Scaling Analysis of Ozone Precursor Relationships

2004 ◽  
pp. 321-329
Author(s):  
Bruce Ainslie ◽  
D.G. Steyn
Keyword(s):  
Scaling Analysis ◽  
Ozone Precursor

scholarly journals A scaling analysis of ozone photochemistry: II Investigation of the similarity relationship

2005 ◽  
Vol 5 (6) ◽  
pp. 12985-13010
Author(s):  
B. Ainslie ◽  
D. G. Steyn
Keyword(s):  
Temporal Variability ◽  
Ozone Production ◽  
Scaling Analysis ◽  
Smog Chamber ◽  
Chemical Mechanisms ◽  
Similarity Relationship ◽  
Test Conditions ◽  
Ozone Photochemistry ◽  
Ozone Precursor ◽  
Scale Break

Abstract. The similarity relationship for maximum ozone concentration as a function of initial precursor concentrations developed in the first article of this 2-part series exhibits a scale break, most easily seen after a Weibull transformation, which identifies a characteristic scale for ozone photochemistry. In this paper, we investigate the similarity relationship using simple semi-quantitative models and model output. From this investigation, we develop a set of properties characterizing ozone-precursor relationships. We find the scaling break is associated with a change in the temporal variability of ozone production. Finally, we examine a series of smog chamber experiments for evidence of a scaling break. We find the data support a slight break after suitable transformation. It is difficult to tell if the lack of distinct break is due to smog chamber experimental limitations, a bias in the test conditions or if the processes leading to the scaling break are overly enhanced by chemical mechanisms.

CRITICAL FIELD IN SPIN GLASSES : A SCALING ANALYSIS

1988 ◽  
Vol 49 (C8) ◽  
pp. C8-1071-C8-1072
Author(s):  
M. A. Continentino ◽  
E. Szkatulla ◽  
B. Elschner ◽  
H. Maletta
Keyword(s):  
Critical Field ◽  
Spin Glasses ◽  
Scaling Analysis

Scaling analysis of core pressure drop in reduced height integral test facility

Kerntechnik ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. 178-180
Author(s):  
P. Ju ◽  
B. Long ◽  
L. Li ◽  
Q. Su ◽  
X. Wu ◽  
...  
Keyword(s):  
Pressure Drop ◽  
Test Facility ◽  
Scaling Analysis ◽  
Integral Test ◽  
Reduced Height

SCALING ANALYSIS OF THE A-PHASE DYNAMICS DURING SLEEP

Fractals ◽  
2020 ◽  
Vol 28 (02) ◽  
pp. 2050050
Author(s):  
V. E. ARCE-GUEVARA ◽  
M. O. MENDEZ ◽  
J. S. MURGUÍA ◽  
A. ALBA ◽  
H. GONZÁLEZ-AGUILAR ◽  
...  
Keyword(s):  
Binary Sequence ◽  
Fluctuation Analysis ◽  
Scaling Analysis ◽  
Scaling Exponent ◽  
Sleep State ◽  
Phase Dynamics ◽  
A Value ◽  
Sleep Condition ◽  
Detrended Fluctuation

In this work, the scaling behavior of the sleep process is evaluated by using detrended fluctuation analysis based on wavelets. The analysis is carried out from arrivals of short and recurrent cortical events called A-phases, which in turn build up the Cyclic Alternating Pattern phenomenon, and are classified in three types: A1, A2 and A3. In this study, 61 sleep recordings corresponding to healthy, nocturnal frontal lobe epilepsy patients and sleep-state misperception subjects, were analyzed. From the A-phase annotations, the onsets were extracted and a binary sequence with one second resolution was generated. An item in the sequence has a value of one if an A-phase onset occurs in the corresponding window, and a value of zero otherwise. In addition, we consider other different temporal resolutions from 2[Formula: see text]s to 256[Formula: see text]s. Furthermore, the same analysis was carried out for sequences obtained from the different types of A-phases and their combinations. The results of the numerical analysis showed a relationship between the time resolutions and the scaling exponents; specifically, for higher time resolutions a white noise behavior is observed, whereas for lower time resolutions a behavior towards to [Formula: see text]-noise is exhibited. Statistical differences among groups were observed by applying various wavelet functions from the Daubechies family and choosing the appropriate sequence of A-phase onsets. This scaling analysis allows the characterization of the free-scale dynamic of the sleep process that is specific for each sleep condition. The scaling exponent could be useful as a diagnosis parameter in clinics when sleep macrostructure does not offer enough information.

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