Computational nanotechnology

In life, we often have to take into account the accuracy of measurements. There is obviously a desire to have the measured value as accurately as possible. This applies to both static and dynamic measurements. Measurements may be made using one or more meters and involve errors that may be systematic or random. The usual approach to obtaining a more accurate value of a measured parameter is the averaging method. This is a simple and quite effective method, especially if the measurements are equally accurate. If there are n measurements, then the averaging method is the addition of n measurements with the same weighting coefficients. The larger n, the more accurate the estimate will be. But with different-precision measurements, the result may not be optimal. To obtain an optimal estimate (estimates with minimal error variance) for multi-precision measurements, the weighting coefficients must take into account their statistical accuracy. Optimal weighting coefficients should ensure a minimum variance of the estimation error. This is the method of statistical filtering of random errors. Statistical filtering of random errors is also applicable for multidimensional problems. For example, its special case is the so-called “Kalman filter”.

The article examines the application of artificial intelligence algorithms, specifically YOLO v8, for image analysis in the construction industry. The focus is on tasks such as automatic object detection, including window openings and building floors, using data from remote sensing technologies. The stages of technology implementation are described: data collection and processing, visualization, and its use in construction project management. The authors highlight the efficiency of the proposed solution, its high accuracy, and scalability potential. Limitations related to model tuning and the need for adaptation of regulatory standards are also discussed. The project is aimed at optimizing and digitizing construction processes.

The work discusses the use of multidimensional measuring and control converters into secondary signals, modern hardware and software tools for measuring and monitoring power supplies in the process of power supply to computing and infocommunication devices, as well as the principles of monitoring and receiving signals to ensure uninterrupted and high-quality operation of devices. In accordance with this, the principle of constructing the model and the software of working and transmitting devices are given. The developed power management device is based on the ongoing processes and selected functional modules. Based on the main purpose of the study, a monitoring algorithm and a software package based on an embedded system were developed. Based on the implemented monitoring model, the integration processes between hardware and software are described in detail.

This article explores the potential application of artificial intelligence (AI) technologies to enhance the operational efficiency of small and micro-enterprises (SMEs) in Russia. The aim of the study is to generalize experience and develop recommendations. Considering the limited resources and specificity of SME activities, the authors examine the challenges faced by these enterprises, as well as the broader technological context. Utilizing computer technologies and data analysis, the authors extracted descriptions of SME representatives’ experiences from the russian professional social network TenChat. The extracted algorithmic themes were expertly transformed into directions for the application of AI technology and formed the basis for the development of practical recommendations. The study formulated conclusions on possible directions for the application of AI technologies, including marketing and customer service, automation and optimization of routine tasks, data analysis, and process optimization. The developed recommendations include strategic decisions, AI reactivity, team management, operational activity provisioning, and data management. The results of the study are of interest to entrepreneurs, SME managers, and researchers seeking to enhance business efficiency through AI technologies.

The research paper is devoted to the study of graph approach application in the context of testing and optimization of IT projects execution. Graph structures are a powerful tool for modeling complex systems and processes, which makes them applicable in various areas of information technology. The article discusses the basic principles of graph models for project analysis and management, as well as methods of their application in testing and optimization of IT project execution processes. Implementation of graph methodology at each stage of testing in the project life cycle can lead to improvement of efficiency and quality of the testing process, as well as to increase the quality and competitiveness of the developed product.

