Cross diffusion governs an oscillatory instability in a ternary mixture with the Soret effect.

In a ternary mixture with the Soret effect, the interplay between cross-diffusion, thermodiffusion, and convection can lead to rich and complex dynamics including spatial patterns and oscillations. We present an experimental and three-dimensional numerical study of dynamic regimes in the toluene-methanol-cyclohexane ternary mixture with the Soret effect in the geometry of a thermogravitational column. An important feature of the system is that for the first component, toluene, the Soret and thermodiffusion coefficients have opposite signs, which triggers the oscillatory instability. Our experiments and numerical analysis show that the primary long-wave instability manifests itself in the form of a standing wave, and the secondary one emerges in the form of a swinging pattern. The computational model provides insight into the role of cross-diffusion coefficient D12 in the emergence and development of oscillatory instability. This study demonstrates that the long-wave oscillatory instability in transverse direction occurs only within a limited range of the D12 values and outside of this range it decays to a stationary pattern of either Turing-like or monotonic instability.

Aging Modulates the Resting Brain after a Memory Task: A Validation Study from Multivariate Models.

Recent work has demonstrated that aging modulates the resting brain. However, the study of these modulations after cognitive practice, resulting from a memory task, has been scarce. This work aims at examining age-related changes in the functional reorganization of the resting brain after cognitive training, namely, neuroplasticity, by means of the most innovative tools for data analysis. To this end, electroencephalographic activity was recorded in 34 young and 38 older participants. Different methods for data analyses, including frequency, time-frequency and machine learning-based prediction models were conducted. Results showed reductions in Alpha power in old compared to young adults in electrodes placed over posterior and anterior areas of the brain. Moreover, young participants showed Alpha power increases after task performance, while their older counterparts exhibited a more invariant pattern of results. These results were significant in the 140-160 s time window in electrodes placed over anterior regions of the brain. Machine learning analyses were able to accurately classify participants by age, but failed to predict whether resting state scans took place before or after the memory task. These findings greatly contribute to the development of multivariate tools for electroencephalogram (EEG) data analysis and improve our understanding of age-related changes in the functional reorganization of the resting brain.