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Amid the ongoing trend of rapid urbanization and the intensive development of megacities and large cities worldwide, the impact of man-made vibrations on residential structures and infrastructure is increasing. The operation of subway systems, construction using pile-driving and drilling equipment, and heavy traffic have become active sources of wave disturbances, which can be a decisive factor in reducing the structural stability of buildings and, consequently, their long-term reliability. This paper proposes a numerical calculation using the grid-characteristic method to model elastic waves propagating through soil layers and load-bearing structures from various sources. By solving the direct problem of numerical pulse simulation and varying its location, the values of velocity vector projections and components of the Cauchy stress tensor were obtained at each time step. Two scenarios were examined: the first simulates the impact of noise generated by construction work or nearby traffic, while the second demonstrates how a subway running through an underground tunnel affects multi-story residential buildings. Wave propagation patterns from these sources were visualized in terms of the parameters of interest, enabling a quick and convenient comprehensive analysis of the problem. The analysis of the obtained data will help adjust the timing and types of repair work, identify structural weak points, and develop innovative methods for preserving historical buildings that are cultural heritage sites. Additionally, it will allow for the most economically optimal construction of modern buildings near architectural landmarks, provide an efficient and safe action plan in emergencies, and modernize existing construction technologies to enhance the comfort of residential buildings, office structures, and other socially significant facilities. It will also aid in selecting the most suitable locations for modern high-precision manufacturing plants.

The paper presents a hybrid artificial neural network model based on an architecture that incorporates a convolutional image encoder (CNN) and an attention module (Attention-based Multiple Instance Learning, Attention MIL). This module aggregates informative features from a sequence of frames capturing the flame spraying process. Additional technological parameters—air pressure, propane pressure, and standoff distance — are integrated into the model via a tabular channel, enabling it to account for the relationship between visual data and numerical process regime characteristics. The software implementation was developed using the Streamlit platform and the PyTorch library. It features an interactive interface for model training and result visualization, analysis of attention weights across frames, and a prediction mode for output characteristics: surface roughness ($R_a$) and the mass of the deposited coating ($m$). Experimental studies were conducted on data from real-world technological processes, and a comparative analysis of the accuracy of various model configurations was performed. The results demonstrate that the hybrid neural network, which combines visual and tabular features, achieves higher prediction accuracy compared to models using only a single modality. Furthermore, when comparing different implementations of the hybrid network, it was established that using the attention mechanism to process the series of flame spray images provides a significant increase in accuracy over a simple averaging of features without attention. The application includes an attention visualization module that creates a montage of the most significant frames and displays their attention weights, allowing users to identify which frames had the greatest influence on the prediction. The model’s capability for export to the ONNX format for integration into process control systems is also demonstrated. The proposed approach showcases the effectiveness of fusing visual and tabular information for manufacturing process monitoring tasks. The model can serve as a foundation for developing a decision support system or an automated quality control system for coatings produced by flame spraying. The limitations of the implemented model and prospects for its further development are also considered.

This article is devoted to the problem of mathematical modeling of nonferrous metal casting and investigation of the influence of main technological parameters on the cooling process of continuously casted copper under down-draw and up-draw.

The problem of accumulation and the statistical analysis of computer experiment results are solved. The main experiment program is considered as the data source. The results of main experiment are collected on specially prepared sheet Excel with pre-organized structure for the accumulation, statistical processing and visualization of the data. The created method and the program are used at efficiency research of the scientific researches which are carried out by authors.

In this paper, we propose an adaptive algorithm that simulates the process of forming the initial behavioral skills on the example of the system ‘eye-arm’ animat. The situation is the formation of the initial behavioral skills occurs, for example, when a child masters the management of their hands by understanding the relationship between baseline unidentified spots on the retina of his eye and the position of the real object. Since the body control skills are not ‘hardcoded’ initially in the brain and the spinal cord at the level of instincts, the human child, like most young of other mammals, it is necessary to develop these skills in search behavior mode. Exploratory behavior begins with trial and error and then its contribution is gradually reduced as the development of the body and its environment. Since the correct behavior patterns at this stage of development of the organism does not exist for now, then the only way to select the right skills is a positive reinforcement to achieve the objective. A key feature of the proposed algorithm is to fix in the imprinting mode, only the final action that led to success, and that is very important, led to the familiar imprinted situation clearly leads to success. Over time, the continuous chain is lengthened right action — maximum use of previous positive experiences and negative ‘forgotten’ and not used.

