A Minimal Model of Metabolism Based Chemotaxis

Since the pioneering work by Julius Adler in the 1960's, bacterial chemotaxis has been predominantly studied as metabolism-independent. All available simulation models of bacterial chemotaxis endorse this assumption. Recent studies have shown, however, that many metabolism-dependent chemotactic patterns occur in bacteria. We hereby present the simplest artificial protocell model capable of performing metabolism-based chemotaxis. The model serves as a proof of concept to show how even the simplest metabolism can sustain chemotactic patterns of varying sophistication. It also reproduces a set of phenomena that have recently attracted attention on bacterial chemotaxis and provides insights about alternative mechanisms that could instantiate them. We conclude that relaxing the metabolism-independent assumption provides important theoretical advances, forces us to rethink some established pre-conceptions and may help us better understand unexplored and poorly understood aspects of bacterial chemotaxis

Using of Open-Source Technologies for the Design and Development of a Speech Processing System Based on Stemming Methods

This article discusses the idea of developing an intelligent and customizable automated system for real-time text and voice dialogs with the user. This system can be used for almost any subject area, for example, to create an automated robot - a call center operator or smart chat bots, assistants, and so on. This article presents the developed flexible architecture of the proposed system. The system has many independent submodules. These modules work as interacting microservices and use several speech recognition schemes, including a decision support submodule, third-party speech recognition systems and a post-processing subsystem. In this paper, the post-processing module of the recognized text is presented in detail on the example of Russian and English dictionary models. The proposed submodule also uses several processing steps, including the use of various stemming methods, the use of word stop-lists or other lexical structures, the use of stochastic keyword ranking using a weight table, etc. © 2020, IFIP International Federation for Information Processing.Acknowledgments. The work was supported by Act 211 Government of the Russian Federation, contract no. 02.A03.21.0006