813 research outputs found
Collection of Mutually Synchronized Chaotic Systems
A general explicit coupling for mutual synchronization of two arbitrary
identical continuous systems is proposed. The synchronization is proved
analytically. The coupling is given for all 19 systems from Sprott's
collection. For one of the systems the numerical results are shown in detail.
The method could be adopted for the teaching of the topic.Comment: Published in Physics Letters A 352 (2006) 222-22
Monte Carlo Methods for Process Algebra
AbstractWe review the recently developed technique of Monte Carlo model checking and show how it can be applied to the implementation problem for I/O Automata. We then consider some open problems in applying Monte Carlo techniques to other process-algebraic problems, such as simulation and bisimulation
ΠΠ°ΡΠΊΠ΅ΡΡ ΡΠ½Π΄ΠΎΡΠ΅Π»ΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π΄ΠΈΡΡΡΠ½ΠΊΡΠΈΠΈ ΠΈ Π²ΠΎΡΠΏΠ°Π»Π΅Π½ΠΈΡ ΠΏΡΠΈ ΠΈΠ΄ΠΈΠΎΠΏΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΡΠΎΠΌΠ±ΠΎΠ·Π΅ Π³Π»ΡΠ±ΠΎΠΊΠΈΡ Π²Π΅Π½
Idiopathic deep vein thrombosis is a process of venous thrombosis with the absence of challenging factors (thrombophilia, cancer, immobilization,
trauma, surgical intervention etc.). In the present study, the admission values of the endothelium dysfunction and inflammation markers have been
assayed in 78 patients with idiopathic deep vein thrombosis. The nitric oxide circulating level was reduced by 56.3% that correlated with a flow mediated
brachial artery dilation decrease by 47% and with a carotid artery intima-media thickness rise by 77.6%. Likewise, an evident activation of inflammatory
response has been attested that was manifested by an increase in serum levels of C reactive protein and IL-6 by 3.14 and 4.7 times. Thus, the shifting
markers indicate a pathogenic role of endothelium dysfunction and inflammation in idiopathic deep vein thrombosis evolution, yet, on the other hand,
they can be used as diagnostic and prognostic predictors of the malady.ΠΠ΄ΠΈΠΎΠΏΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΡΠΎΠΌΠ±ΠΎΠ· Π³Π»ΡΠ±ΠΎΠΊΠΈΡ
Π²Π΅Π½ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΡΠΎΠ±ΠΎΠΉ ΠΏΡΠΎΡΠ΅ΡΡ Π²Π΅Π½ΠΎΠ·Π½ΠΎΠ³ΠΎ ΡΡΠΎΠΌΠ±ΠΎΠ·Π° Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΎΡΡΡΡΡΡΠ²ΠΈΡ ΠΏΡΠΎΠ²ΠΎΡΠΈΡΡΡΡΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ²
(ΡΡΠΎΠΌΠ±ΠΎΡΠΈΠ»ΠΈΡ, ΠΎΠΏΡΡ
ΠΎΠ»Ρ, ΠΈΠΌΠΌΠΎΠ±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΡ, ΡΡΠ°Π²ΠΌΠ°, Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ΅ Π²ΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΡΡΠ²ΠΎ ΠΈ Π΄Ρ.). Π Π΄Π°Π½Π½ΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»ΠΎΡΡ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ²
ΡΠ½Π΄ΠΎΡΠ΅Π»ΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π΄ΠΈΡΡΡΠ½ΠΊΡΠΈΠΈ ΠΈ Π²ΠΎΡΠΏΠ°Π»Π΅Π½ΠΈΡ Ρ 78 Π±ΠΎΠ»ΡΠ½ΡΡ
