scholarly journals SCALO: A program to perform Guttman scaling analysis on the Apple II microcomputer

1983 ◽  
Vol 15 (5) ◽  
pp. 549-549
Author(s):  
Andrew R. Gilpin
Keyword(s):  
Scaling Analysis ◽  
Guttman Scaling

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

Two Comments on Guttman Scaling

1969 ◽  
Vol 75 (2) ◽  
pp. 278-280
Author(s):  
Peter Weldon ◽  
J. David Martin ◽  
Louis N. Gray
Keyword(s):  
Guttman Scaling

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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