37 research outputs found
ΠΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΎΠ΄Π½ΠΎΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΎΠΉ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΠΈΠ³ΡΡ, Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ΅ΠΉ Π΄ΡΡΠ»ΡΠ½ΡΠΉ Π±ΠΎΠΉ ΡΠ°Π½ΠΊΠΎΠ²
In improving computer games, which reproduce a battle of tanks, two tasks can be distinguished: increasing a collection of game tools to represent virtual prototypes of real tank models and ensuring a realistic game. To solve these problems, a tool is necessary that allows us to compare gaming capabilities of virtual tank brands with combat capabilities of their real prototypes. A mathematical model of a computer game that reproduces a duel battle of tanks can be used as the tool. The specified model satisfies the following requirements: the sequence of operations reproduced in the model is in line with the sequence of operations implemented by the player in the course of the game; the maximum amount of ammunition that a tank can use in a model must correspond to the amount of tank ammunition. The duel lasts until one of the tanks is hit, or until all the gunshots available to hit the enemy are expended. It is necessary to find the probabilities of possible outcomes of a duel battle, the mathematical expectation of its duration, the mathematical expectation of the ammunition consumption of each side.The solution to the problem is obtained by constructing a mathematical model according to the scheme of Markov random process with discrete states and continuous time. It is implemented as a program for a model of a duel battle of tanks and can be used when developing a computer game of the genre of tank simulators to assess the gaming capabilities of the virtual tanks in a duel battle from the data on the amount of their ammunition and on the intensity of the game process transition from one state to another; for selecting the intensity values of the game process transition from one state to another, based on the data on the estimated game capabilities of virtual tanks in a duel battle. Thus, game participants can use this model to conduct their own research. Developers of computer games can use it for setting up the game and setting such intensity values of the game process transition from one state to another, at which the gaming capabilities of virtual tanks will correspond to the combat capabilities of their real prototypes on the battlefield.Π ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΠΈΠ³Ρ, Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠΈΡ
Π±ΠΎΠΉ ΡΠ°Π½ΠΊΠΎΠ², ΠΌΠΎΠΆΠ½ΠΎ Π²ΡΠ΄Π΅Π»ΠΈΡΡ Π΄Π²Π΅ Π·Π°Π΄Π°ΡΠΈ: ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ ΠΈΠ³ΡΠΎΠ²ΡΡ
ΡΡΠ΅Π΄ΡΡΠ², ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΡΠΈΡ
ΡΠΎΠ±ΠΎΠΉ Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΡΠ΅ ΠΏΡΠΎΡΠΎΡΠΈΠΏΡ ΡΠ΅Π°Π»ΡΠ½ΡΡ
ΠΎΠ±ΡΠ°Π·ΡΠΎΠ² ΡΠ°Π½ΠΊΠΎΠ²; ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠΈ ΡΠ΅Π°Π»ΠΈΡΡΠΈΡΠ½ΠΎΡΡΠΈ ΠΈΠ³ΡΡ. ΠΠ»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΡΠΈΡ
Π·Π°Π΄Π°Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½Ρ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠΉ ΡΠΎΠΏΠΎΡΡΠ°Π²ΠΈΡΡ ΠΈΠ³ΡΠΎΠ²ΡΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΡΡ
ΠΌΠ°ΡΠΎΠΊ ΡΠ°Π½ΠΊΠΎΠ² Ρ Π±ΠΎΠ΅Π²ΡΠΌΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΠΌΠΈ ΠΈΡ
ΡΠ΅Π°Π»ΡΠ½ΡΡ
ΠΏΡΠΎΡΠΎΡΠΈΠΏΠΎΠ², Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΡΡ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΠΈΠ³ΡΡ, Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΡΡ Π΄ΡΡΠ»ΡΠ½ΡΠΉ Π±ΠΎΠΉ ΡΠ°Π½ΠΊΠΎΠ². Π£ΠΊΠ°Π·Π°Π½Π½Π°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΡΠ΄ΠΎΠ²Π»Π΅ΡΠ²ΠΎΡΡΠ΅Ρ ΡΠ»Π΅Π΄ΡΡΡΠΈΠΌ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡΠΌ: ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΡ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ, Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΡΡ
