This review article presents a generalized, integrated classification of scoring functions for molecular docking of protein interaction models. It describes various types of scoring functions and reports relevant issues in biopharmaceuticals, medical cybernetics, computational biology and biophysics, applied mathematics. This article may be of interest to a wide range of readers, including scientists from various fields, software engineers, developing automated bioinformation processing systems, lecturers on relevant topics, graduate and postgraduate students in bioinformatics, systems analysis, medical software systems research

The article examines an approach to the design and analysis of the organizational model of production management at an industrial enterprise based on cognitive and simulation modeling. The relevance of the study is due to the need to improve industrial enterprise management systems under conditions of high external environmental variability, increasing complexity of production and managerial relationships, and the insufficiency of static optimization models for describing the dynamics of organizational processes. The aim of the study is to formalize the organizational model of production management at a metallurgical enterprise using a causal loop diagram and to implement it subsequently in a simulation modeling environment. The research employs methods of systems analysis, economic-mathematical modeling, cognitive modeling, and system dynamics modeling. The organizational structure of the enterprise is presented as a set of production and functional subsystems interconnected through vertical and horizontal managerial interactions, while the key parameters of the model include span of control, management personnel maintenance costs, production capacity, sales volume, and net profit. The constructed cognitive map made it possible to identify the main cause-and-effect relationships among the organizational, production, and financial parameters of the enterprise, while the simulation model enabled an assessment of the impact of managerial decisions on the dynamics of costs and financial performance. The results obtained show that the use of cognitive maps in combination with simulation modeling expands the possibilities for analyzing the stability of the organizational structure and for substantiating decisions aimed at its improvement. The practical significance of the study lies in the possibility of applying the proposed approach to the design and adjustment of management systems in metallurgical and other industrial enterprises.

In this article, the necessity of applying a systematic approach to ensuring safety in the grain drying process at grain drying complexes and ensuring grain quality, as well as the efficiency of the drying process, is justified. An analytical mathematical model of the drying process has been developed in the form of a system of differential equations that describes the dynamics of temperature change in the grain drying process.

This scientific article presents a comprehensive theoretical and applied study dedicated to the optimization of atmospheric air monitoring systems in modern cities with populations over 100,000. The relevance of this study stems from the need to transition from traditional passive static monitoring methods to dynamic adaptive models of environmental quality management, a key element of the "smart" and "symbiotic" city concept for minimizing environmental risks. Four major industrial and administrative centers of the Republic of Belarus – Grodno, Pinsk, Lida, and Soligorsk – were selected as the research sites. The scientific novelty of this study lies in the development of an integrated approach combining a mathematical solution to the optimal sensor placement problem (SLP) with a multivariate graphical analysis of anthropogenic impact. A detailed methodology is provided for conducting 20,000 iteration cycles for each transport scheme, which made it possible to identify critical nodes in urban framework networks where the concentration of sensors provides minimal error in calculating transport and environmental parameters. Particular attention is paid to the interdisciplinary consideration of meteorological factors, in particular, the use of a weighting factor for wind rose indicators when prioritizing gas analyzer installation locations. The article systematizes data on traffic load, the hierarchy of street and road networks, and the density of social facilities in the studied cities. Implementation of the proposed model (based on 32 automated posts) allows for the generation of symbolic solutions for assessing the environmental situation in unmonitored network sections. The study's results form the basis for the development of predictive emission management systems capable of adjusting traffic management in real time to prevent exceeding maximum permissible pollutant concentrations. The conclusion outlines promising areas for integrating the resulting models with machine learning and artificial intelligence algorithms within the framework of longterm sustainable urban development programs.

This article examines the general principles of constructing a decision support system for controlling an unmanned aerial vehicle (UAV) from a ground control station based on measurements and assessment of the technical condition of the UAV's onboard systems. An analysis of current methods for solving such problems is provided, and the use of algorithmic solutions based on stochastic mathematical models of the processes of change in variables characterizing the UAV's technical condition is substantiated. General differential equations describing the evolution of the system under consideration are presented, derived from the theory of systems with random structure changes. A block diagram characterizing the general principles of solving the problem of probabilistic assessment of the system's technical condition is presented, based on which a discrete algorithm for solving the problem of optimal filtering and identification of the operating mode with monitoring process control is derived. An example of solving the problem for simple mathematical models of the process of change in a technical parameter and its measuring device is given, clearly demonstrating the operability of the proposed algorithm.

