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Agent communication method in cooperative environment based on the artificial neural networks
The problem of communication between cooperating agents in multiagent environments is
considered in this paper. An algorithm is proposed that is based in reinforcement learning and
recurrent neural networks. Main idea behind the algorithm is to use an additional recurrent network that translates information from internal state of one agent to internal state of another agent. Experimental evaluation is performed on model environment. Experimental results have shown that proposed method is potentially useful but requires additional investigation. Π ΡΠΎΠ±ΠΎΡΡ ΡΠΎΠ·Π³Π»ΡΠ΄Π°ΡΡΡΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ° ΠΊΠΎΠΌΡΠ½ΡΠΊΠ°ΡΡΡ ΠΊΠΎΠΎΠΏΠ΅ΡΡΡΡΠΈΡ
Π°Π³Π΅Π½ΡΡΠ² Ρ ΠΌΡΠ»ΡΡΠΈΠ°Π³Π΅Π½ΡΠ½ΠΈΡ
ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ°Ρ
. ΠΠ°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΏΡΠ΄Ρ
ΠΎΠ΄ΡΠ² Π½Π°Π²ΡΠ°Π½Π½Ρ Π· ΠΏΡΠ΄ΠΊΡΡΠΏΠ»Π΅Π½Π½ΡΠΌ Π· Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½ΡΠΌ ΡΠ΅ΠΊΡΡΠ΅Π½ΡΠ½ΠΈΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΠΈΡ
ΠΌΠ΅ΡΠ΅ΠΆ. ΠΠΎΠ»ΠΎΠ²Π½Π° ΡΠ΄Π΅Ρ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ β ΡΠ΅ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ Π΄ΠΎΠ΄Π°ΡΠΊΠΎΠ²ΠΎΡ ΡΠ΅ΠΊΡΡΠ΅Π½ΡΠ½ΠΎΡ ΠΌΠ΅ΡΠ΅ΠΆΡ, ΡΠΊΠ° Π²ΠΈΠΊΠΎΠ½ΡΡ ΠΎΠ±ΠΌΡΠ½ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡΡ ΠΌΡΠΆ Π²Π½ΡΡΡΡΡΠ½ΡΠΌΠΈ ΡΡΠ°Π½Π°ΠΌΠΈ Π΄Π²ΠΎΡ
Π°Π³Π΅Π½ΡΡΠ² ΠΏΡΠ΄ ΡΠ°Ρ ΠΊΠΎΠΌΡΠ½ΡΠΊΠ°ΡΡΡ. ΠΠ±ΡΠ°Π½ΠΈΠΉ ΠΏΡΠ΄Ρ
ΡΠ΄ Π·Π°ΡΠ½ΠΎΠ²Π°Π½ΠΈΠΉ Π½Π° Π·Π°ΡΡΠΎΡΡΠ²Π°Π½Π½Ρ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ A3C, ΡΠ΅ΠΊΡΡΠ΅Π½ΡΠ½ΠΎΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΡ ΠΌΠ΅ΡΠ΅ΠΆΡ Long Short-Term Memory (LSTM) Π΄Π»Ρ ΠΊΠ΅ΡΡΠ²Π°Π½Π½Ρ Π°Π³Π΅Π½ΡΠΎΠΌ ΡΠ° Π΄ΠΎΠ΄Π°ΡΠΊΠΎΠ²ΠΎΡ ΡΠ΅ΠΊΡΡΠ΅Π½ΡΠ½ΠΎΡ ΠΌΠ΅ΡΠ΅ΠΆΡ (ΠΌΠ΅ΡΠ΅ΠΆΡ ΠΊΠΎΠΌΡΠ½ΡΠΊΠ°ΡΡΡ). ΠΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½ΠΎ Π΄Π²Π° Π²Π°ΡΡΠ°Π½ΡΠ° Π°ΡΡ
ΡΡΠ΅ΠΊΡΡΡΠΈ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΡ ΠΌΠ΅ΡΠ΅ΠΆΡ. ΠΠ° ΠΏΠ΅ΡΡΠΎΡ Π²Π΅ΡΡΡΡΡ Π°Π³Π΅Π½ΡΠΈ ΡΠΏΠΎΡΠ°ΡΠΊΡ Β«ΡΠΏΡΠ»ΠΊΡΡΡΡΡΡΒ», Π° ΠΏΠΎΡΡΠΌ Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΡΡΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ Ρ ΡΠΊΠΎΡΡΡ Π΄ΠΎΠ΄Π°ΡΠΊΠΎΠ²ΠΈΡ
Π΄Π°Π½ΠΈΡ
ΠΏΡΠΎ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ΅. ΠΡΡΠ³Π° Π²Π΅ΡΡΡΡ ΡΠΏΠΎΡΠ°ΡΠΊΡ Π°Π½Π°Π»ΡΠ·ΡΡ Π΄Π°Π½Ρ ΠΏΡΠΎ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ΅, Π° ΠΏΠΎΡΡΠΌ ΡΠ΅Π°Π»ΡΠ·ΡΡ Β«ΡΠΏΡΠ»ΠΊΡΠ²Π°Π½Π½ΡΒ» Π°Π³Π΅Π½ΡΡΠ², ΠΎΠ±ΠΌΡΠ½ΡΡΡΠΈΡΡ Π²ΠΈΡΠΎΠΊΠΎΡΡΠ²Π½Π΅Π²ΠΎΡ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡΡ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Ρ ΠΎΡΡΠ½ΠΊΡ Π·Π°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ Π½Π° ΠΏΡΠΈΠΊΠ»Π°Π΄Ρ ΠΌΠΎΠ΄Π΅Π»ΡΠ½ΠΎΡ Π·Π°Π΄Π°ΡΡ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΠΈ Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ Π΄ΠΎΠ²Π΅Π»ΠΈ, ΡΠΎ Π·Π°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΡΠ΄Ρ
ΡΠ΄ ΠΏΠΎΠΊΡΠ°ΡΡΡ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡΡΡ ΠΊΠΎΠΎΠΏΠ΅ΡΡΡΡΠΈΡ
Π°Π³Π΅Π½ΡΡΠ². ΠΠ΅ΡΠ΅Π²Π°Π³ΠΎΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ Ρ ΡΠ΅, ΡΠΎ Π²ΡΠ½ Π½Π΅ ΠΏΠΎΡΡΠ΅Π±ΡΡ Π½Π°ΡΠ²Π½ΠΎΡΡΡ ΡΠΊΠ»Π°Π΄Π½ΠΈΡ
ΡΠ° ΡΡΡΡΠΊΡΡΡΠΎΠ²Π°Π½ΠΈΡ
ΠΎΠ±ΡΠΈΡΠ»ΡΠ²Π°Π»ΡΠ½ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ ΡΠ° ΠΌΠΎΠΆΠ΅ Π±ΡΡΠΈ ΡΡΠ·ΠΈΡΠ½ΠΎ ΡΠ΅Π°Π»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΌ Π·Π° Π΄ΠΎΠΏΠΎΠΌΠΎΠ³ΠΎΡ Π΄ΡΠΆΠ΅ ΠΌΠ°Π»Π΅Π½ΡΠΊΠΈΡ
ΠΎΠ±βΡΠΊΡΡΠ², ΡΠ°ΠΊΠΈΡ
