The relevance of the topic of the article is due to the fact that currently there is no linking of the calendar plan to the schedule for the development of capital investments. The principle of constructing a schedule for the development of capital investments is proposed, which ensures the uniformity and proportionality of the use of financial resources. Schedules for the development of capital investments are constructed, each schedule for the development of capital investments corresponds to an image describing the change in the absolute value of the profitability of the investment and construction project. The use of a mathematical apparatus describing the movement of a material point can be used in the development of investment schedules and calendar plans for construction. The construction of construction schedules in the section of the construction organization project based on investment schedules will have a significant impact on the economic efficiency of investment projects.

Keywords: construction schedule, investments, return on investment, development of capital investments, duration of construction, efficiency of capital investments

The article discusses a method for detecting local areas with hidden defects in products whose length is several orders of magnitude greater than other dimensions, when processing information from non-destructive testing of the product. To obtain the necessary information, various means of introscopy and radiation of different nature are used. Processing of information obtained using scanning control should detect areas with defects and determine their nature. To compare different processing methods and select the optimal method for processing information, a computer modeling method was used, with the help of which the process of obtaining information and processing it was simulated, which simplifies the selection of the most suitable method for detecting a defect. The article describes typical models of the received signal and presents the simulation results.

Keywords: defects, non-destructive testing, extended products, simulation model, moving averaging, time series

The article discusses the conducted studies of changes in the output signal from a measuring device to assess the quality of mixing natural and chemical fibers in semi-finished products of spinning production obtained on a belt machine at various transitions. The construction of polynomial models in data analysis makes it possible to interpret information about the uniformity of fiber distribution in the tape, without taking into account the effect on changes in its linear density.

Keywords: fiber mixing quality, linear density, infrared estimation method, data estimation, linear polynomial, polynomial function

The effect of impurities on the conductivity of the allotropic modification of phosphorene (ε-P) was studied. The quantum chemical research package Quantum Espresso was used for calculations. The study was conducted on the basis of simulation modeling. Three-dimensional structures are modeled using Quantum Espresso. The work adapted the model for two-dimensional ε-phosphorene by choosing a unit cell vector so large that interaction between the layers was possible. It has been shown that S has the smallest changes. Significant changes in conductivity can be achieved by placing F in various configurations relative to the crystal lattice, which can be actively used to create differetnt types of detectors.

Keywords: phosphorene, allotrope, semiconductor, impurity, adsorption, conductivity, density functional theory, modeling, Quantum Espresso, crystal structure

The article explores the interaction of Tello EDU, a small-sized educational drone, with Turtlebot3, an unmanned ground vehicle, in rooms with a weak signal. The article examines how, using a local network and the robot operating system (ROS), it is possible to achieve effective collaboration between these two devices. It analyzes how a local network can be used to broadcast data and monitor devices in conditions of a weak external signal. The role of ROS as the main tool for managing and interacting with devices is being investigated. In addition, the article examines specific use cases, including interaction and coordination between Tello EDU and Turtlebot3. A diagram of the interaction of two unmanned vehicles is also presented, a detailed description of their operation is described, and Python code is presented using various libraries based on the ROS robotic operating system.

Keywords: Tello Edu, operating system (ROS), UAV, local area network, Wi-fi, nodes, SLAM, weak signal, route planning, autonomous robot, Turtlebot3

The paper analyzes and shows the inadequacy of the technology for repairing production wells by rolling out a corrugated metal patch with a dornating device, which consists in premature violation of the tightness of the well due to low strength of the connection. A technology and a hydromechanical device for additional fastening by radial extrusion of spherical cavities in the metal of the plaster are proposed. As a result, a drag force is generated in the cross-section of the fastener, which prevents shear loads from axial forces. Strength calculations have shown that for better performance of the metal patch, it is advisable to increase the shear resistance force by increasing the number of punches or technological transitions of fastening.

Keywords: casing, production string, well tightness, metal plaster, radial extrusion, strength

The problem of developing an intelligent automated system for detecting defects in textile materials is considered. An analysis of machine learning and deep learning algorithms was carried out in relation to solving the problem of product quality control. The implementation of an artificial neural network implemented in a Raspberry Pi microcomputer and receiving a set of input data in the form of a large stream of images from a high-speed digital camera is considered. The stages of creating a model in Python using the TensorFlow and Keras libraries are described. The development process includes the preparation of initial data intended for training and testing the system, as well as testing the operation of the resulting neural network, which consists in recognizing images of defects on fabric according to classification criteria.

Keywords: machine learning, neural network, defect images, textile material, training, testing, accuracy

The article presents a mathematical model for assessing the applicability of intelligent chatbots in the context of studying dialects of foreign languages. The model is based on the analysis of key parameters and characteristics of chatbots, as well as their ability to adapt to various dialects. The model's parameters include questions, answers, evaluation criteria, types, and costs of errors. The quality of the chatbot's responses is evaluated both according to individual criteria and overall. To test the effectiveness of the proposed method, an experimental study was conducted using the dialects of the German language as examples. During the research, such intelligent chatbots as ChatGPT-3.5, GPT-4, YouChat, Bard, DeepSeek, and Chatsonic were evaluated. The analysis of the results of applying the developed mathematical model showed that at present, the models by OpenAI (ChatGPT-3.5 and GPT-4) offer the broadest range of possibilities. ChatGPT-3.5 demonstrated the best results in communication in Bavarian and Austrian dialects, while YouChat excelled in the Swiss dialect. The obtained results allow for important practical recommendations to be made for selecting intelligent chatbots in the field of studying dialects of foreign languages and serve as a basis for further research in the area of evaluating the effectiveness of educational technologies based on artificial intelligence.