Thus there is the gradual replacement of the random search purposeful actions that observed in the real young. Thus, the algorithm is able to establish a correspondence between the laws of the world and the ‘inner feelings’, the internal state of the animat. The proposed animat model was used 2 types of neural networks: 1) neural network NET1 to the input current which is fed to the position of the brush arms and the target point, and the output of motor commands, directing ‘brush’ manipulator animat to the target point; 2) neural network NET2 is received at the input of target coordinates and the current coordinates of the ‘brush’ and the output value is formed likelihood that the animat already ‘know’ this situation, and he ‘knows’ how to react to it. With this architecture at the animat has to rely on the ‘experience’ of neural networks to recognize situations where the response from NET2 network of close to 1, and on the other hand, run a random search, when the experience of functioning in this area of the visual field in animat not (response NET2 close to 0).

The purpose of research was to develop a mathematical model for pulsating blood flow in the part of aorta with their branches. Since the deformation of this most solid part of the aorta is small during the passage of the pulse wave, the blood vessels were considered as non-deformable curved cylinders. The article describes the internal structure of blood and some internal structural effects. This analysis shows that the blood, which is essentially a suspension, can only be regarded as a non-Newtonian fluid. In addition, the blood can be considered as a liquid only in the blood vessels, diameter of which is much higher than the characteristic size of blood cells and their aggregate formations. As a non-Newtonian fluid the viscous liquid with the power law of the relationship of stress with shift velocity was chosen. This law can describe the behaviour not only of liquids but also dispersions. When setting the boundary conditions at the entrance into aorta, reflecting the pulsating nature of the flow of blood, it was decided not to restrict the assignment of the total blood flow, which makes no assumptions about the spatial velocity distribution in a cross section. In this regard, it was proposed to model the surface envelope of this spatial distribution by a part of a paraboloid of rotation with a fixed base radius and height, which varies in time from zero to maximum speed value. The special attention was paid to the interaction of blood with the walls of the vessels. Having regard to the nature of this interaction, the so-called semi-slip condition was formulated as the boundary condition. At the outer ends of the aorta and its branches the amounts of pressure were given. To perform calculations the tetrahedral computer network for geometric model of the aorta with branches has been built. The total number of meshes is 9810. The calculations were performed with use of the software package ABACUS, which has also powerful tools for creating geometry of the model and visualization of calculations. The result is a distribution of velocities and pressure at each time step. In areas of branching vessels was discovered temporary presence of eddies and reverse currents. They were born via 0.47 s from the beginning of the pulse cycle and disappeared after 0.14 s.

We describe an engineering-psychological experiment that continues the study of ways to adapt a person to the increasing complexity of logical problems by presenting a series of problems of increasing complexity, which is determined by the volume of initial data. Tasks require calculations in an associative or non-associative system of operations. By the nature of the change in the time of solving the problem, depending on the number of necessary operations, we can conclude that a purely sequential method of solving problems or connecting additional brain resources to the solution in parallel mode. In a previously published experimental work, a person in the process of solving an associative problem recognized color images with meaningful images. In the new study, a similar problem is solved for abstract monochrome geometric shapes. Analysis of the result showed that for the second case, the probability of the subject switching to a parallel method of processing visual information is significantly reduced. The research method is based on presenting a person with two types of tasks. One type of problem contains associative calculations and allows a parallel solution algorithm. Another type of problem is the control one, which contains problems in which calculations are not associative and parallel algorithms are ineffective. The task of recognizing and searching for a given object is associative. A parallel strategy significantly speeds up the solution with relatively small additional resources. As a control series of problems (to separate parallel work from the acceleration of a sequential algorithm), we use, as in the previous experiment, a non-associative comparison problem in cyclic arithmetic, presented in the visual form of the game “rock, paper, scissors”. In this problem, the parallel algorithm requires a large number of processors with a small efficiency coefficient. Therefore, the transition of a person to a parallel algorithm for solving this problem is almost impossible, and the acceleration of processing input information is possible only by increasing the speed. Comparing the dependence of the solution time on the volume of source data for two types of problems allows us to identify four types of strategies for adapting to the increasing complexity of the problem: uniform sequential, accelerated sequential, parallel computing (where possible), or undefined (for this method) strategy. The Reducing of the number of subjects, who switch to a parallel strategy when encoding input information with formal images, shows the effectiveness of codes that cause subject associations. They increase the speed of human perception and processing of information. The article contains a preliminary mathematical model that explains this phenomenon. It is based on the appearance of a second set of initial data, which occurs in a person as a result of recognizing the depicted objects.