Π² ΠΌΠΎΠ΄Π΅Π»ΠΈ, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΠ΅Ρ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ, ΡΠ΅Π°Π»ΠΈΠ·ΡΠ΅ΠΌΡΡ
ΠΈΠ³ΡΠΎΠΊΠΎΠΌ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΈΠ³ΡΡ; ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π±ΠΎΠ΅ΠΏΡΠΈΠΏΠ°ΡΠΎΠ², ΠΊΠΎΡΠΎΡΠΎΠ΅ ΠΌΠΎΠΆΠ΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡΡΡ ΡΠ°Π½ΠΊΠΎΠΌ Π² ΠΌΠΎΠ΄Π΅Π»ΠΈ, Π΄ΠΎΠ»ΠΆΠ½ΠΎ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΎΠ²Π°ΡΡ ΡΠ°Π·ΠΌΠ΅ΡΡ Π±ΠΎΠ΅ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ° ΡΠ°Π½ΠΊΠ°. ΠΡΡΠ»Ρ ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠ°Π΅ΡΡΡ Π΄ΠΎ ΡΠ΅Ρ
ΠΏΠΎΡ, ΠΏΠΎΠΊΠ° Π½Π΅ Π±ΡΠ΄Π΅Ρ ΠΏΠΎΡΠ°ΠΆΡΠ½ ΠΎΠ΄ΠΈΠ½ ΠΈΠ· ΡΠ°Π½ΠΊΠΎΠ², ΠΈΠ»ΠΈ ΠΏΠΎΠΊΠ° Π½Π΅ Π±ΡΠ΄ΡΡ ΠΈΠ·ΡΠ°ΡΡ
ΠΎΠ΄ΠΎΠ²Π°Π½Ρ Π²ΡΠ΅ ΠΈΠΌΠ΅ΡΡΠΈΠ΅ΡΡ Π΄Π»Ρ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΏΡΠΎΡΠΈΠ²Π½ΠΈΠΊΠ° ΠΏΡΡΠ΅ΡΠ½ΡΠ΅ Π²ΡΡΡΡΠ΅Π»Ρ. ΠΠ΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ Π½Π°ΠΉΡΠΈ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΡ
ΠΈΡΡ
ΠΎΠ΄ΠΎΠ² Π΄ΡΡΠ»ΡΠ½ΠΎΠ³ΠΎ Π±ΠΎΡ, ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΎΠΆΠΈΠ΄Π°Π½ΠΈΠ΅ Π΅Π³ΠΎ ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ, ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΎΠΆΠΈΠ΄Π°Π½ΠΈΠ΅ ΡΠ°ΡΡ
ΠΎΠ΄Π° Π±ΠΎΠ΅ΠΏΡΠΈΠΏΠ°ΡΠΎΠ² ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΠΈΠ· ΡΡΠΎΡΠΎΠ½.Π Π΅ΡΠ΅Π½ΠΈΠ΅ Π·Π°Π΄Π°ΡΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΎ ΠΏΡΡΡΠΌ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΠΎ ΡΡ
Π΅ΠΌΠ΅ ΠΠ°ΡΠΊΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΡΠ»ΡΡΠ°ΠΉΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ° Ρ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΡΠΌΠΈ ΡΠΎΡΡΠΎΡΠ½ΠΈΡΠΌΠΈ ΠΈ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΡΠΌ Π²ΡΠ΅ΠΌΠ΅Π½Π΅ΠΌ. Π Π΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½ΠΎ Π² Π²ΠΈΠ΄Π΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π΄ΡΡΠ»ΡΠ½ΠΎΠ³ΠΎ Π±ΠΎΡ ΡΠ°Π½ΠΊΠΎΠ² ΠΈ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΎ ΠΏΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΠΈΠ³ΡΡ ΠΆΠ°Π½ΡΠ° ΡΠ°Π½ΠΊΠΎΠ²ΡΡ
ΡΠΈΠΌΡΠ»ΡΡΠΎΡΠΎΠ² Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈΠ³ΡΠΎΠ²ΡΡ
Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΡΡ
ΡΠ°Π½ΠΊΠΎΠ² Π² Π΄ΡΡΠ»ΡΠ½ΠΎΠΌ Π±ΠΎΡ ΠΏΠΎ Π΄Π°Π½Π½ΡΠΌ ΠΎ ΡΠ°Π·ΠΌΠ΅ΡΠ°Ρ
ΠΈΡ
Π±ΠΎΠ΅ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΎΠ² ΠΈ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΡΡ
ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π° ΠΈΠ³ΡΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΈΠ· ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ Π² Π΄ΡΡΠ³ΠΎΠ΅; Π΄Π»Ρ ΠΏΠΎΠ΄Π±ΠΎΡΠ° Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΠ΅ΠΉ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π° ΠΈΠ³ΡΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΈΠ· ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ Π² Π΄ΡΡΠ³ΠΎΠ΅, ΠΈΡΡ
ΠΎΠ΄Ρ ΠΈΠ· Π΄Π°Π½Π½ΡΡ
ΠΎ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»Π°Π³Π°Π΅ΠΌΡΡ
ΠΈΠ³ΡΠΎΠ²ΡΡ
Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΡ
Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΡΡ
ΡΠ°Π½ΠΊΠΎΠ² Π² Π΄ΡΡΠ»ΡΠ½ΠΎΠΌ Π±ΠΎΡ. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, Π΄Π°Π½Π½Π°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° ΡΡΠ°ΡΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΈΠ³ΡΡ Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ; ΡΠ°Π·ΡΠ°Π±ΠΎΡΡΠΈΠΊΠ°ΠΌΠΈ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΠΈΠ³Ρ, Π΄Π»Ρ Π½Π°ΡΡΡΠΎΠΉΠΊΠΈ ΠΈΠ³ΡΡ, Π·Π°Π΄Π°Π½ΠΈΡ ΡΠ°ΠΊΠΈΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΠ΅ΠΉ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π° ΠΈΠ³ΡΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΈΠ· ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ Π² Π΄ΡΡΠ³ΠΈΠ΅, ΠΏΡΠΈ ΠΊΠΎΡΠΎΡΡΡ
