In world practice, the role of reproduction of raw material base of oil production by implementing modern methods of oil recovery enhancement (thermal, gas, chemical, microbiological) on the basis of innovative techniques and technologies is rapidly growing and is becoming more important. It is concluded that at present, the priority of increasing oil reserves in world oil production is the development and industrial introduction of modern integrated methods of enhanced oil recovery, which can provide a synergistic effect in the development of new and developed oil fields. This article presents a review and comparative analysis of theoretical and practical methods of improving oil recovery of conventional and unconventional reservoirs. The paper examines in detail methods of improving oil recovery, taking into account the factors of enhanced oil recovery of oil reservoirs. Considered the main methods and technologies currently used to develop oil fields and recommendations for their effective use, taking into account the variety of external factors of oil production: the geological structure of the reservoir, its volume, and properties of oils. It is shown that there is no universal method of oil reservoir development, and it must be chosen after a thorough feasibility study among several proposed models. When describing the methods of enhanced oil recovery, special attention is also paid to the physical processes that occur as a result of applying the technology. In conclusion, the positive and negative characteristics of the presented methods included in EOR are presented, and recommendations that may influence the choice of practical solutions for engineers and oil producers are given. Conclusions are made that development systems, placement and choice of operating mode of wells essentially depend on the geological structure of the reservoir, its volume and properties of oils. An important role in this is the construction of a geological model of the production facility. The used hydrodynamic models of development are based on physical laws, about which oil producers sometimes don’t even suspect, and the authors of the models are not always able to convey it to the real producers. The authors consider it reasonable to make a logical generalizing conclusion that understanding processes occurring in the reservoir and taking appropriate measures for optimization and intensification of oil production will allow making oil production as effective as possible.

This review explores the application of Long Short-Term Memory (LSTM) networks, a specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM networks have shown notable effectiveness in modeling sequential data and predicting time-series outcomes, which are essential for understanding complex molecular structures and dynamic processes in polymers. This review delves into the use of LSTM models for predicting polymer properties, monitoring polymerization processes, and evaluating the degradation and mechanical performance of polymers. Additionally, it addresses the challenges related to data availability and interpretability. Through various case studies and comparative analyses, the review demonstrates the effectiveness of LSTM networks in different polymer science applications. Future directions are also discussed, with an emphasis on real-time applications and the need for interdisciplinary collaboration. The goal of this review is to connect advanced machine learning (ML) techniques with polymer science, thereby promoting innovation and improving predictive capabilities in the field.

One of the significant factors in the sustainability of education is the development of inclusive education. An inclusive educational space implies openness and accessibility of education for students, regardless of their educational needs. Inclusive education also means a partnership between students and teachers. A teacher is a living person whose socio-emotional skills and professional abilities are the basis for the sustainability of education and student development. This article is devoted to studying teachers’ competence and psychological readiness at schools and higher educational institutions to work with students with special educational needs. This article includes the results of a sociological study conducted in September 2022 and is devoted to the readiness of schoolteachers and university professors for inclusive education (N = 125). The general statistical calculation was carried out based on information processing using the Vortex program version 10.0. Universities and schools are located in large administrative centers of Russian regions with a population of about a million. The relevance of this study is due to the relationship of sustainability with the ideas of inclusive education, manifested in its goal of achieving students’ educational levels established by the state and the humanistic concept of equality of opportunity in the process of professional implementation and self-determination of the individual.

The article reviews the existing methods of increasing the energy efficiency of electric transport by analyzing and studying the methods of increasing the energy storage resource. It is grouped according to methods, approaches, and solutions. The most effective methods and ways of their implementation are identified. General methods of increasing energy efficiency, methods of increasing recuperation during braking, methods of energy-efficient energy consumption, the use of energy-saving technologies, and improving the energy efficiency of the traction drive are considered. The purpose of this work is to identify the main operating factors on the basis of a critical review of existing methods for assessing the technical condition of batteries and experimental results on the degradation of lithium-ion batteries. Using the great experience of the research group in the field of modeling, diagnostics, and forecasting of life of electric cars, as well as their intellectual management, the new theoretical and practical methods of integrated assessment of the parameters of the traction battery and state of charge, which are operated in the heavy forced regenerative regimes of electric traction, are created and proposed. A great role is played by the construction of the transport model. The development is based on physical laws that passengers and vehicle owners are unaware of. For each model there is a different area of application, and what is suitable for one object may not be suitable for another. The overview shows that there is no one-size-fits-all way to improve energy efficiency. It is necessary to make a choice among several proposed models after a thorough feasibility study.

This article investigates the utility of machine learning (ML) methods for predicting and analyzing the diverse physical characteristics of polymers. Leveraging a rich dataset of polymers’ characteristics, the study encompasses an extensive range of polymer properties, spanning compressive and tensile strength to thermal and electrical behaviors. Using various regression methods like Ensemble, Tree-based, Regularization, and Distance-based, the research undergoes thorough evaluation using the most common quality metrics. As a result of a series of experimental studies on the selection of effective model parameters, those that provide a high-quality solution to the stated problem were found. The best results were achieved by Random Forest with the highest $R^2$ scores of 0.71, 0.73, and 0.88 for glass transition, thermal decomposition, and melting temperatures, respectively. The outcomes are intricately compared, providing valuable insights into the efficiency of distinct ML approaches in predicting polymer properties. Unknown values for each characteristic were predicted, and a method validation was performed by training on the predicted values, comparing the results with the specified variance values of each characteristic. The research not only advances our comprehension of polymer physics but also contributes to informed model selection and optimization for materials science applications.

The increasing complexity of polymer systems in both experimental and computational studies has led to an expanding interest in machine learning (ML) methods to aid in data analysis, material design, and predictive modeling. Among the various ML approaches, boosting methods, including AdaBoost, Gradient Boosting, XGBoost, CatBoost and LightGBM, have emerged as powerful tools for tackling high-dimensional and complex problems in polymer science. This paper provides an overview of the applications of boosting methods in polymer science, highlighting their contributions to areas such as structure–property relationships, polymer synthesis, performance prediction, and material characterization. By examining recent case studies on the applications of boosting techniques in polymer science, this review aims to highlight their potential for advancing the design, characterization, and optimization of polymer materials.