Kuban State Agrarian University is a university located in Krasnodar, a city in southern Russia..
1,1-Difluoro-4-(4-(prop-2-yn-1-yloxy)phenyl)-2,6-diphenyl-1-bora-2,3,5,6-tetrazine is prepared by the reaction of boron trifluoride etherate with 3-(4-prop-2-yn-1-yloxy)phenyl-1,5-diphenylformazan in the presence of triethylamine in toluene. The obtained compound is structurally characterized.
The paper considers the possibilities of applying interpretable machine learning methods for analyzing environmental and industrial risks of mining enterprises. The study is based on processing production and environmental monitoring data, including technological process indicators, energy consumption parameters, and pollutant emission levels. A gradient boosting algorithm was used to predict the integrated risk indicator using modern data analysis tools. The interpretation of modeling results was performed using the SHAP values method, which makes it possible to determine the contribution of individual parameters to the predicted risk indicator. Computational experiments demonstrated a high predictive accuracy of the developed model and allowed the identification of the key factors affecting environmental and industrial risks. The obtained results confirm the prospects of using interpretable machine learning models for analyzing monitoring data and supporting decision-making processes in the mining industry.
The article addresses the problem of intelligent anomaly detection in industrial monitoring data for assessing the stability of mining engineering systems. Modern mining enterprises generate large volumes of monitoring data that reflect the state of technological processes and engineering structures. Traditional approaches to data analysis often have limitations related to insufficient sensitivity to complex parameter relationships and difficulties in processing large datasets. The study proposes an approach to the intelligent analysis of industrial monitoring data based on machine learning methods. The Isolation Forest algorithm is used as the main tool for anomaly detection, allowing efficient identification of atypical observations in multidimensional time series. The proposed method was tested on real industrial monitoring data. The results demonstrate its ability to detect abnormal operating conditions and provide informative indicators for assessing the operational stability of mining systems.
The paper considers the application of machine learning methods for forecasting production and environmental indicators of mineral processing plants. A forecasting approach based on a hybrid neural network model combining a multilayer feedforward network and an LSTM recurrent architecture is proposed. Production and environmental monitoring data obtained from a mineral processing unit were used as the initial dataset. The data were preliminarily processed, including normalization and removal of anomalous values. The developed model was trained and tested using an experimental dataset, after which the forecasting accuracy was evaluated. The results of computational experiments demonstrate that the proposed hybrid architecture improves forecasting accuracy by 8-10% compared with conventional neural network models. The obtained results confirm the feasibility of applying the developed approach in intelligent systems for industrial data analysis and decision support aimed at improving technological efficiency and reducing environmental impact in mineral processing enterprises.
Contemporary employment relations are often marked by a deficit of trust and rising conflict, which impedes constructive interaction between staff and employers. In this context, it is essential to cultivate good faith and socially responsible employment relations grounded in mutual trust and transparency, and in the employer's commitment to building harmonious professional relationships with employees. The purpose of the article was to identify tools for improving the quality of socially responsible employment relations between employees and the employer, that are aimed at increasing trust, ensuring the good faith performance of duties, and minimizing the employer's personnel risks. The research methods included qualitative information gathering within an applied legal analytical research design that combines doctrinal (normative legal) analysis with the systematization of managerial practices used by Russian employers. To develop good faith and socially responsible employment relations between the employee and the employer, a set of instruments should be used, one of which is systematic programs for training and enhancing staff competencies. Training underpins employees' loyalty to a particular employer by demonstrating the use and development of human potential. Training must be systematic, since one off session doesn't achieve the goals of good faith conduct and, more importantly, can't influence the efficiency with which an employee performs their job function. Based on the analysis conducted, the apprenticeship (training) agreement, mentoring, and professional development were systematized, and it was shown how each instrument contributes to the development of good faith and socially responsible employment relations and strengthens personnel security within the organization.