ΠΈΠ³ΡΠΎΠ²ΡΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΡΡ
ΡΠ°Π½ΠΊΠΎΠ², Π±ΡΠ΄ΡΡ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΎΠ²Π°ΡΡ Π±ΠΎΠ΅Π²ΡΠΌ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΠΌ ΠΈΡ
ΡΠ΅Π°Π»ΡΠ½ΡΡ
ΠΏΡΠΎΡΠΎΡΠΈΠΏΠΎΠ² Π½Π° ΠΏΠΎΠ»Π΅ Π±ΠΎΡ
Segmental paleotetraploidy revealed in sterlet (Acipenser ruthenus) genome by chromosome painting
Backgroun
ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ ΡΠΎΠ±ΡΡΠΈΠΉ ΠΈΠ· Π²ΠΎΠ΅Π½Π½ΠΎΠΉ ΠΈΡΡΠΎΡΠΈΠΈ
One of the ways of knowing the events of military history is to reproduce them using mathematical models. Based on the analysis of the fighting operations of the 4th Panzer Brigade of the Red Army in the vicinity of the city of Mtsensk in early October 1941, the capability to provide mathematical modeling of the fragments of these combat operations and the application of the apparatus of Markov random processes for these purposes is substantiated.The effectiveness of tanks depends not only on their technical properties, but also on the ways they are used on the battlefield. At the same time, combat effectiveness of tanks is commonly understood as their effectiveness in conditions when the methods of conducting combat operations by each of the opposing sides are the best.The battle outcome is probabilistic. It has certain regularity, depending on the combat tactics. The battle can be imagined as a multitude of randomly dueling fights between tanks, differing in their location and range of fire. A study of the probability of a system transition from each transient state to the next leads to the construction of mathematical models that allow calculating the ratio of losses of opposing sides.Based on the facts of military history and discovered regularities, the mathematical models are constructed to allow reproducing various fragments of combat according to the scheme of the Markov random process, and on their basis calculations are performed. The dependence of the ratio of the losses of the opposing sides depending on the number of firing positions used by the ambush tanks was established, provided that the change of these positions was made imperceptibly for the enemy.The obtained results can be used to develop tactical methods of using tanks in antiterrorist operations.ΠΠ΄ΠΈΠ½ ΠΈΠ· ΡΠΏΠΎΡΠΎΠ±ΠΎΠ² ΠΏΠΎΠ·Π½Π°Π½ΠΈΡ ΡΠΎΠ±ΡΡΠΈΠΉ Π²ΠΎΠ΅Π½Π½ΠΎΠΉ ΠΈΡΡΠΎΡΠΈΠΈ, ΡΠΎΡΡΠΎΠΈΡ Π² ΠΈΡ
Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π½Π°Π»ΠΈΠ·Π° Π±ΠΎΠ΅Π²ΡΡ
Π΄Π΅ΠΉΡΡΠ²ΠΈΠΉ 4 ΡΠ°Π½ΠΊΠΎΠ²ΠΎΠΉ Π±ΡΠΈΠ³Π°Π΄Ρ ΠΡΠ°ΡΠ½ΠΎΠΉ ΠΡΠΌΠΈΠΈ Π² ΡΠ°ΠΉΠΎΠ½Π΅ Π³ΠΎΡΠΎΠ΄Π° ΠΡΠ΅Π½ΡΠΊΠ° Π² Π½Π°ΡΠ°Π»Π΅ ΠΎΠΊΡΡΠ±ΡΡ 1941 Π³., ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠΎΠ² ΡΡΠΈΡ
Π±ΠΎΠ΅Π²ΡΡ
Π΄Π΅ΠΉΡΡΠ²ΠΈΠΉ ΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π΄Π»Ρ ΡΡΠΈΡ
ΡΠ΅Π»Π΅ΠΉ Π°ΠΏΠΏΠ°ΡΠ°ΡΠ° ΠΌΠ°ΡΠΊΠΎΠ²ΡΠΊΠΈΡ
ΡΠ»ΡΡΠ°ΠΉΠ½ΡΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ².ΠΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΡΠ°Π½ΠΊΠΎΠ² Π·Π°Π²ΠΈΡΠΈΡ Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ ΠΎΡ ΠΈΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²ΠΎΠΉΡΡΠ², Π½ΠΎ ΠΈ ΠΎΡ ΡΠΏΠΎΡΠΎΠ±ΠΎΠ² ΠΈΡ
ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π° ΠΏΠΎΠ»Π΅ Π±ΠΎΡ. ΠΡΠΈ ΡΡΠΎΠΌ ΠΏΠΎΠ΄ Π±ΠΎΠ΅Π²ΠΎΠΉ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡΡ ΡΠ°Π½ΠΊΠΎΠ² ΠΎΠ±ΡΡΠ½ΠΎ ΠΏΠΎΠ½ΠΈΠΌΠ°ΡΡ ΠΈΡ
ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