Ρ ΠΈΠ΄ΠΈΠΎΠΏΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΡΠΎΠΌΠ±ΠΎΠ·Π΅ Π³Π»ΡΠ±ΠΎΠΊΠΈΡ
Π²Π΅Π½ ΠΏΡΠΈ ΠΏΠΎΡΡΡΠΏΠ»Π΅Π½ΠΈΠΈ. Π¦ΠΈΡΠΊΡΠ»ΠΈΡΡΡΡΠΈΠΉ ΡΡΠΎΠ²Π΅Π½Ρ
ΠΎΠΊΠΈΡΠΈ Π°Π·ΠΎΡΠ° Π±ΡΠ» ΡΠ½ΠΈΠΆΠ΅Π½ Π½Π° 56,3%, ΡΡΠΎ ΠΊΠΎΡΡΠ΅Π»ΠΈΡΠΎΠ²Π°Π»ΠΎ Ρ ΡΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΠ΅ΠΌ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΡ ΠΏΠ»Π΅ΡΠ΅Π²ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠΈ Π½Π° 47% ΠΈ Ρ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ΠΌ
ΡΠΎΠ»ΡΠΈΠ½Ρ ΠΈΠ½ΡΠΈΠΌΡ-ΠΌΠ΅Π΄ΠΈΠΈ ΡΠΎΠ½Π½ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠΈ Π½Π° 77,6%. Π’Π°ΠΊΠΆΠ΅ Π²ΡΡΠ²Π»Π΅Π½Π° Π²ΡΡΠ°ΠΆΠ΅Π½Π½Π°Ρ Π°ΠΊΡΠΈΠ²Π°ΡΠΈΡ Π²ΠΎΡΠΏΠ°Π»ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΡΠ²Π΅ΡΠ°, ΡΡΠΎ ΡΠΎΠΏΡΠΎΠ²ΠΎΠΆΠ΄Π°Π»ΠΎΡΡ
ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ΠΌ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ Π² ΠΊΡΠΎΠ²ΠΈ Π‘-ΡΠ΅Π°ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π±Π΅Π»ΠΊΠ° ΠΈ ΠΈΠ½ΡΠ΅ΡΠ»Π΅ΠΉΠΊΠΈΠ½Π°-6. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΡΠΊΠ°Π·ΡΠ²Π°Π΅Ρ Π½Π° ΠΏΠ°ΡΠΎΠ³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΡΡ
ΡΠΎΠ»Ρ ΡΠ½Π΄ΠΎΡΠ΅Π»ΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π΄ΠΈΡΡΡΠ½ΠΊΡΠΈΠΈ ΠΈ Π²ΠΎΡΠΏΠ°Π»Π΅Π½ΠΈΡ Π² ΡΠ°Π·Π²ΠΈΡΠΈΠΈ ΠΈΠ΄ΠΈΠΎΠΏΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΡΠΎΠΌΠ±ΠΎΠ·Π° Π³Π»ΡΠ±ΠΎΠΊΠΈΡ
Π²Π΅Π½, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΈΡ
ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ
Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΎΡΠΎΠ² Π±ΠΎΠ»Π΅Π·Π½ΠΈ
Model Checking Tap Withdrawal in C. Elegans
We present what we believe to be the first formal verification of a
biologically realistic (nonlinear ODE) model of a neural circuit in a
multicellular organism: Tap Withdrawal (TW) in \emph{C. Elegans}, the common
roundworm. TW is a reflexive behavior exhibited by \emph{C. Elegans} in
response to vibrating the surface on which it is moving; the neural circuit
underlying this response is the subject of this investigation. Specifically, we
perform reachability analysis on the TW circuit model of Wicks et al. (1996),
which enables us to estimate key circuit parameters. Underlying our approach is
the use of Fan and Mitra's recently developed technique for automatically
computing local discrepancy (convergence and divergence rates) of general
nonlinear systems. We show that the results we obtain are in agreement with the
experimental results of Wicks et al. (1995). As opposed to the fixed parameters
found in most biological models, which can only produce the predominant
behavior, our techniques characterize ranges of parameters that produce (and do
not produce) all three observed behaviors: reversal of movement, acceleration,
and lack of response
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