Siberian Institute of Business Management and Psychology

The article is devoted to the study of game models in the process of social interaction in the implementation of marital behavior. The relevance of this work is due to the influence of marital behavior on the formation of a family and its development. The implementation of marital behavior is analyzed and explained from the point of view of the theory of transaction analysis by E. Bern, as a role-playing game based primarily on the use of hidden transactions. It is shown that this game reflects the existing changes in the socio-economic and socio-cultural relations of modern society. Game models are presented, in the implementation of which players use knowledge about traditional gender stereotypes. The analysis of standard game scenarios has been performed. The implemented moves are given, the main types of profits received by participants in the games of marital behavior are indicated. Purpose. Analysis and visual demonstration of specific key game models of marital behavior from the perspective of interpersonal transactions. Methods. The research uses both theoretical and empirical methods. The authors used the following theoretical methods: synthesis and modeling, analysis of literary sources and game models. As empirical methods, the authors used methods of observation, qualitative analysis, and the case study method. Results. As a result of the research, the key game models of marital behavior are presented, described and analyzed, the awareness of which will contribute to the formation of correct marital behavior, as well as further diagnosis and elaboration of life role settings. Practical implications. The results of the study can be applied in the field of marital counseling and family psychotherapy. EDN: YXCBAA

Ensuring the quality of drinking water is a critical public health issue, especially in the face of increasing industrial pollution and climate change. This study explores the application of machine learning techniques, specifically XGBoost, to predict water potability based on physical and chemical parameters. Initial experiments with a baseline XGBoost model demonstrated moderate success in classification, with particular difficulty in accurately identifying potable water, which is often underrepresented in the dataset.To address this challenge, the SMOTE technique was applied to balance the dataset, resulting in improved recall for the potable water class. Additionally, hyperparameter optimization via RandomizedSearchCV further enhanced the model’s performance, albeit modestly. Reducing the model’s complexity by selecting the most significant features led to a more efficient model with reduced computational overhead, while maintaining comparable accuracy.The findings of this study indicate that an optimized XGBoost model, supported by data preprocessing and feature selection, can be effectively integrated into automated water quality monitoring systems. Such a system would enable real-time detection of water quality deviations, facilitating prompt corrective actions to protect public health. This approach not only demonstrates the applicability of machine learning in environmental monitoring but also highlights the potential for broader application in resource management and public safety.

The present study develops the application of suburban forests as bioindicators, with the industrial metropolis of Krasnoyarsk (Central Siberia, Russia) taken as an example. Huge forests, such as those found in large Siberian territories, are climate-forming for the entire planet. Hence, their conservation is essential at both the local and global scales. During the period 2002-2021, the vigor state of two pine forests was evaluated using several inventory and morphological parameters: needle damage, deterioration in tree condition, increased entropy, and tree mortality. Additionally, an original bioindication parameter was applied: episodic increase in the size of needles was analyzed. We hypothesized that this increase in needle size was related to the activation of tree protection at the initial stage of tree damage; the mechanism assumes a redirection of sugar transport into the crown to aid tree regeneration. All parameters were measured annually on six permanent sample plots; each plot included 200-300 numbered trees of similar age (approximately 60-80 years). The long-term parameter changes were analyzed and attributed to chronic exposure to industrial air pollution. Significant changes in pine-forest parameters observed over the past few years (2019-2021) may indicate an approaching stage of irreversible toxic damage that is the destruction of the entire forest system. The results encourage involving forest-based bioindication in the regional system of ecological monitoring. Forest-based bioindication can be used as a tool for evaluating the efficiency of long-term governmental activity on air quality in industrial metropolises. Integr Environ Assess Manag 2023;19:980-987. © 2022 SETAC.