, ΡΠΊ Π½Π°ΠΏΡΠΈΠΊΠ»Π°Π΄ ΠΌΠ°ΠΊΡΠΎΠΌΠΎΠ»Π΅ΠΊΡΠ»ΠΈ. Π ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ° ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΈ ΠΊΠΎΠΎΠΏΠ΅ΡΠΈΡΡΡΡΠΈΡ
Π°Π³Π΅Π½ΡΠΎΠ² Π² ΠΌΡΠ»ΡΡΠΈΠ°Π³Π΅Π½ΡΠ½ΠΎΠΉ ΡΡΠ΅Π΄Π΅. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Ρ ΠΏΠΎΠ΄ΠΊΡΠ΅ΠΏΠ»Π΅Π½ΠΈΠ΅ΠΌ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠ΅ΠΊΡΡΡΠ΅Π½ΡΠ½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ. ΠΠ»Π°Π²Π½Π°Ρ ΠΈΠ΄Π΅Ρ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° β ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ, ΠΊΠΎΡΠΎΡΠ°Ρ Π²ΡΠΏΠΎΠ»Π½ΡΠ΅Ρ ΠΎΠ±ΠΌΠ΅Π½ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ΅ΠΉ ΠΌΠ΅ΠΆΠ΄Ρ Π²Π½ΡΡΡΠ΅Π½Π½ΠΈΠΌΠΈ ΡΠΎΡΡΠΎΡΠ½ΠΈΡΠΌΠΈ Π΄Π²ΡΡ
Π°Π³Π΅Π½ΡΠΎΠ² Π²ΠΎ Π²ΡΠ΅ΠΌΡ ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΈ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΎΡΠ½ΠΎΠ²Π°Π½ Π½Π° ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° A3C, ΡΠ΅ΠΊΡΡΡΠ΅Π½ΡΠ½ΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ Long Short-Term Memory (LSTM) Π΄Π»Ρ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π°Π³Π΅Π½ΡΠΎΠΌ ΠΈ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ΅ΠΊΡΡΡΠ΅Π½ΡΠ½ΠΎΠΉ ΡΠ΅ΡΠΈ (ΡΠ΅ΡΠΈ
ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΈ). ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΎ Π΄Π²Π° Π²Π°ΡΠΈΠ°Π½ΡΠ° ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ. ΠΠ΅ΡΠ²Π°Ρ Π²Π΅ΡΡΠΈΡ ΡΠ½Π°ΡΠ°Π»Π° ΠΎΡΠ³Π°Π½ΠΈΠ·ΠΎΠ²ΡΠ²Π°Π΅Ρ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ Π°Π³Π΅Π½ΡΠΎΠ², Π° Π·Π°ΡΠ΅ΠΌ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΎ ΡΡΠ΅Π΄Π΅. ΠΡΠΎΡΠ°Ρ Π²Π΅ΡΡΠΈΡ ΡΠ½Π°ΡΠ°Π»Π° Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅Ρ Π΄Π°Π½Π½ΡΠ΅ ΠΎ ΡΡΠ΅Π΄Π΅, Π° ΠΏΠΎΡΠΎΠΌ ΠΊΠΎΠΌΠΌΡΠ½ΠΈΡΠΈΡΡΠ΅Ρ, ΠΎΠ±ΠΌΠ΅Π½ΠΈΠ²Π°ΡΡΡ Π²ΡΡΠΎΠΊΠΎΡΡΠΎΠ²Π½Π΅Π²ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ΅ΠΉ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π° ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΡΠ½ΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ° ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΡΠ»ΡΡΡΠ°Π΅Ρ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΊΠΎΠΎΠΏΠ΅ΡΠΈΡΡΡΡΠΈΡ
Π°Π³Π΅Π½ΡΠΎΠ². ΠΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²ΠΎΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΌΠΎΠΆΠ½ΠΎ ΡΡΠΈΡΠ°ΡΡ ΡΠΎ, ΡΡΠΎ ΠΎΠ½ Π½Π΅ ΡΡΠ΅Π±ΡΠ΅Ρ Π½Π°Π»ΠΈΡΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΠΈ ΡΡΡΡΠΊΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΈ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΡΠΈΠ·ΠΈΡΠ΅ΡΠΊΠΈ ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΎΡΠ΅Π½Ρ ΠΌΠ°Π»Π΅Π½ΡΠΊΠΈΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ², ΡΠ°ΠΊΠΈΡ
, ΠΊΠ°ΠΊ Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ ΠΌΠ°ΠΊΡΠΎΠΌΠΎΠ»Π΅ΠΊΡΠ»Ρ
Initial-state dependence of coupled electronic and nuclear fluxes in molecules
We demonstrate that coupled electronic and nuclear fluxes in molecules can strongly depend on the initial state
preparation. Starting the dynamics of an aligned D2
+ molecule at two different initial conditions, the inner and the outer turning points, we observe qualitatively different oscillation patterns of the nuclear fluxes developing after 30 fs. This corresponds to different orders of magnitude bridged by the time evolution of the nuclear dispersion. Moreover, there are attosecond time intervals within which the electronic fluxes do not adapt to the nuclei motion depending on the initial state. These results are inferred from two different approaches for the numerical flux simulation, which are both in good agreement
