1,599 research outputs found
Improved linear response for stochastically driven systems
The recently developed short-time linear response algorithm, which predicts
the average response of a nonlinear chaotic system with forcing and dissipation
to small external perturbation, generally yields high precision of the response
prediction, although suffers from numerical instability for long response times
due to positive Lyapunov exponents. However, in the case of stochastically
driven dynamics, one typically resorts to the classical fluctuation-dissipation
formula, which has the drawback of explicitly requiring the probability density
of the statistical state together with its derivative for computation, which
might not be available with sufficient precision in the case of complex
dynamics (usually a Gaussian approximation is used). Here we adapt the
short-time linear response formula for stochastically driven dynamics, and
observe that, for short and moderate response times before numerical
instability develops, it is generally superior to the classical formula with
Gaussian approximation for both the additive and multiplicative stochastic
forcing. Additionally, a suitable blending with classical formula for longer
response times eliminates numerical instability and provides an improved
response prediction even for long response times
Cross sections for geodesic flows and \alpha-continued fractions
We adjust Arnoux's coding, in terms of regular continued fractions, of the
geodesic flow on the modular surface to give a cross section on which the
return map is a double cover of the natural extension for the \alpha-continued
fractions, for each in (0,1]. The argument is sufficiently robust to
apply to the Rosen continued fractions and their recently introduced
\alpha-variants.Comment: 20 pages, 2 figure
Multilevel Analysis of Oscillation Motions in Active Regions of the Sun
We present a new method that combines the results of an oscillation study
made in optical and radio observations. The optical spectral measurements in
photospheric and chromospheric lines of the line-of-sight velocity were carried
out at the Sayan Solar Observatory. The radio maps of the Sun were obtained
with the Nobeyama Radioheliograph at 1.76 cm. Radio sources associated with the
sunspots were analyzed to study the oscillation processes in the
chromosphere-corona transition region in the layer with magnetic field B=2000
G. A high level of instability of the oscillations in the optical and radio
data was found. We used a wavelet analysis for the spectra. The best
similarities of the spectra of oscillations obtained by the two methods were
detected in the three-minute oscillations inside the sunspot umbra for the
dates when the active regions were situated near the center of the solar disk.
A comparison of the wavelet spectra for optical and radio observations showed a
time delay of about 50 seconds of the radio results with respect to optical
ones. This implies a MHD wave traveling upward inside the umbral magnetic tube
of the sunspot. Besides three-minute and five-minute ones, oscillations with
longer periods (8 and 15 minutes) were detected in optical and radio records.Comment: 17 pages, 9 figures, accepted to Solar Physics (18 Jan 2011). The
final publication is available at http://www.springerlink.co
Natural extensions and entropy of -continued fractions
We construct a natural extension for each of Nakada's -continued
fractions and show the continuity as a function of of both the entropy
and the measure of the natural extension domain with respect to the density
function . In particular, we show that, for all , the product of the entropy with the measure of the domain equals .
As a key step, we give the explicit relationship between the -expansion
of and of
Multi-level Dynamical Systems: Connecting the Ruelle Response Theory and the Mori-Zwanzig Approach
In this paper we consider the problem of deriving approximate autonomous
dynamics for a number of variables of a dynamical system, which are weakly
coupled to the remaining variables. In a previous paper we have used the Ruelle
response theory on such a weakly coupled system to construct a surrogate
dynamics, such that the expectation value of any observable agrees, up to
second order in the coupling strength, to its expectation evaluated on the full
dynamics. We show here that such surrogate dynamics agree up to second order to
an expansion of the Mori-Zwanzig projected dynamics. This implies that the