, ΠΊΠΎΠ³Π΄Π° ΡΠΏΠΎΡΠΎΠ±Ρ Π²Π΅Π΄Π΅Π½ΠΈΡ Π±ΠΎΠ΅Π²ΡΡ
Π΄Π΅ΠΉΡΡΠ²ΠΈΠΉ ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΠΈΠ· ΠΏΡΠΎΡΠΈΠ²ΠΎΠ΄Π΅ΠΉΡΡΠ²ΡΡΡΠΈΡ
ΡΡΠΎΡΠΎΠ½ ΡΠ²Π»ΡΡΡΡΡ Π½Π°ΠΈΠ»ΡΡΡΠΈΠΌΠΈ.Π Π΅Π·ΡΠ»ΡΡΠ°Ρ Π±ΠΎΡ β ΡΡΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΡΠΉ, ΠΈΠΌΠ΅ΡΡΠΈΠΉ Π½Π΅ΠΊΡΡ Π·Π°ΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΠ½ΠΎΡΡΡ, Π·Π°Π²ΠΈΡΡΡΡΡ ΠΎΡ ΡΠ°ΠΊΡΠΈΠΊΠΈ Π±ΠΎΠ΅Π²ΡΡ
Π΄Π΅ΠΉΡΡΠ²ΠΈΠΉ. ΠΠΎΠΉ ΠΌΠΎΠΆΠ½ΠΎ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΠΈΡΡ, ΠΊΠ°ΠΊ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²ΠΎ Π²ΠΎΠ·Π½ΠΈΠΊΠ°Π²ΡΠΈΡ
ΡΠ»ΡΡΠ°ΠΉΠ½ΡΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ Π΄ΡΡΠ»ΡΠ½ΡΡ
Π±ΠΎΠ΅Π² ΠΌΠ΅ΠΆΠ΄Ρ ΡΠ°Π½ΠΊΠ°ΠΌΠΈ, ΡΠ°Π·Π»ΠΈΡΠ°Π²ΡΠΈΡ
ΡΡ ΠΏΠΎ ΠΌΠ΅ΡΡΡ ΠΈΡ
ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΈ Π΄Π°Π»ΡΠ½ΠΎΡΡΡΠΌ Π²Π΅Π΄Π΅Π½ΠΈΡ ΠΎΠ³Π½Ρ. ΠΠ·ΡΡΠ΅Π½ΠΈΠ΅ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠΈ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π° ΡΠΈΡΡΠ΅ΠΌΡ ΠΈΠ· ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ Π½Π΅Π²ΠΎΠ·Π²ΡΠ°ΡΠ½ΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ Π² ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅, ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΡ
ΡΠ°ΡΡΡΠΈΡΠ°ΡΡ ΡΠΎΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠ΅ ΠΏΠΎΡΠ΅ΡΡ ΠΏΡΠΎΡΠΈΠ²ΠΎΠ΄Π΅ΠΉΡΡΠ²ΡΡΡΠΈΡ
ΡΡΠΎΡΠΎΠ½.ΠΠΏΠΈΡΠ°ΡΡΡ Π½Π° ΡΠ°ΠΊΡΡ Π²ΠΎΠ΅Π½Π½ΠΎΠΉ ΠΈΡΡΠΎΡΠΈΠΈ ΠΈ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½Π½ΡΠ΅ Π·Π°ΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΠ½ΠΎΡΡΠΈ, ΠΏΠΎΡΡΡΠΎΠ΅Π½Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠ΅ Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²Π΅ΡΡΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΡ Π±ΠΎΡ ΠΏΠΎ ΡΡ
Π΅ΠΌΠ΅ ΠΌΠ°ΡΠΊΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΡΠ»ΡΡΠ°ΠΉΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ°, ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Ρ ΡΠ°ΡΡΠ΅ΡΡ ΠΏΠΎ Π½ΠΈΠΌ. Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Π° Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΡ ΡΠΎΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΏΠΎΡΠ΅ΡΡ ΠΏΡΠΎΡΠΈΠ²ΠΎΠ΄Π΅ΠΉΡΡΠ²ΡΡΡΠΈΡ
ΡΡΠΎΡΠΎΠ½ Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° ΠΎΠ³Π½Π΅Π²ΡΡ
ΠΏΠΎΠ·ΠΈΡΠΈΠΉ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π½ΡΡ
ΡΠ°Π½ΠΊΠ°ΠΌΠΈ, ΡΡΠΎΡΠ²ΡΠΈΠΌ Π² Π·Π°ΡΠ°Π΄Π΅, ΠΏΡΠΈ ΡΡΠ»ΠΎΠ²ΠΈΠΈ, ΡΡΠΎ ΡΠΌΠ΅Π½Π° ΡΡΠΈΡ
ΠΏΠΎΠ·ΠΈΡΠΈΠΉ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠ»Π°ΡΡ Π½Π΅Π·Π°ΠΌΠ΅ΡΠ½ΠΎ Π΄Π»Ρ ΠΏΡΠΎΡΠΈΠ²Π½ΠΈΠΊΠ°.ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ ΠΏΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ΅ΠΌΠΎΠ² ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ°Π½ΠΊΠΎΠ² Π² Π°Π½ΡΠΈΡΠ΅ΡΡΠΎΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΡΡ
ΠΠ½Π°Π»ΠΈΠ· ΡΠΎΠ±ΡΡΠΈΠΉ ΠΈΠ· ΠΈΡΡΠΎΡΠΈΠΈ ΠΠ΅Π»ΠΈΠΊΠΎΠΉ ΠΡΠ΅ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π²ΠΎΠΉΠ½Ρ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ
When fighting against terrorism in modern armed conflicts, combat vehicles, including tanks, are widely used. To minimise own losses of vehicles and personnel for overthrowing enemy is a relevant task. To solve it, the paper considers certain events in the history of the Great Patriotic War, which are associated with battle of tanks that spring an ambush. A mathematical model of the battle is built. The state graph of the system is given. Using this graph, a probability of tank kills and a ratio of mathematical expectations of losses have been calculated. This mathematical model generalizes the models, previously published in this journal, based on the Markov chain apparatus. The paper gives an example of calculations for this model in the particular case in which experimental