Deep learning’s power stems from complex architectures; however, these can lead to overfitting, where models memorize training data and fail to generalize to unseen examples. This paper proposes a novel probabilistic approach to mitigate this issue. We introduce two key elements: Truncated Log-Uniform Prior and Truncated Log-Normal Variational Approximation, and Automatic Relevance Determination (ARD) with Bayesian Deep Neural Networks (BDNNs). Within the probabilistic framework, we employ a specially designed truncated log-uniform prior for noise. This prior acts as a regularizer, guiding the learning process towards simpler solutions and reducing overfitting. Additionally, a truncated log-normal variational approximation is used for efficient handling of the complex probability distributions inherent in deep learning models. ARD automatically identifies and removes irrelevant features or weights within a model. By integrating ARD with BDNNs, where weights have a probability distribution, we achieve a variational bound similar to the popular variational dropout technique. Dropout randomly drops neurons during training, encouraging the model not to rely heavily on any single feature. Our approach with ARD achieves similar benefits without the randomness of dropout, potentially leading to more stable training.

To evaluate our approach, we have tested the model on two datasets: the Canadian Institute For Advanced Research (CIFAR-10) for image classification and a dataset of Macroscopic Images of Wood, which is compiled from multiple macroscopic images of wood datasets. Our method is applied to established architectures like Visual Geometry Group (VGG) and Residual Network (ResNet). The results demonstrate significant improvements. The model reduced overfitting while maintaining, or even improving, the accuracy of the network’s predictions on classification tasks. This validates the effectiveness of our approach in enhancing the performance and generalization capabilities of deep learning models.

Investigation of logical deterministic cellular automata models of population dynamics allows to reveal detailed individual-based mechanisms. The search for such mechanisms is important in connection with ecological problems caused by overexploitation of natural resources, environmental pollution and climate change. Classical models of population dynamics have the phenomenological nature, as they are “black boxes”. Phenomenological models fundamentally complicate research of detailed mechanisms of ecosystem functioning. We have investigated the role of fecundity and duration of resources regeneration in mechanisms of population growth using four models of ecosystem with one species. These models are logical deterministic cellular automata and are based on physical axiomatics of excitable medium with regeneration. We have modeled catastrophic death of population arising from increasing of resources regeneration duration. It has been shown that greater fecundity accelerates population extinction. The investigated mechanisms are important for understanding mechanisms of sustainability of ecosystems and biodiversity conservation. Prospects of the presented modeling approach as a method of transparent multilevel modeling of complex systems are discussed.

The practical application of nuclear medicine demonstrates the continued information deficiency of the algorithms and programs that provide visualization and analysis of medical images. The aim of the study was to determine the principles of optimizing the processing of planar osteostsintigraphy on the basis of сomputer aided diagnosis (CAD) for analysis of texture descriptions of images of metastatic zones on planar scintigrams of skeleton. A computer-aided diagnosis system for analysis of skeletal metastases based on planar scintigraphy data has been developed. This system includes skeleton image segmentation, calculation of textural, histogram and morphometrical parameters and the creation of a training set. For study of metastatic images’ textural characteristics on planar scintigrams of skeleton was developed the computer program of automatic analysis of skeletal metastases is used from data of planar scintigraphy. Also expert evaluation was used to distinguishing ‘pathological’ (metastatic) from ‘physiological’ (non-metastatic) radiopharmaceutical hyperfixation zones in which Haralick’s textural features were determined: autocorrelation, contrast, ‘forth moment’ and heterogeneity. This program was established on the principles of сomputer aided diagnosis researches planar scintigrams of skeletal patients with metastatic breast cancer hearths hyperfixation of radiopharmaceuticals were identified. Calculated parameters were made such as brightness, smoothness, the third moment of brightness, brightness uniformity, entropy brightness. It has been established that in most areas of the skeleton of histogram values of parameters in pathologic hyperfixation of radiopharmaceuticals predominate over the same values in the physiological. Most often pathological hyperfixation of radiopharmaceuticals as the front and rear fixed scintigramms prevalence of brightness and smoothness of the image brightness in comparison with those of the physiological hyperfixation of radiopharmaceuticals. Separate figures histogram analysis can be used in specifying the diagnosis of metastases in the mathematical modeling and interpretation bone scintigraphy. Separate figures histogram analysis can be used in specifying the diagnosis of metastases in the mathematical modeling and interpretation bone scintigraphy.