The work proposes the use of a unique method of creating passive, multifunctional, non-contact pressure-temperature sensors. The basis of this method is a combination of inorganic semiconductors and high-molecular organic cholesteric crystals. According to their morphology, such crystals represent a spiral structure that is sensitive to changes in external physical factors, such as temperatures, due to changes in the periodicity of the structure, which leads to Bragg diffraction scattering of light on it. The consequence of such influence is the coloring of the cholesteric, which can be identified by external spectrosensitive devices on a non-contact basis. On the other hand, the use of inorganic semiconductors involves the production of a micro-profiled base with a thin silicon membrane that is sensitive to external pressure. The thickness of the membrane determines the operating conditions of the sensor depending on the range of applied pressure from 0.3 bar and above. A hardware and software complex was developed for continuous monitoring of changes in the color of passive pressure-temperature sensors, tracking the spectral distribution of the light intensity of the color of the liquid crystal depending on the operating conditions on a non-contact basis with an external spectrometer. The basis of such a system is a software module created on the basis of the MVVM (Model–View–View Model) architecture template. A feature of the software module is the use of the .NET and WPF frameworks, which natively support this architectural pattern for .NET Windows platforms and are supported by all popular versions of operating systems. The SQlite database, which is a relational database management system, is used to store data in the software application. The OmniDriver library was used in the system to operate and configure the spectrometer. The software module has two modes of operation with spectrometers. The first mode is characterized by the reading of a single spectrum, while the second mode is characterized by periodic reading and processing of the intensity spectral distribution in real time with a given period. When using the second mode, the software module allows you to dynamically change the periods and parameters of changing the color parameters of the light over time. The main algorithm of the software module is the transformation of the spectral intensity distribution normalized in the CIE XYZ color model, which is the basis for all further calculations, into the RGB model.

Since the outbreak of the pandemic in 2019, Covid-19 has become one of the most important topics in the field of medicine. This disease, caused by the SARSCoV-2 virus, can lead to serious respiratory diseases and other complications. They can even lead to death. In recent years, the number of Covid-19 cases around the world has increased significantly, resulting in the need for rapid and effective diagnosis of the disease. Currently, the use of deep learning in medical diagnostics is becoming more and more common. It provides the high diagnostic efficacy that scientists, doctors and patients care about. During the Covid-19 diagnostic procedure, most clinicians order images from Xray and CT to be taken from patients. It is the analysis of these images that gives a full diagnosis. In this article, we will discuss the use of deep neural networks in the diagnosis of Covid-19, especially using chest images taken from X-ray and CT. 

The relevance of the study of the agrarian market of Europe, and in particular of Ukraine, is connected with the growing competition of states on world food markets. Trade regulation does not produce the expected results, does not reduce the level of monopolization by transnational corporations of many industry and regional food markets. The problems of population dissatisfaction and ensuring the independence of states are intensifying, which, in turn, increases the role of rational protectionism of the domestic food market. The results of research on this problem will expand the possibilities of scientific approaches to create large-scale research and educational structures capable of participating in various areas of activity to ensure food security. Since the agricultural sector is still one of the most important sectors of the economy in many countries, providing employment and being the main source of income for significant sections of their population, it is not surprising that most of them are interested in the introduction of new technologies and the adaptation of agriculture to development. Changes in the scale of the world economy in recent years have significantly increased interest in investment tasks, which is evidenced by the intensification of trading in the shares of large and medium-sized international companies and, accordingly, causes a rapid increase in their values. As a special case in investment theory, the problem of decision-making regarding the formation and optimization of an investment portfolio is considered. This task is in the field of attention of both large investment companies and private investors, since, choosing among possible alternatives for the distribution of capital investments within the market of financial assets, the investor will get different results. As a result, it is necessary to understand the amount of income received during the period of ownership of the investment portfolio. In the presented work, using the C# programming language, a model is proposed that can be applied in the formation of a portfolio of securities, which allows potential investors to independently assess the effectiveness of the investment set by comparing the growth dynamics of shares available on the financial market. It is known that most of the information an investor encounters is tabular in nature, and according to the methodology of scientific knowledge, a person better perceives visualized ways of presenting information. The model proposed in the work uses visualization tools built into the software product, which presents available tabulated information in a structured graphic form.

In the work, a study of the design and technological direction of creating a dual-functional non-contact pressure and temperature sensor based on silicon-cholesteric crystal systems was carried out. An optically active environment based on a supramolecular spiral structure is a sensitive element for forming the optical response of a microelectronic label sensor during non-contact monitoring of the state of a physical object using a spectrometer. The optical response of the device provides maximum intensity in the case of interference that corresponds to the Wulff–Bragg condition. The functionality of the sensor is ensured by the conditions for avoiding high electromagnetic interference during optical identification. A hardware and software modules have been created for non-contact monitoring of the state of physical objects, the main part of which is graphical user interface that corresponds to the MVVM architectural design pattern. Visualization of the spectral intensity using the software module was provided by conversion to the RGB model. The algorithm of procedure for calculating the color rendering index is given. In a wide range of light waves, the main parameters' color temperature was defined.