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Analysis of regional economic indicators plays a crucial role in management and development planning, with Gross Regional Product (GRP) serving as one of the key indicators of economic activity. The application of artificial intelligence, including neural network technologies, enables significant improvements in the accuracy and reliability of forecasts of economic processes. This study compares three neural network algorithm models for predicting the GRP of a typical region of the Russian Federation — the Udmurt Republic — based on time series data from 2000 to 2023. The selected models include a neural network with the Bat Algorithm (BA-LSTM), a neural network model based on backpropagation error optimized with a Genetic Algorithm (GA-BPNN), and a neural network model of Elman optimized using the Particle Swarm Optimization algorithm (PSO-Elman). The research involved stages of neural network modeling such as data preprocessing, training model, and comparative analysis based on accuracy and forecast quality metrics. This approach allows for evaluating the advantages and limitations of each model in the context of GRP forecasting, as well as identifying the most promising directions for further research. The utilization of modern neural network methods opens new opportunities for automating regional economic analysis and improving the quality of forecast assessments, which is especially relevant when data are limited and for rapid decision-making. The study uses factors such as the amount of production capital, the average annual number of labor resources, the share of high-tech and knowledge-intensive industries in GRP, and an inflation indicator as input data for predicting GRP. The high accuracy of the predictions achieved by including these factors in the neural network models confirms the strong correlation between these factors and GRP. The results demonstrate the exceptional accuracy of the BA-LSTM neural network model on validation data: the coefficient of determination was 0.82, and the mean absolute percentage error was 4.19%. The high performance and reliability of this model confirm its capacity to predict effectively the dynamics of the GRP. During the forecast period up to 2030, the Udmurt Republic is expected to experience an annual increase in Gross Regional Product (GRP) of +4.6% in current prices or +2.5% in comparable 2023 prices. By 2030, the GRP is projected to reach 1264.5 billion rubles.

Social media posts play an important role in demonstration of financial market state, and their analysis is a powerful tool for trading. The article describes the result of a study of the impact of social media activities on the movement of the financial market. The top authoritative influencers are selected. Twitter posts are used as data. Such texts usually include slang and abbreviations, so methods for preparing primary text data, including Stanza, regular expressions are presented. Two approaches to the representation of a point in time in the format of text data are considered. The difference of the influence of a single tweet or a whole package consisting of tweets collected over a certain period of time is investigated. A statistical approach in the form of frequency analysis is also considered, metrics defined by the significance of a particular word when identifying the relationship between price changes and Twitter posts are introduced. Frequency analysis involves the study of the occurrence distributions of various words and bigrams in the text for positive, negative or general trends. To build the markup, changes in the market are processed into a binary vector using various parameters, thus setting the task of binary classification. The parameters for Binance candlesticks are sorted out for better description of the movement of the cryptocurrency market, their variability is also explored in this article. Sentiment is studied using Stanford Core NLP. The result of statistical analysis is relevant to feature selection for further binary or multiclass classification tasks. The presented methods of text analysis contribute to the increase of the accuracy of models designed to solve natural language processing problems by selecting words, improving the quality of vectorization. Such algorithms are often used in automated trading strategies to predict the price of an asset, the trend of its movement.

A Large Language Model (LLM) is an advanced artificial intelligence algorithm that utilizes deep learning methodologies and extensive datasets to process, understand, and generate humanlike text. These models are capable of performing various tasks, such as summarization, content creation, translation, and predictive text generation, making them highly versatile in applications involving natural language understanding. Generative AI, often associated with LLMs, specifically focuses on creating new content, particularly text, by leveraging the capabilities of these models. Developers can harness LLMs to automate complex processes, such as extracting relevant information from system requirement documents and translating them into a structured database schema. This capability has the potential to streamline the database design phase, saving significant time and effort while ensuring that the resulting schema aligns closely with the given requirements. By integrating LLM technology with Natural Language Processing (NLP) techniques, the efficiency and accuracy of generating database schemas based on textual requirement specifications can be significantly enhanced. The proposed tool will utilize these capabilities to read system requirement specifications, which may be provided as text descriptions or as Entity-Relationship Diagrams (ERDs). It will then analyze the input and automatically generate a relational database schema in the form of SQL commands. This innovation eliminates much of the manual effort involved in database design, reduces human errors, and accelerates development timelines. The aim of this work is to provide a tool can be invaluable for software developers, database architects, and organizations aiming to optimize their workflow and align technical deliverables with business requirements seamlessly.

Numerical simulation of moving sportsman external flow is presented. The unique method is developed for obtaining integral aerodynamic characteristics, which were the function of the flow regime (i.e. angle of attack, flow speed) and body position. Individual anthropometric characteristics and moving boundaries of sportsman (or sports equipment) during the race are taken into consideration.

