The breakthrough in developing large language models (LLMs) over the past few years has led to their widespread implementation in various areas of industry, business, and agriculture. The aim of this article is to critically analyse and generalise the known results and research directions on approaches to the development and utilisation of LLMs, with a particular focus on their functional characteristics when integrated into decision support systems (DSSs) for agricultural monitoring. The subject of the research is approaches to the development and integration of LLMs into DSSs for agrotechnical monitoring. The main scientific and applied results of the article are as follows: the world experience of using LLMs to improve agricultural processes has been analysed; a critical analysis of the functional characteristics of LLMs has been carried out, and the areas of application of their architectures have been identified; the necessity of focusing on retrieval-augmented generation (RAG) as an approach to solving one of the main limitations of LLMs, which is the limited knowledge base of training data, has been established; the characteristics and prospects of using LLMs for DSSs in agriculture have been analysed to highlight trustworthiness, explainability and bias reduction as priority areas of research; the potential socio-economic effect from the implementation of LLMs and RAG in the agricultural sector is substantiated.

The proliferation of computer-oriented and information digitalisation technologies has become a hallmark across various sectors in today’s rapidly evolving environment. Among these, agriculture emerges as a pivotal sector in need of seamless incorporation of high-performance information technologies to address the pressing needs of national economies worldwide. The aim of the present article is to substantiate scientific and applied approaches to improving the efficiency of computer-oriented agrotechnical monitoring systems by developing an intelligent software component for predicting the probability of occurrence of corn diseases during the full cycle of its cultivation. The object of research is non-stationary processes of intelligent transformation and predictive analytics of soil and climatic data, which are factors of the occurrence and development of diseases in corn. The subject of the research is methods and explainable AI models of intelligent predictive analysis of measurement data on the soil and climatic condition of agricultural enterprises specialised in growing corn. The main scientific and practical effect of the research results is the development of IoT technologies for agrotechnical monitoring through the development of a computer-oriented model based on the ANFIS technique and the synthesis of structural and algorithmic provision for identifying and predicting the probability of occurrence of corn diseases during the full cycle of its cultivation.

Review of methods for energy-efficiency improvement in induction machines

The development and implementation of scientifically substantiated solutions for the improvement and modernization of electromechanical devices, systems, and complexes, including electric drives, is an urgent theoretical and applied task for energetics, industry, transport, and other key areas, both in global and national contexts. The aim of this paper is to identify a rational model of an induction motor that balances computational simplicity and control system performance based on predictive approaches while ensuring maximum energy efficiency and reference tracking during the operation in dynamic modes. Five main mathematical models of an induction machine with different levels of detail have been selected. Three predictive control models have been implemented using GRAMPC (v 2.2), Matlab MPC Toolbox (v 24.1), and fmincon (R2024a) (from Matlab Optimization Toolbox). It has been established that in the dynamic mode of operation, the equivalent induction motor circuit with parameters $R_{fe} =const$, $L_{\mu }=f\left(I_{1d}\right)$, and $T_F=f\left({\omega }_{Rm}\right)$ is the most appropriate in terms of the following criteria: accuracy of control action generation, computation speed, and calculation of energy consumption.

The dynamic growth in the dependence of numerous industrial sectors, businesses, and critical infrastructure on infocommunication technologies necessitates the enhancement of their resilience to cyberattacks and radio-frequency threats. This article addresses a relevant scientific and applied issue, which is to formulate prospective directions for improving the effectiveness of cybersecurity approaches for infocommunication networks through a comparative analysis and logical synthesis of the state-of-the-art of applied research on cyber threats to the information security of mobile and satellite networks, including those related to the rapid development of quantum computing technologies. The article presents results on the systematisation of cyberattacks at the physical, signalling and cryptographic levels, as well as threats to cryptographic protocols and authentication systems. Particular attention is given to the prospects for implementing post-quantum cryptography, hybrid cryptographic models and the integration of threat detection mechanisms based on machine learning and artificial intelligence algorithms. The article proposes a classification of current threats according to architectural levels, analyses typical protocol vulnerabilities in next-generation mobile networks and satellite communications, and identifies key research gaps in existing cybersecurity approaches. Based on a critical analysis of scientific and applied literature, this article identifies key areas for future research. These include developing lightweight cryptographic algorithms, standardising post-quantum cryptographic models, creating adaptive cybersecurity frameworks and optimising protection mechanisms for resource-constrained devices within information and digital networks.

This article is devoted to the development and testing of a computer model of an IoT system that combines wireless network technologies for the online monitoring of climatic and soil conditions in agriculture. The system supports decision-making by predicting the probability of crop diseases. This study focuses on the processes of aggregation, wireless transmission, and processing of soil and climatic measurement data within infocommunication software and hardware solutions. This research makes both scientific and practical contributions. Specifically, it presents a computer model based on wireless sensor networks and edge-computing technologies. This model aggregates and intelligently processes agricultural monitoring data to predict crop diseases. The software component, developed using an adaptive neuro-fuzzy inference system (ANFIS), was integrated into the microcontroller unit of IoT systems for agricultural applications. This approach enabled the substantiation of an optimised algorithmic and structural organisation of the IoT system, enabling its use in designing reliable architectures for agricultural monitoring systems in open fields with decision-making support.