Benchmarking cEEGrid and Solid Gel-Based Electrodes to Classify Inattentional Deafness in a Flight Simulator

Transfer from experiments in the laboratory to real-life tasks is challenging due notably to the inability to reproduce the complexity of multitasking dynamic everyday life situations in a standardized lab condition and to the bulkiness and invasiveness of recording systems preventing participants from moving freely and disturbing the environment. In this study, we used a motion flight simulator to induce inattentional deafness to auditory alarms, a cognitive difficulty arising in complex environments. In addition, we assessed the possibility of two low-density EEG systems a solid gel-based electrode Enobio (Neuroelectrics, Barcelona, Spain) and a gel-based cEEGrid (TMSi, Oldenzaal, Netherlands) to record and classify brain activity associated with inattentional deafness (misses vs. hits to odd sounds) with a small pool of expert participants. In addition to inducing inattentional deafness (missing auditory alarms) at much higher rates than with usual lab tasks (34.7% compared to the usual 5%), we observed typical inattentional deafness-related activity in the time domain but also in the frequency and time-frequency domains with both systems. Finally, a classifier based on Riemannian Geometry principles allowed us to obtain more than 70% of single-trial classification accuracy for both mobile EEG, and up to 71.5% for the cEEGrid (TMSi, Oldenzaal, Netherlands). These results open promising avenues toward detecting cognitive failures in real-life situations, such as real flight.

Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

The impact of the COVID-19 pandemic on tourism has received general attention in the literature, while the role of news during the pandemic has been ignored. Using a time-frequency connectedness approach, this paper focuses on the spillover effects of COVID-19-related news on the return and volatility of four regional travel and leisure (T&L) stocks. The results in the time domain reveal significant spillovers from news to T&L stocks. Specifically, in the return system, T&L stocks are mainly affected by media hype, while in the volatility system, they are mainly affected by panic sentiment. This paper also finds two risk contagion paths. The contagion index and Global T&L stock are the sources of these paths. The results in the frequency domain indicate that the shocks in the T&L industry are mainly driven by short-term fluctuations. The spillovers from news to T&L stocks and among these T&L stocks are stronger within 1 month.

Endocannabinoids and Heart Rate Variability Alterations after Exposure to Prolonged Intensive Physical Exercise of the Hellenic Navy SEALs

Background: Recent research indicates that both endocannabinoids (eCB) and heart rate variability (HRV) are associated with stress-induced experiences. However, these underlying mechanisms are not elucidated. The present study aims to investigate whether exposure to acute and chronic stress conditions can give rise to measurable changes, both to the peripheral eCB ligands and HRV. Methods: Thirteen candidates under intense preparation for their enlistment in the Hellenic Navy SEALs (HNS) participated in the study. All subjects underwent mental state examination, while HRV variables in time and frequency domain recordings were acquired. Furthermore, at baseline and 30 days after prolonged and intensive physical exercise, hair was collected to measure eCB ligands, such as anandamide (AEA), 2-arachidonoylglycerol (2-AG), and the N-acyl ethanolamine (NAE) molecules: palmitoylethanolamide (PEA) and oleoylethanolamide (OEA). Results: Comparing basal hair concentrations of eCB ligands before and after intense physical exercise, we found that AEA, PEA, and OEA were notably increased, whereas no differences were observed regarding the ligand 2-AG. Furthermore, there were observed associations between the concentrations of peripheral eCB ligands, both at baseline and after the prolonged physical exercise and the time and frequency domains of HRV. Conclusions: These findings suggest that endocannabinoid–HRV interrelations might share a short-term, and long-term adaptability of the changes in self-regulation associated with stress. Further studies will be required to determine the validity of peripheral eCB signaling and HRV as a biomarker for different aspects of the stress response.

Frequency domain of weakly translation invariant frame MRAs

Frequency domain of bandlimited frame multiresolution analyses (MRAs) plays a key role when derived framelets are applied into narrow-band signal processing and data analysis. In this study, we give a characterization of frequency domain of weakly translation invariant frame scaling functions [Formula: see text] with frequency domain [Formula: see text]. Based on it, we further study convex and ball-shaped frequency domains. If frequency domain of bandlimited frame scaling function [Formula: see text] is convex and completely symmetric about the origin, then it must be weakly invariant and [Formula: see text]. If [Formula: see text] has a ball-shaped frequency domain, the ball radius must be bounded by [Formula: see text]. These frequency domain characters are owned uniquely by frame scaling functions and not by orthogonal scaling functions.