Numerical simulation is realized using FlowVision CFD. The software is based on the finite volume method, high-performance numerical methods and reliable mathematical models of physical processes. A Cartesian computational grid is used by FlowVision, the grid generation is a completely automated process. Local grid adaptation is used for solving high-pressure gradient and object complex shape. Flow simulation process performed by solutions systems of equations describing movement of fluid and/or gas in the computational domain, including: mass, moment and energy conservation equations; state equations; turbulence model equations. FlowVision permits flow simulation near moving bodies by means of computational domain transformation according to the athlete shape changes in the motion. Ski jumper aerodynamic characteristics are studied during all phases: take-off performance in motion, in-run and flight. Projected investigation defined simulation method, which includes: inverted statement of sportsman external flow development (velocity of the motion is equal to air flow velocity, object is immobile); changes boundary of the body technology defining; multiple calculations with the national team member data projecting. The research results are identification of the main factors affected to jumping performance: aerodynamic forces, rotating moments etc. Developed method was tested with active sportsmen. Ski jumpers used this method during preparations for Sochi Olympic Games 2014. A comparison of the predicted characteristics and experimental data shows a good agreement. Method versatility is underlined by performing swimmer and skater flow simulation. Designed technology is applicable for sorts of natural and technical objects.

Simulation of combat and military operations is the most important scientific and practical task aimed at providing the command of quantitative bases for decision-making. The first models of combat were developed during the First World War (M. Osipov, F. Lanchester), and now they are widely used in connection with the massive introduction of automation tools. At the same time, the models of combat and war do not fully take into account the moral potentials of the parties to the conflict, which motivates and motivates the further development of models of battle and war. A probabilistic model of combat is considered, in which the parameter of combat superiority is determined through the parameter of moral (the ratio of the percentages of the losses sustained by the parties) and the parameter of technological superiority. To assess the latter, the following is taken into account: command experience (ability to organize coordinated actions), reconnaissance, fire and maneuverability capabilities of the parties and operational (combat) support capabilities. A game-based offensive-defense model has been developed, taking into account the actions of the first and second echelons (reserves) of the parties. The target function of the attackers in the model is the product of the probability of a breakthrough by the first echelon of one of the defense points by the probability of the second echelon of the counterattack repelling the reserve of the defenders. Solved the private task of managing the breakthrough of defense points and found the optimal distribution of combat units between the trains. The share of troops allocated by the parties to the second echelon (reserve) increases with an increase in the value of the aggregate combat superiority parameter of those advancing and decreases with an increase in the value of the combat superiority parameter when repelling a counterattack. When planning a battle (battles, operations) and the distribution of its troops between echelons, it is important to know not the exact number of enemy troops, but their capabilities and capabilities, as well as the degree of preparedness of the defense, which does not contradict the experience of warfare. Depending on the conditions of the situation, the goal of an offensive may be to defeat the enemy, quickly capture an important area in the depth of the enemy’s defense, minimize their losses, etc. For scaling the offensive-defense model for targets, the dependencies of the losses and the onset rate on the initial ratio of the combat potentials of the parties were found. The influence of social costs on the course and outcome of wars is taken into account. A theoretical explanation is given of a loss in a military company with a technologically weak adversary and with a goal of war that is unclear to society. To account for the influence of psychological operations and information wars on the moral potential of individuals, a model of social and information influence was used.

Posts on social networks can both predict the movement of the financial market, and in some cases even determine its direction. The analysis of posts on Twitter contributes to the prediction of cryptocurrency prices. The specificity of the community is represented in a special vocabulary. Thus, slang expressions and abbreviations are used in posts, the presence of which makes it difficult to vectorize text data, as a result of which preprocessing methods such as Stanza lemmatization and the use of regular expressions are considered. This paper describes created simplest machine learning models, which may work despite such problems as lack of data and short prediction timeframe. A word is considered as an element of a binary vector of a data unit in the course of the problem of binary classification solving. Basic words are determined according to the frequency analysis of mentions of a word. The markup is based on Binance candlesticks with variable parameters for a more accurate description of the trend of price changes. The paper introduces metrics that reflect the distribution of words depending on their belonging to a positive or negative classes. To solve the classification problem, we used a dense model with parameters selected by Keras Tuner, logistic regression, a random forest classifier, a naive Bayesian classifier capable of working with a small sample, which is very important for our task, and the k-nearest neighbors method. The constructed models were compared based on the accuracy metric of the predicted labels. During the investigation we recognized that the best approach is to use models which predict price movements of a single coin. Our model deals with posts that mention LUNA project, which no longer exist. This approach to solving binary classification of text data is widely used to predict the price of an asset, the trend of its movement, which is often used in automated trading.

