The use of information technology and, in particular, learning management systems, increases the ability of both the teacher and the learner to achieve their goals in the educational process. Such systems provide educational content, help organize and monitor training, collect progress statistics, and can also take into account the individual characteristics of each user of the system. The purpose of this study is to determine the direction of development of modern learning systems and technologies for their implementation. The evolution of learning management systems, the transition to intelligent learning systems, the main stages of such systems were reviewed, the types of learning sequences were analyzed, the transformationinto adaptive learning systems was identified, and the scheme of the system and its mathematical model were presented. Expertise systems, the theory of fuzzy sets and fuzzy logic, cluster analysis, as well as genetic algorithms and artificial neural networks are defined as the mechanisms for implementing the learning systems. An artificial neural network in an adaptive learning system will allow you to create a unique training program that will build on existing knowledge and the level of perception of educational material by students. By formalizing the intellectual processes that both the teacher and the student carry out, it is possible to automate a certain part of the teacher’s functions, reduce the cost of manual labor, which will make it easier to monitor the learning process and also make the learning process more efficient.

The central object of computer lexicography is a computer or electronic dictionary, which must have a sufficiently large vocabulary, provide the consistent extraction of information depending on the user’s need and provide complete grammatical information about the words of input and output languages. Taking into account the current trend in the development of special terminological dictionaries, the authors propose an English-Belarusian-Russian dictionary of technical terms. At the initial stage of the work the dictionary was named TechLex and covers the following subject areas: architecture and construction, water supply, information technology, pedagogy, transport communications, economics, energy-supply. Currently, each subject area of the dictionary is located in the Internet GoogleTable and contains about 1000 terms. It has the possibility to be simultaneously filled by several teachers. The linguistic database of the dictionary is not created by the traditional way of processing a large number of paper dictionaries and combining the received translations. Lexis from sequential processing of scientific and technical English periodicals of particular subject areas is the base of it. The software of the proposed electronic dictionary is designed taking into account the analysis of modern electronic multilingual translation dictionaries and is a client-server application in Java programming language. The client part of the system contains a mobile application for the Android operating system, which was tested on tablets and smartphones with different screen diagonals. The interface of TechLex dictionary is designed in such a way that only a single zone is activated according to the query, so there is no need to view all the subject areas of the dictionary. The proposed TechLex dictionary is the first technical multilingual electronic dictionary with an English-Belarusian-Russian version.

Sintered friction materials are widely used in friction units of automotive vehicles and special purpose vehicles.  The main purpose is to transmit torque to the actuator. The development of the technology market requires the development and use of new units. At the same time, the creation of new materials is required, which also applies to sintered friction materials. This group of materials is characterized by a high service life, efficiency of torque transmission, as well as the ability to restore performance in case of violation of operating modes. One of the most significant parameters characterizing  a sintered friction material is wear resistance. In most cases, it determines not only the resource of the unit itself, but the entire machine as a whole. A special place is occupied by brake units, which also use friction materials. The increased wear  resistance of the friction material contributes to a decrease in the efficiency and service life of the brake system. Evaluation  of the wear resistance of a friction material for the given operational parameters is a very long and costly process. The development of methodology and methods for accelerating the assessment of wear resistance is an important scientific and practical task. The paper presents the results of using artificial neural networks to predict the service life of a composite friction material based on copper on the sliding speed, pressure on the material and the amount of lubricant supplied to the friction zone. An artificial neural network has been trained using an array of experimental data for the FM-15 friction material.  The training results have shown high accuracy, correctness of the proposed and implemented network architecture. The developed software has demonstrated its efficiency and the possibility of using it in calculations to determine the wear of a composite friction material.

This paper presents a variant of using an artificial neural network (ANN) for adaptive learning. The main idea of using ANN is to apply it for a specific educational material, so that after completing the course or its separate topic, the student can determine, not only his level of knowledge, without the teacher’s participation, but also get some recommendations on what material needs to be studied further due to gaps in the studied issues. This approach allows you to build an individual learning trajectory, significantly reduce the time to study academic disciplines and improve the quality of the educational process. The training of an artificial neural network takes place according to the method of back propagation of an error. The developed ANN can be applied to study any academic discipline with a different number of topics and control questions. The research results are implemented and tested in the CATS adaptive training system. This system is the author's development.

We present a description of a reusable software package able to control a large, heterogeneous network of fully and semi-robotic observatories initially developed to run the MONET network of two 1.2 m telescopes. Special attention is given to the design of a robust, long-term observation scheduler which also allows the trading of observation time and facilities within various networks. The handling of the "Phase I&II" project-development process, the time-accounting between complex organizational structures, and usability issues for making the package accessible not only to professional astronomers, but also to amateurs and high-school students is discussed. A simple RTML-based solution to link multiple networks is demonstrated.