The Impact of the Measure Used to Calculate the Distance between Exchange Rate Time Series on the Topological Structure of the Currency Network.

Structural properties of the currency market were examined with the use of topological networks. Relationships between currencies were analyzed by constructing minimal spanning trees (MSTs). The dissimilarities between time series of currency returns were measured in various ways: by applying Euclidean distance, Pearson's linear correlation coefficient, Spearman's rank correlation coefficient, Kendall's coefficient, partial correlation, dynamic time warping measure, and Kullback-Leibler relative entropy. For the constructed MSTs, their topological characteristics were analyzed and conclusions were drawn regarding the influence of the dissimilarity measure used. It turned out that the strength of most types of correlations was highly dependent on the choice of the numeraire currency, while partial correlations were invariant in this respect. It can be stated that a network built on the basis of partial correlations provides a more adequate illustration of pairwise relationships in the foreign exchange market. The data for quotations of 37 of the most important world currencies and four precious metals in the period from 1 January 2019 to 31 December 2022 were used. The outbreak of the COVID-19 pandemic in 2020 and Russia's invasion of Ukraine in 2022 triggered changes in the topology of the currency network. As a result of these crises, the average distances between tree nodes decreased and the centralization of graphs increased. Our results confirm that currencies are often pegged to other currencies due to countries' geographic locations and economic ties. The detected structures can be useful in descriptions of the currency market, can help in constructing a stable portfolio of the foreign exchange rates, and can be a valuable tool in searching for economic factors influencing specific groups of countries.

Mechanical Stimulation Decreases Auxin and Gibberellic Acid Synthesis but Does Not Affect Auxin Transport in Axillary Buds; It Also Stimulates Peroxidase Activity in Petunia × atkinsiana.

Thigmomorphogenesis (or mechanical stimulation-MS) is a term created by Jaffe and means plant response to natural stimuli such as the blow of the wind, strong rain, or touch, resulting in a decrease in length and an increase of branching as well as an increase in the activity of axillary buds. MS is very well known in plant morphology, but physiological processes controlling plant growth are not well discovered yet. In the current study, we tried to find an answer to the question if MS truly may affect auxin synthesis or transport in the early stage of plant growth, and which physiological factors may be responsible for growth arrest in petunia. According to the results of current research, we noticed that MS affects plant growth but does not block auxin transport from the apical bud. MS arrests IAA and GA3 synthesis in MS-treated plants over the longer term. The main factor responsible for the thickening of cell walls and the same strengthening of vascular tissues and growth arrestment, in this case, is peroxidase (POX) activity, but special attention should be also paid to AGPs as signaling molecules which also are directly involved in growth regulation as well as in cell wall modifications.

Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks.

Artificial neural networks are currently one of the most commonly used classifiers and over the recent years they have been successfully used in many practical applications, including banking and finance, health and medicine, engineering and manufacturing. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, although the method itself has been successfully applied in other machine learning techniques. This paper undertakes the effort to examine the q -generalized function based on Tsallis statistics as an alternative error measure in neural networks. In order to validate different performance aspects of the proposed function and to enable identification of its strengths and weaknesses the extensive simulation was prepared based on the artificial benchmarking dataset. The results indicate that Tsallis entropy error function can be successfully introduced in the neural networks yielding satisfactory results and handling with class imbalance, noise in data or use of non-informative predictors.