This paper explores the potential of generative neural network simulations, focusing on the application of reinforcement learning methods and neural world models for creating interactive worlds. Key achievements in agent training using reinforcement learning are discussed. Special attention is given to neural world models, as well as generative models such as Oasis, DIAMOND, Genie, and GameNGen, which employ diffusion networks to generate realistic and interactive game worlds. The opportunities and limitations of generative simulation models are examined, including issues related to error accumulation and memory constraints, and their impact on the quality of generation. The conclusion presents suggestions for future research directions.

This article examines the technical challenges involved in transferring game scenes between various game engines. It analyzes the key issues arising from differences in scene formats, incompatibilities in rendering and physics APIs, as well as problems in converting materials, shaders, and animation data, and discrepancies in coordinate systems. Existing tools and methods, including automated solutions for exporting, converting, and importing data, are presented with a particular focus on migrating content from Unreal Engine to Unigine. Furthermore, the paper discusses fundamental approaches to solving the problem, such as the use of universal exchange formats (FBX, glTF, USD), the development of middleware, and the modular design of game scenes, which pave the way for future automation. The work also highlights our group’s research results on the formal description of game logic and approaches to porting VR applications across different libraries. The conclusions provide practical recommendations for developers and outline future research directions in the area of automated content transfer between game engines.

In this study, we present a method for creating a synthetic dataset using the MetaHuman framework to optimize the skinning of 3D models. The research focuses on improving the quality of skeletal deformation (skinning) by leveraging a diverse array of high-fidelity virtual human models. Using MetaHuman, we generated an extensive dataset comprising dozens of virtual characters with varied anthropometric fea-tures and precisely defined skinning weight parameters. This data was used to train an algorithm that optimizes the distribution of skinning weights between bones and the character mesh.

The proposed approach automates the weight rigging process, significantly reducing manual effort for riggers and increasing the accuracy of deformations during animation. Experimental results show that leveraging synthetic data reduces skinning errors and produces smoother character movements compared to traditional methods. The outcomes have direct applications in the video game, animation, virtual reality, and simulation industries, where rapid and high-quality rigging of numerous characters is required. The method can be integrated into existing graphics engines and development pipelines (such as Unreal Engine or Unity) as a plugin or tool, facilitating the adoption of this technology in practical projects.

Unique aspects of game design documentation development for massively multiplayer online role-playing game in virtual reality (VR) are considered. Relevance and problems of designing such games are discussed, existing approaches to creating and adapting documentation for VR are described, and also such key issues in VR as network interaction, immersion, interaction of players and non-player characters controlled by artificial intelligence are defined. A methodological approach to the construction of a game design document for games of this genre is proposed, using the example of a technical specification of several games with a focus on structuring and standardizing the document. Future directions are discussed, including form-mal descriptions of game logic for scene portability, perspectives on auto-automated VR porting, and tools for auto-generation. Finally, the importance of standardizing technical specifications is emphasized and directions for further research are suggested.

The Virtual Reality Trainer for the Prenatal Detection of Congenital Heart Disease and Associated Malformations project addresses the need to improve specialized training in prenatal diagnosis through immersive technologies. Its theoretical module, “My Friend the Lung”, gamifies learning about fetal cardiovascular anatomy through 3D puzzles, timed challenges, and badges, achieving good results in its pilot test, is noteworthy. The practical module simulates ultrasounds with haptic devices, reducing diagnostic errors and enabling the generation of personalized ultrasounds. A database with 1,200 labeled studies was consolidated as a fundamental source of information. The implementation aims to initially reduce clinical errors, with the potential to decrease perinatal mortality. This project fuses technological innovation, medical rigor, and interactive pedagogy, positioning gamification as an essential tool in medical education and laying the groundwork for its expansion to other specialties.

Cooperative mechanics of multiplayer video games are typified. Cooperative game mechanics are the key elements of gameplay design that determine the ways of interaction between players in cooperative gameplay. Existing research in this area is analyzed and the main principles influencing the success of cooperative interaction are identified. Eight types of cooperative mechanics were identified and classified: cooperative, cooperative-emergent, altruistic, socio-economic, complementary, simultaneous control, penalty, and chain mechanics.