Large Language Models (LLMs) are transforming software engineering by bridging the gap between natural language and programming languages. These models have revolutionized communication within development teams and the Software Development Life Cycle (SDLC) by enabling developers to interact with code using natural language, thereby improving workflow efficiency. This survey examines the impact of LLMs across various stages of the SDLC, including requirement gathering, system design, coding, debugging, testing, and documentation. LLMs have proven to be particularly useful in automating repetitive tasks such as code generation, refactoring, and bug detection, thus reducing manual effort and accelerating the development process. The integration of LLMs into the development process offers several advantages, including the automation of error correction, enhanced collaboration, and the ability to generate high-quality, functional code based on natural language input. Additionally, LLMs assist developers in understanding and implementing complex software requirements and design patterns. This paper also discusses the evolution of LLMs from simple code completion tools to sophisticated models capable of performing high-level software engineering tasks. However, despite their benefits, there are challenges associated with LLM adoption, such as issues related to model accuracy, interpretability, and potential biases. These limitations must be addressed to ensure the reliable deployment of LLMs in production environments. The paper concludes by identifying key areas for future research, including improving the adaptability of LLMs to specific software domains, enhancing their contextual understanding, and refining their capabilities to generate semantically accurate and efficient code. This survey provides valuable insights into the evolving role of LLMs in software engineering, offering a foundation for further exploration and practical implementation.

Accurate tree identification is essential for ecological monitoring, biodiversity assessment, and forest management. Traditional manual survey methods are labor-intensive and ineffective over large areas. Advances in remote sensing technologies including lidar and hyperspectral imaging improve automated, exact detection in many fields.

Nevertheless, these technologies typically require extensive labeled data and manual feature engineering, which restrict scalability. This research proposes a new method of Self-Supervised Learning (SSL) with the SimCLR framework to enhance the classification of tree species using unlabelled data. SSL model automatically discovers strong features by merging the spectral data from hyperspectral data with the structural data from LiDAR, eliminating the need for manual intervention.

We evaluate the performance of the SSL model against traditional classifiers, including Random Forest (RF), Support Vector Machines (SVM), and Supervised Learning methods, using a dataset from the ECODSE competition, which comprises both labeled and unlabeled samples of tree species in Florida’s Ordway-Swisher Biological Station. The SSL method has been demonstrated to be significantly more effective than traditional methods, with a validation accuracy of 97.5% compared to 95.56% for Semi-SSL and 95.03% for CNN in Supervised Learning.

Subsampling experiments showed that the SSL technique is still effective with less labeled data, with the model achieving good accuracy even with only 20% labeled data points. This conclusion demonstrates SSL’s practical applications in circumstances with insufficient labeled data, such as large-scale forest monitoring.

The problem is to automate the correction of LaTeX documents. Each document is represented as a parse tree. The modified Zhang-Shasha algorithm is used to construct a mapping of tree vertices of the original document to the tree vertices of the edited document, which corresponds to the minimum editing distance. Vertex to vertex maps form the training set, which is used to generate rules for automatic correction. The statistics of the applicability to the edited documents is collected for each rule. It is used for quality assessment and improvement of the rules.

Most biomechanical tasks of interest to clinicians can be solved only using personalized mathematical models. Such models allow to formalize and relate key pathophysiological processes, basing on clinically available data evaluate non-measurable parameters that are important for the diagnosis of diseases, predict the result of a therapeutic or surgical intervention. The use of models in clinical practice imposes additional restrictions: clinicians require model validation on clinical cases, the speed and automation of the entire calculated technological chain, from processing input data to obtaining a result. Limitations on the simulation time, determined by the time of making a medical decision (of the order of several minutes), imply the use of reduction methods that correctly describe the processes under study within the framework of reduced models or machine learning tools.

Personalization of models requires patient-oriented parameters, personalized geometry of a computational domain and generation of a computational mesh. Model parameters are estimated by direct measurements, or methods of solving inverse problems, or methods of machine learning. The requirement of personalization imposes severe restrictions on the number of fitted parameters that can be measured under standard clinical conditions. In addition to parameters, the model operates with boundary conditions that must take into account the patient’s characteristics. Methods for setting personalized boundary conditions significantly depend on the clinical setting of the problem and clinical data. Building a personalized computational domain through segmentation of medical images and generation of the computational grid, as a rule, takes a lot of time and effort due to manual or semi-automatic operations. Development of automated methods for setting personalized boundary conditions and segmentation of medical images with the subsequent construction of a computational grid is the key to the widespread use of mathematical modeling in clinical practice.

The aim of this work is to review our solutions for personalization of mathematical models within the framework of three tasks of clinical cardiology: virtual assessment of hemodynamic significance of coronary artery stenosis, calculation of global blood flow after hemodynamic correction of complex heart defects, calculating characteristics of coaptation of reconstructed aortic valve.