data are used as a basis. The ratios of mathematical expectations of losses of the warring parties are obtained. Further, we consider the mathematical models, in which it is assumed that probabilities for tank crews to provide operations of targets detection in firing are known. With technology development and its mathematical support it becomes increasingly more real. The formulas to obtain the probability of tank kills are given according to the graph of states using the known probabilities of transition from one state to another. In each of the three mathematical models under consideration there is a graph of the system state, which allows calculation of the tank kills probability. We have analysed the models to prove a significant dependence of the loss ratio of the warring parties on the number of firing positions used by the tank in ambush in case re-siting is unnoticeable for the enemy. The authors-considered models that use the examples of historical events confirm that the tactics of organising and conducting ambushes in tank battles can be successfully used nowadays, when the technology intensiveness of the opposing forces significantly grows. The obtained results can be applied to organise and conduct tank ambushes in modern armed conflicts and fight against terrorist army.ΠΡΠΈ Π±ΠΎΡΡΠ±Π΅ Ρ ΡΠ΅ΡΡΠΎΡΠΈΠ·ΠΌΠΎΠΌ Π² ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
Π²ΠΎΠΎΡΡΠΆΠ΅Π½Π½ΡΡ
ΠΊΠΎΠ½ΡΠ»ΠΈΠΊΡΠ°Ρ
ΡΠΈΡΠΎΠΊΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ Π±ΠΎΠ΅Π²ΡΠ΅ ΠΌΠ°ΡΠΈΠ½Ρ, ΠΈ Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ ΡΠ°Π½ΠΊΠΈ. ΠΠΊΡΡΠ°Π»Π΅Π½ Π²ΠΎΠΏΡΠΎΡ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΡΡ
ΠΏΠΎΡΠ΅ΡΡ Π΅Π΄ΠΈΠ½ΠΈΡ ΡΠ΅Ρ
Π½ΠΈΠΊΠΈ ΠΈ Π»ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π° ΠΏΡΠΈ ΡΠ°Π·Π³ΡΠΎΠΌΠ΅ ΠΏΡΠΎΡΠΈΠ²Π½ΠΈΠΊΠ°. ΠΠ»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΡΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ Π² ΡΡΠ°ΡΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΠ΅ ΡΠΎΠ±ΡΡΠΈΡ ΠΈΡΡΠΎΡΠΈΠΈ ΠΠ΅Π»ΠΈΠΊΠΎΠΉ ΠΡΠ΅ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π²ΠΎΠΉΠ½Ρ, ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠ²ΡΠ·Π°Π½Ρ Ρ Π΄Π΅ΠΉΡΡΠ²ΠΈΡΠΌΠΈ ΡΠ°Π½ΠΊΠΎΠ² ΠΈΠ· Π·Π°ΡΠ°Π΄. ΠΠΎΡΡΡΠΎΠ΅Π½Π° ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ Π±ΠΎΡ. ΠΡΠΈΠ²Π΅Π΄Π΅Π½ Π³ΡΠ°Ρ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ. ΠΡΠΏΠΎΠ»ΡΠ·ΡΡ ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Π½ΡΠΉ Π³ΡΠ°Ρ, ΡΠ°ΡΡΡΠΈΡΠ°Π½Ρ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ ΡΠ°Π½ΠΊΠΎΠ² ΠΈ ΡΠΎΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΠΆΠΈΠ΄Π°Π½ΠΈΠΉ ΠΏΠΎΡΠ΅ΡΡ. ΠΠ°Π½Π½Π°Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΎΠ±ΠΎΠ±ΡΠ°Π΅Ρ ΡΠ°Π½Π΅Π΅ ΠΎΠΏΡΠ±Π»ΠΈΠΊΠΎΠ²Π°Π½Π½ΡΠ΅ Π² Π΄Π°Π½Π½ΠΎΠΌ ΠΆΡΡΠ½Π°Π»Π΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ, ΠΏΠΎΡΡΡΠΎΠ΅Π½Π½ΡΠ΅ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π°ΠΏΠΏΠ°ΡΠ°ΡΠ° ΡΠ΅ΠΏΠ΅ΠΉ ΠΠ°ΡΠΊΠΎΠ²Π°. ΠΡΠΈΠ²Π΅Π΄Π΅Π½ ΠΏΡΠΈΠΌΠ΅Ρ ΡΠ°ΡΡΡΡΠΎΠ² ΠΏΠΎ ΡΡΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π² ΡΠ°ΡΡΠ½ΠΎΠΌ ΡΠ»ΡΡΠ°Π΅, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ Π·Π° ΠΎΡΠ½ΠΎΠ²Ρ Π²Π·ΡΡΡ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅. ΠΠΎΠ»ΡΡΠ΅Π½Ρ ΡΠΎΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΠΆΠΈΠ΄Π°Π½ΠΈΠΉ ΠΏΠΎΡΠ΅ΡΡ ΠΏΡΠΎΡΠΈΠ²ΠΎΠ±ΠΎΡΡΡΠ²ΡΡΡΠΈΡ
ΡΡΠΎΡΠΎΠ½. ΠΠ°Π»Π΅Π΅ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ, Π² ΠΊΠΎΡΠΎΡΡΡ
ΠΏΡΠ΅Π΄ΠΏΠΎΠ»Π°Π³Π°ΡΡΡΡ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΠΌΠΈ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΡ ΡΠΊΠΈΠΏΠ°ΠΆΠ°ΠΌΠΈ ΡΠ°Π½ΠΊΠΎΠ² ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ ΠΏΠΎ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΡΠ΅Π»Π΅ΠΉ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΡΡΡΠ΅Π»ΡΠ±Ρ. Π‘ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ΠΌ ΡΠ΅Ρ
