The Impact of the COVID-19 Pandemic on the Unpredictable Dynamics of the Cryptocurrency Market

The global economy is under great shock again in 2020 due to the COVID-19 pandemic; it has not been long since the global financial crisis in 2008. Therefore, we investigate the evolution of the complexity of the cryptocurrency market and analyze the characteristics from the past bull market in 2017 to the present the COVID-19 pandemic. To confirm the evolutionary complexity of the cryptocurrency market, three general complexity analyses based on nonlinear measures were used: approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv complexity (LZ). We analyzed the market complexity/unpredictability for 43 cryptocurrency prices that have been trading until recently. In addition, three non-parametric tests suitable for non-normal distribution comparison were used to cross-check quantitatively. Finally, using the sliding time window analysis, we observed the change in the complexity of the cryptocurrency market according to events such as the COVID-19 pandemic and vaccination. This study is the first to confirm the complexity/unpredictability of the cryptocurrency market from the bull market to the COVID-19 pandemic outbreak. We find that ApEn, SampEn, and LZ complexity metrics of all markets could not generalize the COVID-19 effect of the complexity due to different patterns. However, market unpredictability is increasing by the ongoing health crisis.

AFFECTIVE AUTONOMIC DIFFERENCES BETWEEN MONOLINGUALS AND BILINGUALS: ELICITED BY PICTORIAL STIMULI

The main objective of the current study was to evaluate differences between autonomic responses of monolinguals and Turkish- and Kurdish-bilinguals, while pictorial stimuli with four emotional contents, including happy, relax, sad and fear was presented. The galvanic skin responses (GSR), electrocardiogram (ECG) and pulse signal (PS) of 21 college students, including seven Persian monolinguals, seven Kurdish–Persian, and seven Turkish–Persian bilinguals were collected. Estimating the maximum value of the power spectral density and some nonlinear features including Lyapunov exponents (LE), Sample entropy (SaEn), and Lempel–Ziv (LZ) complexity, significant differences were examined by means of statistical analysis. Heightened autonomic responses were found for maximum power and LZ of bilinguals in comparing to monolinguals. A reverse pattern was revealed for other measures. Another important finding was the significant differences between bilinguals and monolinguals autonomic indices; however, no significant differences within two groups of bilinguals were observed. It can be concluded that the autonomic responses to pictorial stimuli can reveal emotional differences of monolingual and bilingual individuals. The results of the current study will be of interest to the researchers working in affective sciences, psycholinguistics, and cross-cultural psychology.

Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine-Pairwise Algorithm Utilizing LZ-Complexity

This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.