The problem evaluated in this study is related to the optimization of a budget of an industrial enterprise using simulation methods of the production process. Our goal is to offer a universal and straightforward methodology for simulating a production budget at any level of complexity by presenting it in a specific form. The calculation of such production schemes, in most enterprises, is currently done manually, which significantly limits the possibilities for optimization. This article proposes a model based on the Monte Carlo method to automate the budgeting process. The application of this model is described using an example of a typical meat processing enterprise. Approbation of the model showed its high applicability and the ability to transform the process of making management decisions and the potential to increase the profits of the enterprise, which is unattainable using other methods. As a result of the study, we present a methodology for modeling industrial production that can significantly speed up the formation and optimization of an enterprise’s budget. In our demonstration case, the profit increased by over 30 percentage points.

In this paper, two not-difficult inequalities are invented and proved in detail, which adequately describe the behavior of discrete logical functions $xor\left(x_1, x_2,\dots , x_n\right)$ and $and\left(x_1, x_2,\dots , x_n\right)$. Based on these proven inequalities, infinitely differentiable extensions of the logical functions $xor\left(x_1, x_2,\dots , x_n\right)$ and $and\left(x_1, x_2,\dots , x_n\right)$ were defined for the entire ${ℝ}^n$. These suitable extensions were applied to systems of logical equations. Specifically, the system of m logical equations in a constructive way without adding any equations (not field equations and no others) is transformed in ${ℝ}^n$ first into an equivalent system of $m$ smooth rational equations $\left(SmSRE\right)$ so that the solution of $SmSRE$ can be reduced to the problem minimization of the objective function, and any numerical optimization methods can be applied since the objective function will be infinitely differentiable. Again, we transformed $SmSRE$ into an equivalent system of $m$ polynomial equations $\left(SmPE\right)$. This means that any symbolic methods for solving polynomial systems can be used to solve and analyze an equivalent $SmPE$. The equivalence of these systems has been proved in detail. Based on these proofs and results, in the next paper, we plan to study the practical applicability of numerical optimization methods for $SmSRE$ and symbolic methods for $SmPE$.

A model of a layered hierarchically constructed composite is presented, the structure of which demonstrates the properties of similarity at different scales. For the proposed model of the composite, fractal analysis was carried out, including an assessment of the permissible range of scales, calculation of fractal capacity, Hausdorff and Minkovsky dimensions, calculation of the Hurst exponent. The maximum and minimum sizes at which fractal properties are observed are investigated, and a quantitative assessment of the complexity of the proposed model is carried out. A software package is developed that allows calculating the fractal characteristics of hierarchically constructed composite media. A qualitative analysis of the calculated fractal characteristics is carried out.

The chapter is devoted to the problem of analytical analysis of implementation of generative-competitive neural networks in predicting the state of financial markets (particularly to predict future moments of changing market conditions) based on the use of convolutional and generative neural networks, as well as reinforcement training. An algorithm for predicting future moments of trend change under concrete market conditions based on generative adversarial networks was developed. Special software that realizes algorithms for predicting future moments of changing market conditions, based on the algorithms mentioned above was designed.

The authors set the goal of their research to form the reader's opinion about the need to use modern technologies for regulating and supervising the turnover of digital financial assets. To achieve this , the following tasks were solved: 1. The article provides an overview of the events organized by Rosfinmonitoring, which determine the control over the turnover of digital financial assets. 2. The main elements of digital financial assets, the properties of their turnover and measures to counteract the laundering of proceeds from crime are analyzed. 2. The analysis of classifications of blockchain platforms is given. The study is presented in the form of an analytical review. The conducted research allowed us to draw conclusions: 1. the thesis is true about the unconditional fulfillment of the requirement to disclose information about the buyer of cryptocurrencies, regardless of the technologies used; 2. the emergence of new risks in the field of money laundering obtained by criminal means becomes more intense with the increase in the number of technologies being introduced in the sphere of digital assets turnover, despite the formal signs of their compliance with the regulatory rules established at some initial stage; 3. the activity of cyber fraudsters, which is increasing against the background of the emergence of new technologies, requires the use and development of new modern technologies of regulation and supervision.