Π½ΠΈΠΊΠΈ ΠΈ Π΅Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠΈ ΡΡΠΎ ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡΡ Π²ΡΠ΅ Π±ΠΎΠ»Π΅Π΅ ΡΠ΅Π°Π»ΡΠ½ΡΠΌ. ΠΠΎ ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Π½ΠΎΠΌΡ Π² Π³ΡΠ°ΡΡ ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΡ
Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ΅ΠΉ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π° ΠΈΠ· ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ Π² Π΄ΡΡΠ³ΠΎΠ΅, ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΡΠΎΡΠΌΡΠ»Ρ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ ΡΠ°Π½ΠΊΠΎΠ². Π ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΠΈΠ· ΡΡΠ΅Ρ
ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Π½ΡΡ
ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½ Π³ΡΠ°Ρ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠΉ Π²ΡΡΠΈΡΠ»ΠΈΡΡ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ ΡΠ°Π½ΠΊΠΎΠ². ΠΡΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ, ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡΠΈΠΉ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΡ ΡΠΎΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΏΠΎΡΠ΅ΡΡ ΠΏΡΠΎΡΠΈΠ²ΠΎΠ΄Π΅ΠΉΡΡΠ²ΡΡΡΠΈΡ
ΡΡΠΎΡΠΎΠ½ ΠΎΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° ΠΎΠ³Π½Π΅Π²ΡΡ
ΠΏΠΎΠ·ΠΈΡΠΈΠΉ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ ΡΠ°Π½ΠΊΠΎΠΌ Π² Π·Π°ΡΠ°Π΄Π΅ Π² ΡΠ»ΡΡΠ°Π΅, Π΅ΡΠ»ΠΈ ΡΠΌΠ΅Π½Π° ΡΡΠΈΡ
ΠΏΠΎΠ·ΠΈΡΠΈΠΉ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΡΡ Π½Π΅Π·Π°ΠΌΠ΅ΡΠ½ΠΎ Π΄Π»Ρ ΠΏΡΠΎΡΠΈΠ²Π½ΠΈΠΊΠ°. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Π½ΡΠ΅ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ Π² ΡΡΠ°ΡΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ°Ρ
ΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΎΠ±ΡΡΠΈΠΉ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡ, ΡΡΠΎ ΡΠ°ΠΊΡΠΈΠΊΠ° ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ ΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π·Π°ΡΠ°Π΄ ΠΏΡΠΈ Π²Π΅Π΄Π΅Π½ΠΈΠΈ ΡΠ°Π½ΠΊΠΎΠ²ΡΡ
Π±ΠΎΠ΅Π², ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΡΡΠΏΠ΅ΡΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° ΠΈ Π² Π½Π°ΡΠ΅ Π²ΡΠ΅ΠΌΡ, ΠΊΠΎΠ³Π΄Π° Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π²ΡΡΠ°ΡΡΠ°Π΅Ρ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΎΡΠ½Π°ΡΠ΅Π½Π½ΠΎΡΡΡ ΠΏΡΠΎΡΠΈΠ²ΠΎΠ±ΠΎΡΡΡΠ²ΡΡΡΠΈΡ
ΡΠΈΠ». ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½Ρ ΠΏΡΠΈ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ ΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΡΠ°Π½ΠΊΠΎΠ²ΡΡ
Π·Π°ΡΠ°Π΄ Π² ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
Π²ΠΎΠΎΡΡΠΆΠ΅Π½Π½ΡΡ
ΠΊΠΎΠ½ΡΠ»ΠΈΠΊΡΠ°Ρ
ΠΈ Π±ΠΎΡΡΠ±Π΅ Ρ ΡΠ΅ΡΡΠΎΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡΠΌΠΈ
LINE-1 retrotransposon methylation in chorionic villi of first trimester miscarriages with aneuploidy
Purpose High frequency of aneuploidy in meiosis and cleavage stage coincides with waves of epigenetic genome reprogramming that may indicate a possible association between epigenetic mechanisms and aneuploidy occurrence. This study aimed to assess the methylation level of the long interspersed repeat element 1 (LINE-1) retrotransposon in chorionic villi of first trimester miscarriages with a normal karyotype and aneuploidy. Methods The methylation level was assessed at 19 LINE-1 promoter CpG sites in chorionic villi of 141 miscarriages with trisomy of chromosomes 2, 6, 8-10, 13-15, 16, 18, 20-22, and monosomy X using massive parallel sequencing. Results The LINE-1 methylation level was elevated statistically significant in chorionic villi of miscarriages with both trisomy (45.2 +/- 4.3%) and monosomy X (46.9 +/- 4.2%) compared with that in induced abortions (40.0 +/- 2.4%) (p < 0.00001). The LINE-1 methylation levels were specific for miscarriages with different aneuploidies and significantly increased in miscarriages with trisomies 8, 14, and 18 and monosomy X (p < 0.05). The LINE-1 methylation level increased with gestational age both for group of miscarriages regardless of karyotype (R = 0.21, p = 0.012) and specifically for miscarriages with trisomy 16 (R = 0.48, p = 0.007). LINE-1 methylation decreased with maternal age in miscarriages with a normal karyotype (R = - 0.31, p = 0.029) and with trisomy 21 (R = - 0.64, p = 0.024) and increased with paternal age for miscarriages with trisomy 16 (R = 0.38, p = 0.048) and monosomy X (R = 0.73, p = 0.003). Conclusion Our results indicate that the pathogenic effects of aneuploidy in human embryogenesis can be supplemented with significant epigenetic changes in the repetitive sequences
The inverse problem for a discrete elliptic equation with prescribed symmetry conditions
Mathematical Single-Player Computer Game Model to Reproduce Duel Fight of Tanks
In improving computer games, which reproduce a battle of tanks, two tasks can be distinguished: increasing a collection of game tools to represent virtual prototypes of real tank models and ensuring a realistic game. To solve these problems, a tool is necessary that allows us to compare gaming capabilities of virtual tank brands with combat capabilities of their real prototypes. A mathematical model of a computer game that reproduces a duel battle of tanks can be used as the tool. The specified model satisfies the following requirements: the sequence of operations reproduced in the model is in line with the sequence of operations implemented by the player in the course of the game; the maximum amount of ammunition that a tank can use in a model must correspond to the amount of tank ammunition. The duel lasts until one of the tanks is hit, or until all the gunshots available to hit the enemy are expended. It is necessary to find the probabilities of possible outcomes of a duel battle, the mathematical expectation of its duration, the mathematical expectation of the ammunition consumption of each side.The solution to the problem is obtained by constructing a mathematical model according to the scheme of Markov random process with discrete states and continuous time. It is implemented as a program for a model of a duel battle of tanks and can be used when developing a computer game of the genre of tank simulators to assess the gaming capabilities of the virtual tanks in a duel battle from the data on the amount of their ammunition and on the intensity of the game process transition from one state to another; for selecting the intensity values of the game process transition from one state to another, based on the data on the estimated game capabilities of virtual tanks in a duel battle. Thus, game participants can use this model to conduct their own research. Developers of computer games can use it for setting up the game and setting such intensity values of the game process transition from one state to another, at which the gaming capabilities of virtual tanks will correspond to the combat capabilities of their real prototypes on the battlefield
Mathematical Modeling-based Analysis from the Great Patriotic War Events
