The article presents a system of remote control and management of car functions, developed using innovative technologies of the Internet of Things (IoT) and wireless networks. During the development of the system, technologies such as sensors for monitoring the technical condition of the car, a website for remote access, and microcontrollers for data processing and remote control of the car's functions were used. The use of these technologies allows vehicle owners to monitor vehicle parameters such as fuel level, tire pressure and condition, cabin air quality, climate control system, and perform remote operations such as opening doors or windows or starting the engine. This system increases the safety and convenience of using cars in the modern world. Keywords - car remote control system, wireless networks, Internet of Things, sensors, car safety, remote control.

The work is dedicated to the study of fundamental prompting techniques to improve the efficiency of using large language models (LLMs). Significant attention is given to the issue of prompt engineering. Various techniques are examined in detail: zero-shot prompting, feedback prompting, few-shot prompting, chain-of-thought, tree of thoughts, and instruction tuning. Special emphasis is placed on Reaction & Act Prompting and Retrieval Augmented Generation (RAG) as critical factors in ensuring effective interaction with LLMs. The features of applying these techniques and their impact on results are highlighted. However, leveraging their full potential requires a careful approach and consideration of application specifics. A review of the parameters of large language models, such as temperature, top P, maximum number of tokens, stop sequences, frequency and presence penalties, etc., is provided. It is noted that prompt development is an iterative process that involves sequential testing of different options to achieve optimal results. All techniques discussed in the study are supported by illustrative examples with obtained results. It is indicated which types of tasks each technique is more suitable for. The study results include comparisons of both fundamental techniques and more advanced technologies such as ReAct and RAG. Prompt engineering is a key technology for the effective use of large language models. It is relevant due to the increasing application of artificial intelligence in all areas of human activity, and its role will only grow with the development of technology. The ability to correctly formulate prompts is becoming an important skill necessary for working with modern large models, especially given their versatility and complexity. Keywords: large language models, prompt engineering, prompting technique, content generation.

Abstract: The relevance of the research is determined by the need to optimize traffic light control at intersections to reduce congestion and delays and increase the capacity of intersections. A practical solution to this problem is using intelligent transport systems and specific decision-making subsystems. However, automating such tasks requires scientific research to develop effective algorithms suitable for practical use. This work proposes an approach to optimizing traffic light control at intersections that considers the traffic flow parameters at a specific intersection and those at adjacent intersections, utilizing an ant colony optimization algorithm to optimize traffic light control at neighboring intersections. The results obtained show that this approach is more effective compared to existing methods and has the potential to reduce delays by 10% and increase intersection capacity by 15% and more. Keywords: Adaptive traffic management, cyber-physical system, intersections, jams, traffic, traffic light controller.

In modern information systems, the client-server architecture is one of the most common models of interaction between users and servers. It ensures a clear distribution of responsibilities between the client, which makes requests for information or operations, and the server, which processes them and provides responses. With the development of technology and the increase in the load on servers, there is a need to improve the interaction mechanisms to ensure greater efficiency and performance of the system. One of the key aspects affecting the efficiency of client-server systems is the use of CDN networks. Using CDN reduces response time and improves overall system performance. This technology is becoming increasingly popular due to its ability to increase the efficiency and scalability of modern web resources and services. The relevance of this topic is that CDN networks can solve a number of problems associated with high server loads and decreased system response speed, which is especially important in conditions of a large number of users simultaneously using network resources. The purpose of this study is to analyze the efficiency of client-server interaction using CDN, as well as to study technologies that improve the efficiency of this interaction. The study will examine the theoretical foundations of client-server systems using CDN and analyze the efficiency of their use. Keywords: client-server, CDN network, response time, work optimization, performance.

The article investigates the process of block formation in blockchain networks and the impact of node network architecture and consensus algorithms on their scalability and performance. Analysis of blockchain system scalability is important due to problems that arise when network load increases, particularly the increase in the number of block forks and transaction confirmation times. The research focuses on studying the impact of network delays and the choice of consensus algorithm on the performance and scalability of blockchain networks. The main attention is devoted to mathematical models that describe block formation, as well as the analysis of factors affecting transaction processing speed and throughput. The primary consensus algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), are considered, and their impact on scalability in implementations based on the Ethereum Virtual Machine (EVM) and Bitcoin is compared. Experimental studies using Geth and Amazon cloud services revealed that the application of the Proof of Stake (PoS) consensus algorithm increases network performance by reducing the complexity of the block formation process in blockchain networks by 99% and accelerates consensus achievement by 70% compared to Proof of Work (PoW). It was also established that increasing the number of nodes from 5 to 50 reduces the network's throughput by almost 10%, and the average confirmation time doubles. The obtained results are aimed at solving the scalability issue by reducing transaction confirmation times for the implementation of decentralized technologies in the Internet of Things (IoT) sphere, where processing speed and storage of large volumes of data are critically important. Keywords: blockchain, block formation, consensus algorithms, decentralized technologies, Ethereum Virtual Machine (EVM), Internet of Things (IoT), mathematical modeling, network delays, scalability.

