Stability and low-frequency oscillations of thin shells with entirely or partially negative Gaussian curvature

2011 ◽  
Vol 44 (1) ◽  
pp. 51-53
Author(s):  
M. B. Petrov
Keyword(s):  
Gaussian Curvature ◽  
Low Frequency ◽  
Thin Shells ◽  
Low Frequency Oscillations ◽  
Negative Gaussian Curvature

scholarly journals Low-frequency oscillations in cortical level to help diagnose task-specific dystonia

2021 ◽  
pp. 105444
Author(s):  
Chun-Chuan Chen ◽  
Antonella Macerollo ◽  
Hoon-Ming Heng ◽  
Ming-Kuei Lu ◽  
Chon-Haw Tsai ◽  
...  
Keyword(s):  
Low Frequency ◽  
Cortical Level ◽  
Low Frequency Oscillations

A Clustering-Based Approach for Estimation of Low Frequency Oscillations in Power Systems

2020 ◽  
Vol 35 (6) ◽  
pp. 4666-4677
Author(s):  
Piyush Warhad Pande ◽  
Saikat Chakrabarti ◽  
Suresh Chandra Srivastava ◽  
Subrata Sarkar
Keyword(s):  
Power Systems ◽  
Low Frequency ◽  
Low Frequency Oscillations

scholarly journals Force Control Is Related to Low-Frequency Oscillations in Force and Surface EMG

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e109202 ◽  
Author(s):  
Hwasil Moon ◽  
Changki Kim ◽  
Minhyuk Kwon ◽  
Yen Ting Chen ◽  
Tanya Onushko ◽  
...  
Keyword(s):  
Force Control ◽  
Low Frequency ◽  
Surface Emg ◽  
Low Frequency Oscillations

Nonrandom variability in respiratory cycle parameters of humans during stage 2 sleep

1990 ◽  
Vol 69 (2) ◽  
pp. 630-639 ◽  
Author(s):  
M. Modarreszadeh ◽  
E. N. Bruce ◽  
B. Gothe
Keyword(s):  
High Frequency ◽  
Minute Ventilation ◽  
Power Spectra ◽  
Low Frequency ◽  
Neural Mechanism ◽  
Normal Subjects ◽  
Feedback Loops ◽  
Low Frequency Oscillations ◽  
Stage 2 Sleep ◽  
Correlated Components

We analyzed breath-to-breath inspiratory time (TI), expiratory time (TE), inspiratory volume (VI), and minute ventilation (Vm) from 11 normal subjects during stage 2 sleep. The analysis consisted of 1) fitting first- and second-order autoregressive models (AR1 and AR2) and 2) obtaining the power spectra of the data by fast-Fourier transform. For the AR2 model, the only coefficients that were statistically different from zero were the average alpha 1 (a1) for TI, VI, and Vm (a1 = 0.19, 0.29, and 0.15, respectively). However, the power spectra of all parameters often exhibited peaks at low frequency (less than 0.2 cycles/breath) and/or at high frequency (greater than 0.2 cycles/breath), indicative of periodic oscillations. After accounting for the corrupting effects of added oscillations on the a1 estimates, we conclude that 1) breath-to-breath fluctuations of VI, and to a lesser extent TI and Vm, exhibit a first-order autoregressive structure such that fluctuations of each breath are positively correlated with those of immediately preceding breaths and 2) the correlated components of variability in TE are mostly due to discrete high- and/or low-frequency oscillations with no underlying autoregressive structure. We propose that the autoregressive structure of VI, TI, and Vm during spontaneous breathing in stage 2 sleep may reflect either a central neural mechanism or the effects of noise in respiratory chemical feedback loops; the presence of low-frequency oscillations, seen more often in Vm, suggests possible instability in the chemical feedback loops. Mechanisms of high-frequency periodicities, seen more often in TE, are unknown.

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