ΠΠ΅ΡΠΎΠ΄Ρ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π°Π²ΡΠΎΡΡΡΠ²Π° Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ ΡΡΡΠ΄Π΅Π½ΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠ»Π°Π³ΠΈΠ°ΡΠ°
In the modern educational context the problem of plagiarism is urgent and requires the development of effective methods of detection and prevention of this phenomenon. The application of authorship identification methods in the field of student plagiarism detection is considered. Different check, detect and analyze plagiarism approaches in various works are investigated. Both classical methods, which include text comparison and similarity search, and modern methods based on machine learning algorithms, as well as their combination and potential modifications, are considered. The advantages and limitations of each method are also discussed, and recommendations are given for choosing one or another approach according to the specific requirements of the research.Special attention is paid to such modern methods as metadata analysis and the application of neural networks. Stylistic analysis reveals authorial peculiarities such as word choice, preferred wording, and even punctuation. Lexical and syntactic models are used to identify repetitive phrases and structures that may indicate plagiarism. Statistical methods can identify anomalies in the use of words and phrases, and machine learning can create models to calculate the probability of plagiarism based on large amounts of data.Ultimately, an comparison of authorship identification techniques in the field of student plagiarism detection is provided, which aims to provide valuable information about different approaches and their applicability, and to help researchers and educators develop effective strategies for detecting and preventing plagiarism in educational environments.Π ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΠΌ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ° ΠΏΠ»Π°Π³ΠΈΠ°ΡΠ° ΡΠ²Π»ΡΠ΅ΡΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΈ ΡΡΠ΅Π±ΡΠ΅Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΈ ΠΏΡΠ΅Π΄ΠΎΡΠ²ΡΠ°ΡΠ΅Π½ΠΈΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ²Π»Π΅Π½ΠΈΡ. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π°Π²ΡΠΎΡΡΡΠ²Π° Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΡΡΡΠ΄Π΅Π½ΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠ»Π°Π³ΠΈΠ°ΡΠ°. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΠ΅ Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅ΡΠΊΠΈ, ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΠ»Π°Π³ΠΈΠ°ΡΠ° Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠ°Π±ΠΎΡΠ°Ρ
. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΊΠ°ΠΊ ΠΊΠ»Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ, Π² ΡΠΈΡΠ»Π΅ ΠΊΠΎΡΠΎΡΡΡ
ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΡΠ΅ΠΊΡΡΠΎΠ² ΠΈ ΠΏΠΎΠΈΡΠΊ ΡΡ
ΠΎΠ΄ΡΡΠ²Π°, ΡΠ°ΠΊ ΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠ΅ Π½Π° Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°Ρ
ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΡ
ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΠ΅ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ. Π’Π°ΠΊΠΆΠ΅ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½Ρ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΈ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡ ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΈ Π΄Π°Π½Ρ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΈ ΠΏΠΎ Π²ΡΠ±ΠΎΡΡ ΡΠΎΠ³ΠΎ ΠΈΠ»ΠΈ ΠΈΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΠΌΠΈ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡΠΌΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ. ΠΡΠΎΠ±ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠ΄Π΅Π»Π΅Π½ΠΎ ΡΠ°ΠΊΠΈΠΌ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠΌ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌ, ΠΊΠ°ΠΊ Π°Π½Π°Π»ΠΈΠ· ΠΌΠ΅ΡΠ°Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ. Π‘ΡΠΈΠ»ΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π²ΡΡΠ²ΠΈΡΡ Π°Π²ΡΠΎΡΡΠΊΠΈΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ, ΡΠ°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ Π²ΡΠ±ΠΎΡ ΡΠ»ΠΎΠ², ΠΏΡΠ΅Π΄ΠΏΠΎΡΡΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²ΠΊΠΈ ΠΈ Π΄Π°ΠΆΠ΅ ΠΏΡΠ½ΠΊΡΡΠ°ΡΠΈΡ. ΠΠ΅ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ Π΄Π»Ρ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΠΏΠΎΠ²ΡΠΎΡΡΡΡΠΈΡ
ΡΡ ΡΡΠ°Π· ΠΈ ΡΡΡΡΠΊΡΡΡ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ ΡΠΊΠ°Π·ΡΠ²Π°ΡΡ Π½Π° ΠΏΠ»Π°Π³ΠΈΠ°Ρ. Π‘ΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ Π²ΡΡΠ²ΠΈΡΡ Π°Π½ΠΎΠΌΠ°Π»ΠΈΠΈ Π² ΡΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΠΈ ΡΠ»ΠΎΠ² ΠΈ ΡΡΠ°Π·, Π° ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ β ΡΠΎΠ·Π΄Π°ΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ,Β ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠ΅ ΡΠ°ΡΡΡΠΈΡΠ°ΡΡ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΡ ΠΏΠ»Π°Π³ΠΈΠ°ΡΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π±ΠΎΠ»ΡΡΠΎΠ³ΠΎ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° Π΄Π°Π½Π½ΡΡ
.Π ΠΊΠΎΠ½Π΅ΡΠ½ΠΎΠΌ ΠΈΡΠΎΠ³Π΅ ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²Π»Π΅Π½ΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π°Π²ΡΠΎΡΡΡΠ²Π° Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΡΡΠ΄Π΅Π½ΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠ»Π°Π³ΠΈΠ°ΡΠ°, ΡΡΠΎ ΠΈΠΌΠ΅Π΅Ρ ΡΠ΅Π»ΡΡ Π΄Π°ΡΡ ΡΠ΅Π½Π½ΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΎ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π°Ρ
ΠΈ ΠΈΡ
ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΠΎΡΡΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΠΌΠΎΠΆΠ΅Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠΌ ΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»ΡΠΌ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠ΅ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΈ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΠΈ ΠΏΡΠ΅Π΄ΠΎΡΠ²ΡΠ°ΡΠ΅Π½ΠΈΡ ΠΏΠ»Π°Π³ΠΈΠ°ΡΠ° Π² ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΡΠ΅Π΄Π΅
THE USE OF NEW REAGENT KITS FOR DETECTION AND DESCRIPTION OF ADDITIONAL ALLELES
During the screening typing of recruited volunteers with Volga Federal District for unrelated hematopoietic stem cell registry on the loci (HLA)-A, B, DRB1, DRB345 in sample No 1758 identified a new allele at locus A. The use of basic kit AlleleSEQR HLA-A Sequencing in combination with HARP β A2F98A allowed to determine the genotype of this sample β Π*30:01:01, a new allele Π*25, Π*13, 44, DRB1*03, 09, DRB3*02, DRB4*01
Construction of the Initial Part of a Ion Linear Accelerator from Similar Short Cavities
The construction of the initial part of a normally conducting linac for
hydrogen ion beams with a pulsed current of ~20 mA up to an energy of ~70 MeV
is considered. The RFQ at a frequency of ~160 MHz accelerates ions to an energy
of ~4 MeV. Further acceleration is carried out at a doubled frequency by short,
up to , cavities, operating in the TM010 mode, with drift tubes.
Focusing is carried out by doublets of quadrupole lenses placed between the
cavities. The structure of the accelerating-focusing channel, with given beam
parameters, with reserves provides both the conditions for stable longitudinal