When fighting against terrorism in modern armed conflicts, combat vehicles, including tanks, are widely used. To minimise own losses of vehicles and personnel for overthrowing enemy is a relevant task. To solve it, the paper considers certain events in the history of the Great Patriotic War, which are associated with battle of tanks that spring an ambush. A mathematical model of the battle is built. The state graph of the system is given. Using this graph, a probability of tank kills and a ratio of mathematical expectations of losses have been calculated. This mathematical model generalizes the models, previously published in this journal, based on the Markov chain apparatus. The paper gives an example of calculations for this model in the particular case in which experimental data are used as a basis. The ratios of mathematical expectations of losses of the warring parties are obtained. Further, we consider the mathematical models, in which it is assumed that probabilities for tank crews to provide operations of targets detection in firing are known. With technology development and its mathematical support it becomes increasingly more real. The formulas to obtain the probability of tank kills are given according to the graph of states using the known probabilities of transition from one state to another. In each of the three mathematical models under consideration there is a graph of the system state, which allows calculation of the tank kills probability. We have analysed the models to prove a significant dependence of the loss ratio of the warring parties on the number of firing positions used by the tank in ambush in case re-siting is unnoticeable for the enemy. The authors-considered models that use the examples of historical events confirm that the tactics of organising and conducting ambushes in tank battles can be successfully used nowadays, when the technology intensiveness of the opposing forces significantly grows. The obtained results can be applied to organise and conduct tank ambushes in modern armed conflicts and fight against terrorist army
Using Mathematical Models in Event Analysis from Military History
One of the ways of knowing the events of military history is to reproduce them using mathematical models. Based on the analysis of the fighting operations of the 4th Panzer Brigade of the Red Army in the vicinity of the city of Mtsensk in early October 1941, the capability to provide mathematical modeling of the fragments of these combat operations and the application of the apparatus of Markov random processes for these purposes is substantiated.The effectiveness of tanks depends not only on their technical properties, but also on the ways they are used on the battlefield. At the same time, combat effectiveness of tanks is commonly understood as their effectiveness in conditions when the methods of conducting combat operations by each of the opposing sides are the best.The battle outcome is probabilistic. It has certain regularity, depending on the combat tactics. The battle can be imagined as a multitude of randomly dueling fights between tanks, differing in their location and range of fire. A study of the probability of a system transition from each transient state to the next leads to the construction of mathematical models that allow calculating the ratio of losses of opposing sides.Based on the facts of military history and discovered regularities, the mathematical models are constructed to allow reproducing various fragments of combat according to the scheme of the Markov random process, and on their basis calculations are performed. The dependence of the ratio of the losses of the opposing sides depending on the number of firing positions used by the ambush tanks was established, provided that the change of these positions was made imperceptibly for the enemy.The obtained results can be used to develop tactical methods of using tanks in antiterrorist operations