The publication provides brief information about the VII All–Russian Conference with International Participation “Digital Technologies of the Future—Modern Solutions in Earth Sciences. ITES-2025,” which took place on September 22–26, 2025, in Vladivostok.

It is assumed that meaning is a function that generates a new value in the process of informational interaction of the message with the recipient’s knowledge. It is concluded that meaning is information about information. Meaning acts as a technological information object that implements information reduction: it “organizes” perceived signals, signs, or texts in accordance with a certain purpose and other circumstances of the activity. It dynamically shapes and maintains the orientation of the cognitive process, thereby ensuring an appropriate choice of the components for the next step on the phase trajectory of development. The essence and purpose, as well as the scope of meaning, its necessity and naturalness are considered, including in inanimate nature. Meaning is presented as a possibility and consequence of the functioning of reason as such, in the way a reasonable subject integrates itself into the universe.

The article presents an overview of modern methods and artificial intelligence (AI) models designed to perform the main tasks of intelligent text processing of scientific and technical documents. The rationale for the historical transition from linguistic methods of document processing to modern methods based on AI technologies, including using Python libraries, is given. The most powerful tools that effectively perform the tasks of intelligent text processing are highlighted.

Increasing urbanization and motorization are leading to a shortage of parking spaces, resulting in congestion, increased emissions, and a declining quality of life. Traditional methods of parking management methods are ineffective for addressing this issue, necessitating the use of data analysis and forecasting tools. This paper examines the use of a digital twin of the Kazan parking system. Data were filtered and integrated, points of interest were clustered, and a correlation analysis of factors influencing parking occupancy was performed. Linear regression, decision tree, random forest, XGBoost, MLP, and LSTM models were trained and compared to predict occupancy levels. The random forest model demonstrated the best results. The developed digital twin prototype enables monitoring and scenario modeling, making it an effective tool for parking space optimization and management decision-making.

For a specially designed training sample, it is necessary to generate a set of hypotheses larger than r = (m – 1)/8ε, so the probabilistic–combinatorial formal (VKF) learning method has the I-type error lesser than ε on m test cases.

As game services that require constant content updates to retain players, automating the generation of adaptive playable characters has become an urgent task. This article examines existing approaches to character generation, including evolutionary algorithms and in-session adaptation systems. Current solutions are limited by their inability to provide sufficient long-term adaptation to individual player styles and their reliance on manual design. To address these limitations, we propose a three-component system that integrates: player action modeling based on gameplay replays using reinforcement learning (RL) agents, character generation through combinatorial mechanics and parameter balancing, and automatic validation via simulations to assess balance and alignment with a player’s individual style. This work synthesizes contemporary research, highlighting the potential that generative methods have to reduce development costs for game services. The results could accelerate prototyping and enhancing the long-term viability of live-service projects.

The author has presented possibilities for mathematical approximation and the dimension theory in computer science. The received formulas have led to a conclusion concerning existence of energetically-information equivalents thanks to which the theory of linearization of the nonlinear data and continuum digitization have received substantiation. The author has processed the obtained data from the perspective of the theory of dimension, and also has discussed hypotheses concerning an “error method” of chemical elements with negative mass.

This paper describes the development of an intelligent system for detecting environmental problems based on multispectral satellite images. The functionality of the system is based on machine learning algorithms and provides the ability to highlight areas of environmental problems based on the analysis of each individual pixel of the image. The article describes the system architecture, data collection and preprocessing process, model development and training. The evaluation of the results of the system is based on real data. The effectiveness of application of the developments obtained as a result of the study for the tasks of monitoring the environmental state of the Earth’s surface for large territories is confirmed. The use of software modules provides the ability to respond quickly to emerging environmental abnormal situations.