The article examines modern approaches to forming an optimal investment portfolio in the cryptocurrency market, including classical methods of technical analysis. Trends in the development of the global digital financial assets market are studied. Various interpretations of the concept of investment strategy are considered. Particular emphasis is placed on the Markowitz model. Its basic version and modification for crypto-futures and crypto-options are reviewed. A computer implementation of the model was developed, numerical experiments were conducted, followed by a substantive interpretation of the obtained results. An algorithm for solving the nonlinear optimization problem using pre-trained neural networks and the Pyomo framework was developed. Additionally, alternative approaches to cryptocurrency portfolio optimization are considered.

While use algorithms of pre-processing raster image involves improving image characteristics to use qualitative transformation effect result on following processing stages. Assessing the effect of an impact on a raster image is not a trivial task, where the choice of further processing steps and algorithmic tools depends on the quality of its implementation. The algorithm for balanced "extension" of boundary pixels of a static raster image is as example. Algorithm developed to improve the results of object edge detection algorithms based on the two-dimensional convolution operation, where the improvement consists of reducing the number of sharply contrasting pixels at the boundaries of raster images relative to adjacent ones. Existing numerical raster images estimates of PSNR and SSIM which obtained after applying the balanced algorithm of redefining border pixels have shown their low efficiency due to the small number of pixels affected by the transformations. The tonal deviation of raster image boundary pixels is alternative assessment criterion. The object of study in the article is the pixel matrix of three-channel color static raster image. The subject of the study is the evaluation of the tonal deviation of the boundary pixels of a static raster image obtained by used balanced algorithm for redefining border pixels. The main objective is to calculate the tonal deviation estimates of boundary static raster images after applying balanced algorithm of "redefining" border pixels, analysis and ranking by alternative methods of "extension" of boundary sizes according to the criterion of tonal deviation. The article provides a definition of the concept of tonal deviation and justification for the use of tonal deviation assessment. An example of calculating the tonal deviation of pixels after applying the balanced algorithm of "redefining" border pixels and the copy method for 1 thousand color three-channel raster images from the SpaceNet Satellite Images dataset from ImageNet is presented. It was carried out short analysis of the obtained assessment results and recommendations for choosing the best methods of "redefining" border pixels.

The article presents the results of the development and testing of a prototype automated system for remote screening of diabetic retinopathy (DR), featuring preprocessing and classification functionalities for digital images acquired by fundus cameras from various manufacturers. Experimental testing of the prototype was performed on several thousand real-world images from Belarusian medical institutions and open-access sources. Objective assessment of retinal image quality is computed through analysis of the central fragment based on the Weibull distribution scale parameter applied to local quality estimates of the green channel of the image. The prototype was developed on the Streamlit platform and employs the YOLOv8 and YOLOv11 neural network architectures for classifying five stages of DR, achieving classification accuracy of 84.0 % and 82.7 %, respectively. The combination of low-quality image filtering and neural network-based classification enhances the reliability of screening diagnostics and can be deployed in telemedicine systems with limited computational resources.

The neural network for diagnosis is trained on international data from patients with neurological diseases (ND). The neural network (NN) is dynamically updated with data on the condition of a particular patient to support therapy. An IoT device (smartphone) collects patient data (voice, movement), initiating their preprocessing and feature extraction. This data is transmitted via the local Flask framework to a server using Open Semantic Technology for Intelligent Systems (OSTIS), a platform that processes and interprets patient information. An NN-based forecasting agent on the server, using a trained neural network, performs the patient's diagnosis, which is transmitted to the doctor. Treatment of ND requires continuous, adaptive and individual therapy. The NN-based electronic therapy unit, which includes modules for patient and doctor, decision-making and the patient's digital twin, expands the diagnostic system to support therapeutic decisionmaking. The block searches for the patient's previous condition; predictive modeling: course of treatment, drug therapy; explanation for the doctor. The block structure includes: a recurrent neural network based on GRU, a component of the semantic memory of the OSTIS system, context-sensitive rules in the form of SC-graphical forms.

The article is devoted to the study of new architectures of artificial neural networks aimed at improving classical transformers by reducing computational costs and increasing efficiency. The main stages of the evolution of transformers are considered, starting with the introduction of the mechanism of self-attention and ending with modern sparse models. Particular attention is paid to optimization techniques such as parametrically efficient fine-tuning (PEFT), adaptive layers (adapters), as well as the use of external knowledge repositories and memory vectors to expand the long-term memory of models. The article concludes with a discussion of current achievements, limitations, and promising areas for future research in this area.