parametrizations of unresolved processes suited for prediction and for the
representation of long term statistical properties are closely related, if one
takes into account, in addition to the widely adopted stochastic forcing, the
often neglected memory effects.Comment: 14 pages, 1 figur
ΠΠ»ΠΈΡΠ½ΠΈΠ΅ ΠΈΠ½Π΄ΠΎΠ»-3-ΠΌΠ°ΡΠ»ΡΠ½ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡΡ ΠΈ Ρ Π»ΠΎΡΠΌΠ΅ΠΊΠ²Π°ΡΠ° Ρ Π»ΠΎΡΠΈΠ΄Π° Π½Π° ΡΠΎΡΡ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ ΡΠΎΠΌΠ°ΡΠ°
Relevance. This study aimed to improve fruit set and plant performance to increase tomato productivity by studying the effect of plant growth regulators on tomato plants.Methods. A specific experiment has been carried out to study the effect of plant growth regulators Milagro (1% indol-3-butric acid) and Atlet active substances indol-3-butric acid and chloromequate chloride with applied doses (0.6 M/L, 1.0 M/L and 1.5 M/L) and (1.0, 1.5 and 2.0 M/L) on growth and physiological characteristics of plants (Big Beef F1). The experimental design was a Complete Randomized Blocks Design. Both Hemo bles was applied three times (spraying on plants at 30 DAP, spraying on plants at 60 DAP and spraying on plants 90 DAP).Results. The obtained results showed that, Applying Milagro (1% indol-3-butric acid) had the highest significant Plant height (80.13, 128.77 and 239 cm), number of leaves/plant (18.0, 34.67 and 44.3) and stem diameter (1.07, 1.5 and 2.03 cm), fruit weight (122.0 and 136 g), Flower Clusters number in the plant (4.64, 13.33 and 16.33) and Fruits Number (61.67, 62.0 and 67) Over the three years of study. The results were analyzed using one-way analysis of variance (ANOVA) followed by Tukeyβs HSD test with Ξ± = 0.05 with the help of MINITAB (v. 19.0) program.ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π±ΡΠ»ΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΎ Π½Π° ΡΠ»ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΏΠ»ΠΎΠ΄ΠΎΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΈ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ Π΄Π»Ρ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ ΡΡΠΎΠΆΠ°ΠΉΠ½ΠΎΡΡΠΈ ΡΠΎΠΌΠ°ΡΠΎΠ² ΠΏΡΡΠ΅ΠΌ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ Π²Π»ΠΈΡΠ½ΠΈΡ ΡΠ΅Π³ΡΠ»ΡΡΠΎΡΠΎΠ² ΡΠΎΡΡΠ° ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ.ΠΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½Ρ ΠΏΠΎ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ Π²Π»ΠΈΡΠ½ΠΈΡ ΡΠ΅Π³ΡΠ»ΡΡΠΎΡΠΎΠ² ΡΠΎΡΡΠ° ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ ΠΠΈΠ»Π°Π³ΡΠΎ (1% ΠΈΠ½Π΄ΠΎΠ»-3-ΠΌΠ°ΡΠ»ΡΠ½Π°Ρ ΠΊΠΈΡΠ»ΠΎΡΠ°) ΠΈ Π°ΠΊΡΠΈΠ²Π½ΡΡ
Π²Π΅ΡΠ΅ΡΡΠ² ΠΡΠ»Π΅Ρ β ΠΈΠ½Π΄ΠΎΠ»-3-ΠΌΠ°ΡΠ»ΡΠ½ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡΡ ΠΈ Ρ
Π»ΠΎΡΠΌΠ΅ΠΊΠ²Π°ΡΠ° Ρ
Π»ΠΎΡΠΈΠ΄Π°, ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΡ
Π² Π΄ΠΎΠ·Π°Ρ
(0,6 ΠΌ/Π», 1,0 ΠΌ/Π») ΠΈ 1,5 ΠΌ/Π») ΠΈ (1,0, 1,5 ΠΈ 2,0 ΠΌ/Π») ΠΏΠΎ ΡΠΎΡΡΡ ΠΈ ΡΠΈΠ·ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ (Big Beef F1). ΠΠ±Π° ΡΠ΅Π³ΡΠ»ΡΡΠΎΡΠ° ΡΠΎΡΡΠ° Π½Π°Π½ΠΎΡΠΈΠ»ΠΈ ΡΡΠΈ ΡΠ°Π·Π° (ΠΎΠΏΡΡΡΠΊΠΈΠ²Π°Π½ΠΈΠ΅ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ 30 DAP, ΠΎΠΏΡΡΡΠΊΠΈΠ²Π°Π½ΠΈΠ΅ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ 60 DAP ΠΈ ΠΎΠΏΡΡΡΠΊΠΈΠ²Π°Π½ΠΈΠ΅ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ 90 DAP).Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΠΈΠ»Π°Π³ΡΠΎ (1% ΠΈΠ½Π΄ΠΎΠ»-3-ΠΌΠ°ΡΠ»ΡΠ½ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡΡ) ΠΎΠΊΠ°Π·Π°Π»ΠΎ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΠ΅Π΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° Π²ΡΡΠΎΡΡ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ (80,13, 128,77 ΠΈ 239 ΡΠΌ), ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π»ΠΈΡΡΡΠ΅Π² / ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ (18,0, 34,67 ΠΈ 44,3) ΠΈ Π΄ΠΈΠ°ΠΌΠ΅ΡΡ ΡΡΠ΅Π±Π»Ρ (1,07, 1,5 ΠΈ 2,03 ΡΠΌ), ΠΌΠ°ΡΡΡ ΠΏΠ»ΠΎΠ΄ΠΎΠ² (122,0 ΠΈ 136 Π³), ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΡΠ²Π΅ΡΠΎΡΠ½ΡΡ
ΠΊΠΈΡΡΠ΅ΠΉ Π½Π° ΡΠ°ΡΡΠ΅Π½ΠΈΠΈ (4,64, 13,33 ΠΈ 16,33) ΠΈ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΏΠ»ΠΎΠ΄ΠΎΠ² (61,67, 62,0 ΠΈ 67) Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΡΡΠ΅Ρ
Π»Π΅Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ Π±ΡΠ»ΠΈ ΠΏΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΎΠ΄Π½ΠΎΡΡΠΎΡΠΎΠ½Π½Π΅Π³ΠΎ Π΄ΠΈΡΠΏΠ΅ΡΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° (ANOVA) Ρ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠΈΠΌ HSD-ΡΠ΅ΡΡΠΎΠΌ Π’ΡΡΠΊΠΈ Ρ Ξ± = 0,05 Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ MINITAB (v. 19,0)
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