈ. Π ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Π½ΠΎΠΌ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΏΡΠΈΠΌΠ΅ΡΠ΅, Π³Π΄Π΅ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠΌ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ΅Π±Π΅Π½ΠΎΠΊ Ρ ΠΎΠ±ΡΠΈΡΠ½ΠΎΠΉ ΡΠΊΠ°Π»ΡΠΏΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠ°Π½ΠΎΠΉ ΠΎΠΏΠΎΡΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ Π»Π΅Π²ΠΎΠΉ ΡΡΠΎΠΏΡ, ΠΏΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°Π½Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ ΠΎΠΏΠΎΡΠ½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΡΡΠΎΠΏΡ ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΌΠΈΠΊΡΠΎΡ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠ΅ΡΠ΅ΡΠ°Π΄ΠΊΠΈ ΠΌΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°ΡΡΠΎΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠ° Π² ΡΠΎΡΠ΅ΡΠ°Π½ΠΈΠΈ Ρ ΠΏΠΎΠ»Π½ΠΎΡΠ»ΠΎΠΉΠ½ΠΎΠΉ Π°ΡΡΠΎΠ΄Π΅ΡΠΌΠΎΠΏΠ»Π°ΡΡΠΈΠΊΠΎΠΉ.
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