The field of signal processing using machine and deep learning algorithms has undergone significant growth in the last few years, with a wide scope of practical applications for electroencephalography (EEG). Transcutaneous electroacupuncture stimulation (TEAS) is a well-established variant of the traditional method of acupuncture that is also receiving increasing research attention. This paper presents the results of using deep learning algorithms on EEG data to investigate the effects on the brain of different frequencies of TEAS when applied to the hands in 66 participants, before, during and immediately after 20 min of stimulation. Wavelet packet decomposition (WPD) and a hybrid Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) model were used to examine the central effects of this peripheral stimulation. The classification results were analysed using confusion matrices, with kappa as a metric. Contrary to expectation, the greatest differences in EEG from baseline occurred during TEAS at 80 pulses per second (pps) or in the ‘sham’ (160 pps, zero amplitude), while the smallest differences occurred during 2.5 or 10 pps stimulation (mean kappa 0.414). The mean and CV for kappa were considerably higher for the CNN-LSTM than for the Multilayer Perceptron Neural Network (MLP-NN) model. As far as we are aware, from the published literature, no prior artificial intelligence (AI) research appears to have been conducted into the effects on EEG of different frequencies of electroacupuncture-type stimulation (whether EA or TEAS). This ground-breaking study thus offers a significant contribution to the literature. However, as with all (unsupervised) DL methods, a particular challenge is that the results are not easy to interpret, due to the complexity of the algorithms and the lack of a clear understanding of the underlying mechanisms. There is therefore scope for further research that explores the effects of the frequency of TEAS on EEG using AI methods, with the most obvious place to start being a hybrid CNN-LSTM model. This would allow for better extraction of information to understand the central effects of peripheral stimulation. Full article

The reservoir-triggered seismicity at the Song Tranh 2 reservoir in Vietnam is investigated by using Shannon entropy, a well-known informational method used to analyze complexity in time series in terms of disorder and uncertainty. The application of the time-varying Shannon entropy to the time series of the interevent times of seismicity has evidenced clear links with the temporal fluctuations of the water level of the reservoir, strengthening the belief that the reservoir operational regime is one of the sources of the seismicity occurring in the area. Shannon entropy has also shed light on the tectonic mechanisms of generation of reservoir-triggered seismicity, revealing that the change in stress due to the variation in water level causes the seismic system to be in a state of greater disorder and instability, well depicted by Shannon entropy, which would lead to an increase in seismic activity. Full article

The current scientific consensus is that genetic mutations are random processes. According to the Darwinian theory of evolution, only natural selection determines which mutations are beneficial in the course of evolution, and there is no deterministic correlation between any parameter and the probability that these mutations will occur. Here, we investigate RNA genetic sequences of the SARS-CoV-2 virus using Shannon’s information theory, and we report a previously unobserved relationship between the information entropy of genomes and their mutation dynamics. Based on the analysis presented here, we are able to formulate a governing law of genetic mutations, stating that genomes undergo genetic mutations over time driven by a tendency to reduce their overall information entropy, challenging the existing Darwinian paradigm. Full article

In this paper, informational (Shannon) measures of symmetry are introduced and analyzed for patterns built of 1D and 2D shapes. The informational measure of symmetry $H_{sym}\left(G\right)$ characterizes the averaged uncertainty in the presence of symmetry elements from group G in a given pattern, whereas the Shannon-like measure of symmetry ${\mathsf{\Omega }}_{sym}\left(G\right)$ quantifies the averaged uncertainty of the appearance of shapes possessing a total of n elements of symmetry belonging to group G in a given pattern. $H_{sym}\left(G_1\right)={\mathsf{\Omega }}_{sym}\left(G_1\right)=0$ for the patterns built of irregular, non-symmetric shapes, where $G_1$ is the identity element of the symmetry group. Both informational measures of symmetry are intensive parameters of the pattern and do not depend on the number of shapes, their size, and the entire area of the pattern. They are also insensitive to the long-range order (translational symmetry) inherent for the pattern. Additionally, informational measures of symmetry of fractal patterns are addressed, the mixed patterns including curves and shapes are considered, the time evolution of Shannon measures of symmetry are examined, the close-packed and dispersed 2D patterns are analyzed, and an application of the suggested measures of symmetry for the analysis of the chemical reaction is demonstrated. Full article