and transverse motion of particles, and reliable technical implementation. The
main results of the simulations of particle dynamics and the main parameters of
the elements of the channel are presented. The possibility of constructing an
linac with a higher output energy is analyzed.Comment: in Russian languag
Immunogenetic characteristics of unrelated hematopoietic stem cell donors recruited in the Sverdlovsk, Saratov, Yaroslavl and Vladimir regions
Aim of the study was to investigate the distribution features of HLA alleles and multilocus haplotypes in potential donors of hematopoietic stem cells recruting in the Sverdlovsk, Saratov, Yaroslavl and Vladimir regions. Material and methods. Sequence Based Typing technology was used to identify human leukocyte antigen (HLA)-A, -B, -C, -DRB1 alleles from 2683 Russian unrelated bone marrow volunteers living in the Sverdlovsk (n = 1018), Saratov (n = 825), Yaroslavl (n = 604) and Vladimir (n = 236) regions. HLA allele and haplotype frequencies were estimated via maximum-likelihood analysis from genotypic data through an expectation-maximization (EM) algorithm for unknown gametic phase. Results and discussion. In all studied populations, 16 HLA-A, 13 HLA-C, 13 HLA-DRB1 alleles were selected. In the locus HLA-B, 28 alleles were detected in the populations of the Sverdlovsk and Yaroslavl regions, 27alleles β in the Saratov region, 25 alleles β in the Vladimir. Seventeen alleles, HLA-A*02, HLA-A*03, HLA-A*01, HLA-A*24, HLA-B*07, HLA-B*35, HLA-Π‘*07, HLA-Π‘*06, HLA-Π‘*04, HLA-Π‘*03, HLA-Π‘*12, HLA-DRB1*15, HLA-DRB1*07, HLA-DRB1*01, HLA-DRB1*13, HLA-DRB1*04, HLA-DRB1*11 exhibit frequencies over 10 %. The highest frequency extended haplotype in the all studied populations HLA-A*01-B*08-C*07-DRB1*03, was observed frequencies of 4,4 % β in the Sverdlovsk region, 3,2 % β in the Saratov region, 4,9 % β in the Yaroslavl region and 4,2 % β in the Vladimir region. Routine HLA typing allowed us to define four new HLA alleles in the populations of the Sverdlovsk and Saratov region
Reconstruction of recombination sites in genomic structures of the strains of genotype 6 of hepatitis C virus
The encoded portion of the complete genomes of 46 strains of the genotype 6 of hepatitis C virus through bioinformatics RDP programs complex group of 6 recombinants strains was identified, in which 7 recombination sites were fixed. Strains correspond to the three-recombinant HCV subtypes: 6a, 6b and 61. For each of the identified recombinant we defined parent strains from which they can be obtained. Three recombinants were obtained from parent strains of the same subtype (homologous inside subgenotypic recombination). For the remaining three recombinants parent strains were members of three different subtypes (between subgenotypic recombination).In one strain we identified a unique recombination site in a highly conservative NS3 gene. Most of the recombination sites occurred in the region of the structural genes C, E1 and E2, and in the area of non-structural genes NS5a and NS5b.In the recombinant strain DQ480518-6a two recombination site were identified. One site is located in the structural and nonstructural genes (E2 + NS1 + NS2), and a second one in non-structural region. Dimensions of recombination sites can vary from 86 to 1072 nucleotide bases. The study identified "hot spots" of recombination in the strains of genotype 6 of hepatitis C virus. The recombinants were found in the population of the three countries: the United States (from the serum of an immigrant), Hong Kong and China