In this work, a review of logarithmic analog-to-digital converters (LADCs) was carried out and an analysis of their properties in the dynamic range of input signals of 80 dB was carried out. It is shown that the highest metrological characteristics are obtained by LADCs on switched capacitors (CC) using high-quality analog switches from Maxima and Analog Devices companies, in which parasitic interelectrode capacitances do not exceed 1 pF. LADC of different classes were considered. Serial LADCs on CC have the lowest speed, they are performed with redistribution or accumulation of charge (RC or AC) in capacitor cells, in which switching is carried out with analog switches; in such LADCs, the conversion error can be reduced to 0.25% (taking into account the quantization error of 0.1%) with a conversion time of no more than 20 ms. The same speed has LADC with pulse feedback, the conversion error of which is almost completely determined by the value of the quantization error for values ​​of the last 0.1% and more. Interpolation LADCs make it possible to reduce the conversion error below 0.1% with a conversion time of the order of hundreds of microseconds. Medium-speed LADCs with a conversion time of 100 μs or less include subband, recurrent, and bit-by-bit, which achieve a conversion error of 0.005%, 0.0015%, and 0.0015%, respectively. High-speed LADCs are parallel, their conversion error does not exceed 0.4% with a conversion time of less than 10 ns. Key words: logarithmic ADCs, construction, characteristics, parameters

The methods for identifying persons of interest (POI) based on mobile device data has been considered. The problem is relevant and unresolved in the activities of law enforcement intelligence and other agencies involved in operational search activities due to the large amount of data stored on mobile devices. Given the complexity and volume of mobile data traditional analysis methods are often insufficiently effective. The authors propose use of artificial intelligence (AI) including machine learning and natural language processing to improve the efficiency and speed of mobile device data analysis. This approach aims to overcome the limitations of manual data analysis and enhance the process of identifying POIs while adhering to the principles of forensic integrity. The research specifically demonstrates how machine learning can be utilized to search for persons of interest in WhatsApp messenger data. A method has been developed for decentralized control of adaptive data collection processes using the principle of equilibrium and reinforcement learning using the normalized exponential function method. The developed method allows for efficient operation of autonomous distributed systems in conditions of dynamic changes in the number of data collection processes and limited information interaction between them. The results of the experiment indicate that using artificial intelligence for facial recognition may result in false positive outcomes implying that humans cannot be entirely replaced at the current stage of AI evolution. However the application of deep learning showed an 88% success rate in facial recognition. These findings underscore the transformative potential of artificial intelligence in mobile forensics highlighting its capacity to enhance the accuracy and efficiency of data analysis in mobile devices. Key words: artificial intelligence mobile forensics data analysis ios whatsapp

This article explores the creation of a data classification policy in line with SOC2 Type 2 compliance requirements. SOC2 Type 2 is a notable certification that attests to an organization's ability to adhere to the Trust Services Criteria including security availability processing integrity confidentiality and privacy. The initial and crucial step in formulating a solid data security strategy is data classification which helps organizations recognize their data and assign a sensitivity level guiding the appropriate security measures. Data classification aims to organize and manage data in a manner that enhances its protection and aligns with the organization's overall data security strategy. In the data classification process data security has a central role as it directly impacts the protection and management of classified data. The design of a data classification policy for SOC2 Type 2 compliance presents several challenges and considerations. Organizations must understand the scope of their data align with the Trust Services Criteria balance security with usability provide training and awareness conduct regular updates and reviews define classification levels ensure consistency automate classification integrate with other policies and controls handle third-party vendors monitor and enforce and comply with legal and regulatory requirements. Keywords: SOC2 Type 2 storage standards data classification data storage data security.

The National Cybersecurity Cluster of Ukraine is functionally oriented towards building systems to protect various platforms of information infrastructure including the creation of secure technologies for detecting deepfake modifications of biometric images based on neural networks in cyberspace. This space proposes a conceptual approach to detecting deepfake modifications which is deployed based on the functioning of a convolutional neural network and the classifier algorithm for biometric images structured as 'sensitivity-Yuden index-optimal threshold-specificity'. An analytical security structure for neural network information technologies is presented based on a multi-level model of 'resources-systems-processes-networks-management' according to the concept of 'object-threat-defense'. The core of the IT security structure is the integrity of the neural network system for detecting deepfake modifications of biometric face images as well as data analysis systems implementing the information process of 'video file segmentation into frames-feature detection processing - classifier image accuracy assessment'. A constructive algorithm for detecting deepfake modifications of biometric images has been developed: splitting the video file of biometric images into frames - recognition by the detector - reproduction of normalized facial images - processing by neural network tools - feature matrix computation - image classifier construction. Keywords: biometric image deepfake modifications neural network technology convolutional neural network classification decision support system conceptual approach analytical security structure.

In this article the authors focused on analyzing the possibilities of using artificial intelligence models for effective detection and analysis of cybercrimes. A comprehensive method using artificial intelligence algorithms such as Random Forest and Isolation Forest algorithms is developed and described to detect ransomware which is one of the main threats to information security management systems (ISMS) in the field of critical infrastructure. The result of the study is the determination of the compatibility of such methods with the requirements of ISO 27001:2022 emphasizing the importance of integrating innovative AI technologies into already existing security systems. In addition the article analyzes the potential advantages of such integration including compliance with the requirements of international information security frameworks. Keywords: Isolation Forest Random Forest critical infrastructure information security management system ISO 27001 cyber security cyber security standard cybercrime ISMS ransomware siem edr security monitoring antivirus machine learning computer networks information systems.