Keywords: large language model, chatbot, quality assessment, foreign language learning, artificial intelligence technology in education

Fuel efficiency of dump trucks is affected by real world variables such as vehicle parameters, road conditions, weather parameters, and driver behavior. Predicting fuel consumption per trip using dynamic road condition data can effectively reduce the cost and time associated with on-road testing. This paper proposes new models for predicting fuel consumption of dump trucks in surface mining operations. The models combine locally collected data from dump truck sensors and analyze it to enhance their capabilities. The architectural design consists of two distinct parts, initially based on dual Long-term Short-Term Memories (LSTMs) and dual dense layers of Deep Neural Networks (DNNs). The new hybrid architecture improves the performance of the proposed model compared to other models, especially in terms of accuracy measurement. The MAE, RMSE, MSE and R2 scores indicate high prediction accuracy.

Keywords: LSTM algorithm, DNN, density, prediction, fuel consumption, quarries

The work is devoted to the study of the effect of electrical pulses on the processes of cisplatin transport through the plasma membrane. Technical devices based on the application of this technology are used to increase cellular uptake of cytotoxic agents within certain cancer treatment strategies. This article presents the results of mathematical modeling performed using computer methods that allow us to study the influence of different pulse parameters (amplitude, duration, frequency) on the efficiency of cisplatin transport. Numerical experiments using various difference schemes and mathematical models that take into account the physical properties of the plasma membrane have been performed. The results obtained allow us to better understand the mechanisms of the impact of electrical impulses on the processes of cisplatin transport, which may be of practical importance for the development of new methods of drug delivery and cancer treatment.

Keywords: mathematical modeling, software package, electroporation, cisplastin transport, plasma membrane, computational experiment

The article discusses the application of machine vision methods for embedded systems using modern microcontrollers. Machine learning methods that are used in embedded systems to solve recognition problems, as well as neural network models, are described. The use of trained models for solving image recognition problems in embedded systems is proposed. The architectures of YOLOv3 and R-CN neural networks are compared. The Jetson TX2 hardware platform is considered. The results of comparing the calculation speed for different modes of the device are presented.

Keywords: machine vision, neural networks, artificial intelligence, embedded systems, pattern recognition, YOLO, RCN, Jetson, Tensorflow

In today's world, facial recognition is becoming an increasingly important and relevant task. With the development of technology and the increasing amount of data, the need for reliable, accurate and efficient face recognition systems increases. Neural networks demonstrate high efficiency in solving computer vision problems and have great potential for improving existing mathematical models of face recognition. This paper is devoted to the study of methods for human face recognition, the Viola-Jones algorithm will be discussed in detail, which, which can be applied in the task of face recognition using neural networks. It will also analyse techniques for training deep learning models using libraries that also use the Viola-Jones algorithm and describe an algorithm for using the trained model in an API that can be used in desktop and mobile applications.

Keywords: biometric identification, human face recognition, mathematical models, face recognition methods, deep learning, convolutional neural networks, tensorflow

The article discusses the development of a software module for controlling electric drives as part of the ModBus industrial network, which was intended for modernize the automated control system of the production line section of the "Zarya" bakery plant. Frequency converters are used to change the speed of motors in the available control system, and the operator has to manually control each converter. The rotation speed of the motors is controlled by setting the frequency value, which is inconvenient for the operator. It is impossible to simultaneously change the operating speed of all production line equipment with this control method, which leads to mismatch in equipment operation. To solve this problem, a network is being built based on the ModBus protocol, based on available equipment. The article describes the main peculiarities of the ModBus protocol, developed method, that allows the operator to specify the required system run time instead of the frequency value for each motor, and developed software module, that implements the proposed method.

Keywords: programmable logic controller, ladder diagram, ModBus protocol, controlling without feedback, frequency converter

The development of a decision support system for evaluating a fashionable image of a person is described. This is done by selecting a set of visual attributes from an image and comparing this set with "fashionable" patterns. Fashion patterns are set by the user himself. These are images that are defined in the system as reference images. This paper provides an overview of decision-making methods, analyzes the relevance of decision-making systems in different spheres of society. The algorithm of the program and the tools with which the image is first preprocessed are considered, then the visual attributes are highlighted. The method of making decisions for different types of attributes is given. The comparison of colors in HSL notation is considered.

Keywords: decision support system, decision making methods, machine learning, Python, model learning, image, fashion, information and analytical system, k-means method

The article discusses the developed model for recognizing a clothing brand by image. The model not only predicts the type and brand of clothing, but can also determine their similarity. At the initial stage, a dataset was collected containing images of clothing from various brands with a total volume of 9,000 images. In this work, the ViT (Vision Transformer) neural network architecture was used, a model for working with images, which was presented by experts from Google Brain. The vit-base-patch16-224 model acted as a representative of the transformer architecture. Before training, all images were converted to black and white, and data augmentation was also used: image rotation by a random angle, mirror transformation. All photos have been normalized – pixel coordinates have been adjusted to the interval [0,1].

Keywords: neural network, model, machine learning, Vision Transformer, fashion industry, clothing brand prediction, clothing type prediction, brand similarity determination

The text describes software tools for analyzing the structure of metallic materials, including ferrous and non-ferrous metals. It presents image processing methods for edge detection and segmentation of structural elements on the metal surface. A Python program is described, which applies watershed algorithms and searches for white and black grains to segment metal images. The program performs analysis of grain sizes and shapes, and the results are presented visually and for further use. This tool is crucial for quality control and optimization of the properties of metallic materials.

Keywords: software tool, metal, quantitative analysis of microstructure, computer program, Python programming language