Bioinformational analysis of Yersinia pseudotuberculosis IP32953 CRISPR/cas system
The results of this study include Yersinia pseudotuberculosis CRISPR/Cas system structure analysis. CRISPR/Cas system is a specific adaptive protection against heterogeneous genetic elements. The object of research was the complete genome of Y. pseudotuberculosis IP32953 (NC_006155). CRISPR/Cas system screening was performed by program modelling methods MacSyFinder ver. 1.0.2. CRISPR loci screening and analyzing were carried out by program package: CRISPR Recognition tool (CRT), CR1SP1: a CRISPR Interactive database, CRISPRFinder, and PilerCR. Spacer sequences were used in order to find protospacers in ACLAME, GenBank-Phage and RefSeq-Plasmid databases by BLASTn search algorithm. Protospacer sequences could be found in genomes of phages, plasmids and bacteria. In last case complete genomes of bacteria were analyzed by online-tool PHAST: PHAge Search Tool. Y. pseudotuberculosis IP329353 has CRISPR/Cas system that consists of one sequence of cas-genes and three loci. These loci are far away from each other. Locus YP1 is situated in close proximity to cas-genes. Protospacers were found in genomes of Y. pseudotuberculosis PB1/+, Y. intermedia Y228, Y. similis str. 228, Salmonella phage, Enterobacteria phage, Y. pseudotuberculosis 1P32953 plasmid pYV and plasmid of Y. pseudotuberculosis 1P31758. Thus, the combination of four program methods allows finding CRISPR/Cas system more precisely. Spacer sequences could be used for protospacer screening
APPLICATION OF MATHEMATICAL METHOD PREDICTIONS FOR IDENTIFICATION OF PATTERNS RELATIONS MUTATIONS IN PROTEINS ENCEPHALITIS VIRUS AND A MANIFESTATION OF ITS PHENOTYPIC TRAITS
We studied the natural connections between the amino acid sequences of proteins C, prM, E and NS1 virus strains of tick-borne encephalitis (TBE) and their three phenotypic traits -neuroinvasiveness, thermal stability and thermoresistance. Coupling strength is assessed using measures of competitive sequence similarity of each strain with reference strains. For such purposes subsets of strain sections are chosen amino acid composition specifics of which can predict the value of a phenotypic trait of interest. The possibility to predict missing elements in data both in amino acid composition, and in target properties is demonstrated. The relationships between pairs of phenotypic traits of strains were evaluated
Dynamic magnetic response of a ferrofluid in a static uniform magnetic field
A theory for the frequency-dependent magnetic susceptibility of a ferrofluid in a static uniform magnetic field is developed, including the dipolar interactions between the constituent particles. Interactions are included within the framework of modified mean-field theory. Predictions are given for the linear responses of the magnetization to a probing ac field both parallel and perpendicular to the static field and are tested against results from Brownian dynamics simulations. The effects of the particle concentration and dipolar coupling constant on the field-dependent static susceptibilities and the frequency dispersions are shown to be substantial, which justifies taking proper account of the interactions between particles. The theory is reliable provided that the volume concentration and dipolar coupling constant are not too large and within the range of values for real ferrofluids. Β© 2018 American Physical Society
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