Search

We present a detailed analysis of the Hindriks and van Putten thalamocortical mean-field model for propofol anesthesia [NeuroImage 60(23), 2012]. The Hindriks and van Putten (HvP) model predicts increases in delta and alpha power for moderate (up to 130%) prolongation of GABAA inhibitory response, corresponding to light anesthetic sedation. Our analysis reveals that, for deeper anesthetic effect, the model exhibits an unexpected abrupt jump in cortical activity from a low-firing state to an extremely high-firing stable state (∼250 spikes/s), and remains locked there even at GABAA prolongations as high as 300% which would be expected to induce full comatose suppression of all firing activity. We demonstrate that this unphysiological behavior can be completely suppressed with appropriate tuning of the parameters controlling the sigmoidal functions that map soma voltage to firing rate for the excitatory and inhibitory neural populations, coupled with elimination of the putative population-dependent anesthetic efficacies introduced in the HvP model. The modifications reported here constrain the anesthetized brain activity into a biologically plausible range in which the cortex now has access to a moderate-firing state (“awake”) and a low-firing (“anesthetized”) state such that the brain can transition from “awake” to “anesthetized” states at a critical level of drug concentration. The modified HvP model predicts a drug–effect hysteresis in which the drug concentration required for induction is larger than that at emergence. In addition, the revised model shows a decrease in the intensity and frequency of alpha-band fluctuations, transitioning to delta-band dominance, with deepening anesthesia. These predicted drug concentration-dependent changes in EEG dynamics are consistent with clinical reports.

SCASA is a patented technique commercialized as a surge protector device (SPD) that adheres to UL-1449 test standards. Apart from the novel use of supercapacitors, SCASA design incorporates a coupled-inductor wound to a specially selected magnetic material of powdered-iron. In this study, we investigate the limitations of the present design under transient operation and elucidate ways to eliminate them with the use of air-gapped ferrite cores. In modelling the operation under 50 Hz AC and transient conditions, a permeance-based approach is used; in addition, non-ideal characteristics of the transformer core are emphasized and discussed with empirical validations. The experimental work was facilitated using a lightning surge simulator coupled with the 230 V AC utility mains; combinational surge-waveforms (6 kV/3 kA) defined by IEEE C62.41 standards were continuously injected into SPD prototypes during destructive testing. Such procedures substantiate the overall surge-endurance capabilities of the different core types under testing. With regard to optimizations, we validated a 95% depletion of a negative-surge effect that would otherwise pass to the load-end, and another 13–16% reduction of the clamping voltage verified the effectiveness of the methods undertaken. In conclusion, SCASA prototypes that utilized air-gapped cores revealed a greater surge endurance with improved load-end characteristics.

Seasonal phase disturbances in satellite Interferometric Synthetic Aperture Radar (InSAR) measurements have been reported in other studies to suggest sub-centimetre land surface terrain motion. These have been interpreted in various ways because they correlate with multiple other (sub-)seasonal signatures of, e.g., clay swelling/shrinkage and groundwater level. Recent microwave radar studies mention the occurrence of phase disturbances in different soil types and soil moisture. This study further explored this topic by modeling phase disturbances caused by both soil and vegetation surface characteristics and aimed to interpret what their possible effects on InSAR-interpreted terrain motion is. Our models, based on fundamental microwave reflection and transmission theory, found phase disturbances caused by seasonal variation of soil and vegetation that have the same magnitude as interpreted seasonal land movement in earlier InSAR studies. We showed that small, temporal differences in soil moisture and vegetation can lead to relatively large phase disturbances in InSAR measurements. These disturbances are a result of waves having to comply with boundary conditions at the interface between media with different dielectric properties. The findings of this study explain the seasonal variations found in other InSAR studies and will therefore bring new insights and alternative explanations to help improve interpretation of InSAR-derived seasonal terrain motion.

Growth of critical fluctuations prior to catastrophic state transition is generally regarded as a universal phenomenon, providing a valuable early warning signal in dynamical systems. Using an ecological fisheries model of three populations (juvenile prey J, adult prey A and predator P), a recent study has reported silent early warning signals obtained from P and A populations prior to saddle-node (SN) bifurcation, and thus concluded that early warning signals are not universal. By performing a full eigenvalue analysis of the same system we demonstrate that while J and P populations undergo SN bifurcation, A does not jump to a new state, so it is not expected to carry early warning signs. In contrast with the previous study, we capture a significant increase in the noise-induced fluctuations in the P population, but only on close approach to the bifurcation point; it is not clear why the P variance initially shows a decaying trend. Here we resolve this puzzle using observability measures from control theory. By computing the observability coefficient for the system from the recordings of each population considered one at a time, we are able to quantify their ability to describe changing internal dynamics. We demonstrate that precursor fluctuations are best observed using only the J variable, and also P variable if close to transition. Using observability analysis we are able to describe why a poorly observable variable (P) has poor forecasting capabilities although a full eigenvalue analysis shows that this variable undergoes a bifurcation. We conclude that observability analysis provides complementary information to identify the variables carrying early-warning signs about impending state transition.