This article is dedicated to the development of an innovative approach to optimizing membrane separation processes. The paper introduces the integration of a genetic algorithm (GA) and mesoscopic modeling to enhance the efficiency and accuracy of process parameter optimization. The GA is employed for evolutionary search of optimal parameters, such as pressure, temperature, and membrane material characteristics. The use of evolutionary principles allows for efficient exploration of parameter space, identifying optimal solutions. Mesoscopic modeling serves as a tool for detailed analysis and visualization of membrane separation processes. It involves modeling the interaction of molecules with the membrane surface, enabling a more accurate consideration of the physicochemical aspects of the process. The integration of the GA and mesoscopic modeling creates a unique tool for membrane separation process optimization. The developed approach contributes not only to improving component separation efficiency but also to minimizing energy consumption. The method presented in the article has been successfully tested on model membrane process systems and demonstrated significant improvements compared to traditional optimization methods. The research results confirm the potential of the proposed approach for application in membrane technology industries, opening new perspectives in the field of separation process optimization.

In the radio telemetry systems of spacecraft, various data compression methods are used for data processing. When using any compression methods, the data obtained as a result of compression is formed randomly, and transmission over radio communication channels should be carried out evenly over time. This leads to the need to use special buffer storage devices. In addition, existing spacecraft radio telemetry systems require grouping of compressed data streams by certain characteristics. This leads to the need to sort compressed data by streams. Therefore, it is advisable to use associative buffer storage devices in such systems. This article is devoted to the analysis of the processes of formation of output streams of compressed data generated at the output of an associative storage device (ASD). Since the output stream of compressed data is random, queue theory and probability theory are used for analysis. At the same time, associative memory is represented as a queue system. Writing and reading in an ASD can be interpreted as servicing orders in a queue system. The purpose of the analysis is to determine the characteristics of an associative storage device (ASD). Such characteristics are the queue length M{N} in the ASD, the deviation of the queue length D{N} in the ASD and the probability $p_n$ of a given volume $n$ of compressed data in the ASD (including the probability of emptying and the probability of memory overflow). The results obtained are of great practical importance, since they can be used to select the amount of memory of an associative storage device (ASD) when designing compression devices for telemetry systems of spacecraft.

This study examines the effectiveness of machine learning models in predicting the engagement metric, which characterizes the popularity of social media content based on posts published on Facebook. Linear regression, Ridge, Lasso, and Random Forest models were utilized during the experiment. From a methodological standpoint, special attention was paid to the issue of data leakage; a modeling scenario was developed to eliminate data leakage by using only those variables known prior to publication. Furthermore, considering the pronounced positive asymmetry and heavy-tailed distribution of the dependent variable, the impact of the log1p transformation on the generalization ability of the models was investigated. The application of the log1p transformation enhanced the prediction quality of the Ridge regression, reduced bias, and increased the R² score. Additional validation based on time-based splitting proved the methodological correctness of the obtained conclusions.

This article looks at mathematical modeling and computer calculations for a layer of woven composite material that has some local technological defects. From the studies done, theres a mathematical model that describes how a woven composite layer deforms when it has curved fibers and those defects. With current computer tech, they came up with efficient algorithms to handle big sets of data on stress and strain states. Then a special software was made using Python and the Streamlit framework, and some calculations got run through it.