The authors concluded that successful cooperative mechanics are built on the principles of complementary roles, the combination of different abilities, and the need to distribute tasks for the sake of achieving a common goal. The proposed classification contributes to the systematization of knowledge in the field of game design and can be useful for developers of multiplayer video games. In the future, the classification can be refined and expanded taking into account the evolution of the industry and the emergence of new forms of cooperation.

Now days, the development of ontological models for creating edges and their contours for moving objects in real time or close to it is an urgent task. An ontological model for implementing this process is shown in the article. The main algorithms for detecting object edges and constructing contours in an image and program codes for their implementation are considered in the article. It is noted that the Canny algorithm is the best for recognizing edges. At the same time, its serious drawback is determined, which consists in the fact that with insignificant movement of objects, more than 50% of information about the contours is lost.

The dependences of the first four frequencies of natural oscillations of a planar regular truss of the thrust type are obtained numerically. A model is used in which the mass of the truss is concentrated in its nodes. The Maxwell–Mohr formula is used to calculate the rigidity of the truss. For the first frequency, an analytical dependence on the number of panels is derived by the induction method using a simplified version of the Dunkerley method in the Maple computer mathematics system. Good agreement with the numerical result is shown. An analytical dependence of the static deflection of the truss on its dimensions and load is obtained.

In this paper, the authors found a transformation that is valid for any arbitrary signal. This transformation is strictly periodical and therefore it allows to apply the ordinary F-transformation for the fitting of the transformed signal. The most interesting application (in accordance with the author's opinion) is the fitting of the frequency-phase modulated signals that actually located inside the found transformation. This new transformation will be useful for application of the responses of different complex systems when an ordinary model is absent.

As an available data we consider meteo-data corresponding to measurements of methane concentration (CH₄) in atmosphere during 4 weeks of its observation. For us it is important to consider the integral (cumulative) data and find their amplitude-frequency response (AFR). If one considers each column as frequency-phase modulated signal, then AFR can be evaluated with the help of F-transformation that has the period equals 2p that is valid for any analyzed random signal. This "universal" F-transformation allows to fit a wide set of random signals and compare them with each other in terms of their AFRs. Concluding the abstract one can say that these new possibilities of the traditional F-analysis will serve as a common tool in the armory of the methods used by researchers in data processing area.

The article discusses the methodology of developing educational algorithm executors using a combined pictographic-text interface in the Python programming language. The relevance of the research is due to the need to improve approaches to teaching algorithms and programming in the school course of computer science. The author presents the toolkit for creating algorithm executors, combining visibility of icons with text programming capabilities. Particular attention is paid to the practical aspects of implementation, including the use of builtin Python routines for processing graphics and text.

The paper proposes a method for estimating the fluctuation characteristics of distributed objects based on the fluctuation analysis and the assumption that the optical flow estimate is equivalent to random-walk increments. The reliability and applicability of the proposed method are evaluated in two computational experiments. The first experiment analyses the Brownian motion of a compact object. The second evaluates the adequacy of the method for estimating the dynamic characteristics of a spatially distributed fluctuating object. In both experiments, the Hurst exponent has been validated using detrended fluctuation analysis (DFA). The results obtained indicate the applicability of the method and the need to improve its robustness.

This article discusses the impact of global interpreter blocking on the performance of multithreaded applications in Python. The concept of subinterpreters is described as one of the solutions that allows bypassing the limitations of the GIL and ensuring effective parallel code execution. A comparative analysis of subinterpretors with traditional methods of parallel computing, such as the use of processes and threads, is carried out. The experimental results show that subinterpreters significantly increase performance under conditions of high computing loads. In addition, the article explores the use of subinterpreters in web development. The advantages of using this approach for query processing and resource management in modern web applications are considered, which can significantly improve their scalability and responsiveness. The novelty of this work lies in the in-depth analysis of subinterpreters in the context of specific use cases, which has previously not received sufficient coverage in the scientific literature. The results of the work emphasize the need for further study of subinterpreters as an alternative approach in Python, which is of interest to developers and researchers in the field of high-performance computing.

The paper analyzes the structure of the department's memos to simplify a retrospective search among an array of documentation and identify groups of similar documents, which will greatly simplify the process of searching and working with them, as well as the creation of new standard documents.