scholarly journals Effect of Stress on Cardiorespiratory Synchronization of Ironman Athletes

2021 ◽  
Vol 12 ◽  
Author(s):  
Maia Angelova ◽  
Philip M. Holloway ◽  
Sergiy Shelyag ◽  
Sutharshan Rajasegarar ◽  
H. G. Laurie Rauch
Keyword(s):  
Phase Synchronization ◽  
Decomposition Analysis ◽  
Recovery Phase ◽  
Stroop Test ◽  
Physical Stress ◽  
Cognitive Stress ◽  
Mode Decomposition ◽  
Stress Task ◽  
Before And After ◽  
Cardiorespiratory Synchronization

The aim of this paper is to investigate the cardiorespiratory synchronization in athletes subjected to extreme physical stress combined with a cognitive stress tasks. ECG and respiration were measured in 14 athletes before and after the Ironman competition. Stroop test was applied between the measurements before and after the Ironman competition to induce cognitive stress. Synchrogram and empirical mode decomposition analysis were used for the first time to investigate the effects of physical stress, induced by the Ironman competition, on the phase synchronization of the cardiac and respiratory systems of Ironman athletes before and after the competition. A cognitive stress task (Stroop test) was performed both pre- and post-Ironman event in order to prevent the athletes from cognitively controlling their breathing rates. Our analysis showed that cardiorespiratory synchronization increased post-Ironman race compared to pre-Ironman. The results suggest that the amount of stress the athletes are recovering from post-competition is greater than the effects of the Stroop test. This indicates that the recovery phase after the competition is more important for restoring and maintaining homeostasis, which could be another reason for stronger synchronization.

Machine learning–based reduced-order modeling of hydrodynamic forces using pressure mode decomposition

2021 ◽  
pp. 095441002199986
Author(s):  
Hassan F Ahmed ◽  
Hamayun Farooq ◽  
Imran Akhtar ◽  
Zafar Bangash
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Stokes Equations ◽  
Decomposition Analysis ◽  
Hydrodynamic Forces ◽  
Reduced Order Modeling ◽  
Navier Stokes ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Lift And Drag

In this article, we introduce a machine learning–based reduced-order modeling (ML-ROM) framework through the integration of proper orthogonal decomposition (POD) and deep neural networks (DNNs), in addition to long short-term memory (LSTM) networks. The DNN is utilized to upscale POD temporal coefficients and their respective spatial modes to account for the dynamics represented by the truncated modes. In the second part of the algorithm, temporal evolution of the POD coefficients is obtained by recursively predicting their future states using an LSTM network. The proposed model (ML-ROM) is tested for flow past a circular cylinder characterized by the Navier–Stokes equations. We perform pressure mode decomposition analysis on the flow data using both POD and ML-ROM to predict hydrodynamic forces and demonstrate the accuracy of the proposed strategy for modeling lift and drag coefficients.

Chronic physical stress differences between genders in a military training on the Amazon jungle

2015 ◽  
Vol 11 (1) ◽  
pp. 17-21
Author(s):  
Y.R. de Souza ◽  
F.B. Feitosa
Keyword(s):  
Plasma Levels ◽  
Scientific Literature ◽  
Military Training ◽  
Female Gender ◽  
Physical Stress ◽  
Male Gender ◽  
Normal Range ◽  
Male And Female ◽  
Normal Limits ◽  
Before And After

This study aimed to investigate the gender difference in the manifestation of physical stress in a strenuous military training on Amazon jungle, using alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatine kinase (CK) and lactate dehydrogenase (LDH) markers, measured before and after an adaptation to jungle training. The sample consisted of 49 military volunteers, 35 male and 14 female, recently moved to the Amazon region. All plasma levels rose after the training. Serum ALT (male and female) and AST (male and female), although borderline, remained within normal limits. Already plasma levels of CK (both male and female) and LDH (male and female) largely exceeded the normal range. The average of all markers listed in female gender remained below the levels of the male gender. However, significant differences in biomarkers ALT, AST and CK between genders were found. The study points out that, in a jungle environment, biometric markers ALT, AST, CK and LDH are efficient for monitoring chronic physical stress in both genders, when used in combination. The influence of the weather on the occurrence of physical stress in unacclimated people of both genders, and the lower responses in the levels of ALT, AST, LDH and CK in females were discussed basing on the scientific literature.

Adaptation of Arterial Wall to Physical Stress Before and After Pentoxifylline Administration

1980 ◽  
pp. 565-566 ◽  
Author(s):  
G. Brevetti ◽  
S. Abate ◽  
G. Lavecchia ◽  
G. P. Ferulano ◽  
G. Paudice ◽  
...  
Keyword(s):  
Arterial Wall ◽  
Physical Stress ◽  
Before And After

scholarly journals Phase Synchrony Analysis of Rolling Bearing Vibrations and Its Application to Failure Identification

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2964 ◽  
Author(s):  
Qing Zhang ◽  
Tingting Jiang ◽  
Joseph D. Yan
Keyword(s):  
Phase Synchronization ◽  
Chaotic Behavior ◽  
Rolling Bearing ◽  
Bearing Failure ◽  
Phase Synchrony ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Initial Stage ◽  
Failure Evolution ◽  
Band Limited

As the failure-induced component (FIC) in the vibration signals of bearings transmits through housings and shafts, potential phase synchronization is excited among multichannel signals. As phase synchrony analysis (PSA) does not involve the chaotic behavior of signals, it is suitable for characterizing the operating state of bearings considering complicated vibration signals. Therefore, a novel PSA method was developed to identify and track the failure evolution of bearings. First, resonance demodulation and variational mode decomposition (VMD) were combined to extract the mono-component or band-limited FIC from signals. Then, the instantaneous phase of the FIC was analytically solved using Hilbert transformation. The generalized phase difference (GPD) was used to quantify the relationship between FICs extracted from different vibration signals. The entropy of the GPD was regarded as the indicator for quantifying failure evolution. The proposed method was applied to the vibration signals obtained from an accelerated failure experiment and a natural failure experiment. Results showed that phase synchronization in bearing failure evolution was detected and evaluated effectively. Despite the chaotic behavior of the signals, the phase synchronization indicator could identify bearing failure during the initial stage in a robust manner.

Dynamic Mode Decomposition Analysis of High-Fidelity CFD Simulations of the Sinus Ventilation

2020 ◽  
Vol 105 (3) ◽  
pp. 699-713 ◽  
Author(s):  
Hadrien Calmet ◽  
Daniel Pastrana ◽  
Oriol Lehmkuhl ◽  
Takahisa Yamamoto ◽  
Yoshiki Kobayashi ◽  
...  
Keyword(s):  
Decomposition Analysis ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
High Fidelity ◽  
Cfd Simulations ◽  
Mode Decomposition
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