Dynamic responses of elastic marine propellers in non-uniform flows

This paper focuses on the development of a three-dimensional panel method in time and frequency domains combined with the finite element method for analyzing the hydroelastic responses of rotating marine propellers in the wake of ships. A fully non-penetration boundary condition imposed on the deformed blade surface is conducted, in which the corrections of both the incoming flow velocities and the normal vectors imposed on the deformed and undeformed blade surface are taken into account. The added-mass and -damping matrices due to strongly coupled fluid-structure interaction are considered. Results of the present method are compared with experimental data available in the literature. It is observed that the fully non-penetration boundary condition applied on the deformed blade surface should be imposed to predict the unsteady performance of elastic propellers, which is due to the change of the added damping predicted by considering different non-penetration boundary conditions.

Similar Supine Heart Rate Variability Changes During 24-h Exposure to Normobaric vs. Hypobaric Hypoxia

Purpose: This study aimed to investigate the differences between normobaric (NH) and hypobaric hypoxia (HH) on supine heart rate variability (HRV) during a 24-h exposure. We hypothesized a greater decrease in parasympathetic-related parameters in HH than in NH.Methods: A pooling of original data from forty-one healthy lowland trained men was analyzed. They were exposed to altitude either in NH (FIO2 = 15.7 ± 2.0%; PB = 698 ± 25 mmHg) or HH (FIO2 = 20.9%; PB = 534 ± 42 mmHg) in a randomized order. Pulse oximeter oxygen saturation (SpO2), heart rate (HR), and supine HRV were measured during a 7-min rest period three times: before (in normobaric normoxia, NN), after 12 (H12), and 24 h (H24) of either NH or HH exposure. HRV parameters were analyzed for time- and frequency-domains.Results: SpO2 was lower in both hypoxic conditions than in NN and was higher in NH than HH at H24. Subjects showed similarly higher HR during both hypoxic conditions than in NN. No difference in HRV parameters was found between NH and HH at any time. The natural logarithm of root mean square of the successive differences (LnRMSSD) and the high frequency spectral power (HF), which reflect parasympathetic activity, decreased similarly in NH and HH when compared to NN.Conclusion: Despite SpO2 differences, changes in supine HRV parameters during 24-h exposure were similar between NH and HH conditions indicating a similar decrease in parasympathetic activity. Therefore, HRV can be analyzed similarly in NH and HH conditions.

Effects of Blade Number on the Propulsion and Vortical Structures of Pre-Swirl Stator Pump-Jet Propulsors

Reducing the noise of the underwater propulsor is gaining more and more attention in the marine industry. The pump-jet propulsor (PJP) is an extraordinary innovation in marine propulsion applications. This paper inspects the effects of blade number on a pre-swirl stator pump-jet propulsor (PJP) quantitatively and qualitatively. The numerical calculations are conducted by IDDES and ELES, where the ELES is only adopted to capture the vortical structures after refining the mesh. The numerical results show good agreement with the experiment. Detailed discussions of the propulsion, the features of thrust fluctuation in time and frequency domains, and the flow field are involved. Based on the ELES results, the vortices in the PJP flow field and the interactions between the vortices of the stator, rotor, and duct are presented. Results suggest that, though changing the blade number under a constant solidity does not affect the propulsion, it has considerable effects on the thrust fluctuation of PJP. The wakes of the stator and rotor are also notably changed. Increasing the stator blade numbers has significantly weakened the high-intensity vortices in the stator wake and, hence, the interaction with the rotor wake vortices. The hub vortices highly depend upon the wake vortices of the rotor. The hub vortices are considerably broken by upstream wake vortices when the load per rotor blade is high. In summary, the blade number is also vital for the further PJP design, particularly when the main concerns are exciting force and noise performance.

Engineering Nonlinear Epileptic Biomarkers Using Deep Learning and Benford’s Law

In this study we designed two deep neural networks to encode 16 feature latent spaces for early seizure detection in intracranial EEG and compared them to 16 widely used engineered metrics: Epileptogenicity Index (EI), Phase Locked High Gamma (PLHG), Time and Frequency Domain Cho Gaines Distance (TDCG, FDCG), relative band powers, and log absolute band powers (from alpha, beta, theta, delta, gamma, and high gamma bands. The deep learning models were pretrained for seizure identification on the time and frequency domains of one second single channel clips of 127 seizures (from 25 different subjects) using “leave-one-out” (LOO) cross validation. Each neural network extracted unique feature spaces that were used to train a Random Forest Classifier (RFC) for seizure identification and latency tasks. The Gini Importance of each feature was calculated from the pretrained RFC, enabling the most significant features (MSFs) for each task to be identified. The MSFs were extracted from the UPenn and Mayo Clinic's Seizure Detection Challenge to train another RFC for the contest. They obtained an AUC score of 0.93, demonstrating a transferable method to identify interpretable biomarkers for seizure detection.