Defining requirements is one of the most important stages of software system development. Mistakes made at this stage are very costly after the system is developed and implemented. Currently, despite the vast experience gained in the development of automated information systems, mobile applications and services in the computer industry, the problems associated with the development of requirements remain unresolved. The article emphasizes the need for a careful approach to substantiating and developing requirements at the initial stages of software system development, because no matter how well a software system is implemented, the requirements for which were initially incomplete, ambiguous or incorrect, the result of its work will greatly disappoint the user. It is also important to make sure that it is necessary to develop exactly the software that the customer is talking about, and that all participants have a common vision of the product. The features of the justification and development of requirements for a software system presented in the article relate to the automation of business processes of a particular organization. Different research theories and methodologies were used in the work. The business process described in the IDEF0 notation in the “as is” state made it possible to identify the main shortcomings of the existing technology for executing the organization’s business processes and present countermeasures to eliminate them in the “as it will be” state. The result of the work were specifications of requirements for the development of a mobile application. The research was conducted with the involvement of end users of the product. Constant interaction with the customer and end users allowed us to avoid problems related to the development of requirements. Analysis and design models were actively used at all stages of requirements development. The calculations carried out in the study showed that the developed requirements will allow achieving the set business goal of the customer’s company. The models presented in the article, certain requirements and implemented ideas can be used by business analysts and developers when developing their own software systems.

This article provides an overview of the integration approach to documentation based on metagraphs. This approach synthesizes the principles of documenting methods of working with graph data structures, allowing you to describe the relationships and interactions between various elements of information in the documentation. The article examines the importance of metagraphs for the organization of information, their impact on structuring and the value of documentation for users. The architectural framework of metagraphs, their flexibility in adapting to changing requirements, as well as their role in ensuring consistency and quality of the material are analyzed in detail. The use of metadata and meta-texts for structuring information, sharing and coordinating efforts in a team is considered. The article highlights the advantages of the integration approach to documentation based on metagraphs in creating a unified understanding of the subject area and simplifying work with distributed data.

The article is devoted to the relevance of the use of cloud technologies in the modern world; data on the size of the global market for public cloud services over the past two years is presented. The advantages of using cloud technologies are determined, the classification of cloud systems is considered. The main regulations governing the operation of cloud storage are given. Particular attention is paid to the issues of information security of cloud systems, the most dangerous attacks that are relevant in the field of cloud technologies are presented, their characteristic features and the possible consequences of their occurrence for users and organizations are described. As a solution for creating a cloud system with a high level of security, the concept of multi-level security has been defined, including encryption, multi-factor authentication, the use of the TLS protocol, protection against DDoS attacks, timely monitoring of infrastructure security risks and organizational security policy.

The work is devoted to the development of a mathematical model for the evaluation of non-stationary performance characteristics of medical information-measuring systems used in medicine to assess the condition of patients in critical health and life situations. The model is a unilinear mass service system with a finite queue, Poisson input flow and impatient requests, adequately describing the functioning of real-time medical systems including under conditions of equipment malfunctions and failures. The paper presents a system of Kolmogorov differential equations describing the mass service system under study, as well as its solution based on the method of probability transformation matrix. Expressions for finding the probabilities of the system states at an arbitrary moment of time are obtained, as well as non-stationary characteristics of the system performance, such as loss probability, throughput, and transient time. The results of numerical calculations are presented for a system with a buffer size equal to two packets at different ratios of the intensity of leaving impatient requests from the queue and service intensity.

This paper presents a deep learning-based algorithm for identifying oil pollution on water surfaces using multispectral images from a 5-channel camera obtained from unmanned aerial vehicles (UAVs). The algorithm, based on the Unet architecture with the efficientnet-b0 encoder, demonstrates high segmentation accuracy and is part of an environmental monitoring system. Using data on natural and controlled oil spills, as well as organic discharges, the method has been field tested on various water bodies, which confirms its efficiency and reliability in the prompt detection of pollution. Particular attention in the article is paid to the accuracy and speed of the algorithm. The developed method has a high data processing speed and can be successfully applied in various climatic conditions. The results demonstrate that the proposed algorithm is able to automatically detect even minor pollution of water surfaces, which allows for a prompt response to environmental disasters and minimize their consequences. The proposed algorithm has shown high results. With the selected model configuration, the Dice Loss metrics were achieved at the level of 0.00265 and the IoU Score equal to 0.9971. These high values confirm the reliability and accuracy of the proposed approach, ensuring accurate identification of oil spills.