Ulyanovsk State Technical University

A detailed study of promising flexible field emitters based on a nanocomposite polyvinyl alcohol with multiwalled carbon nanotubes was conducted. The properties of the emitter were measured in three configurations: a flat surface, a vertical edge, and a loop. Sample characterization in field emission projectors was accompanied by scanning electron, atomic-force, and Kelvin probe force microscopies as well as Raman spectroscopy. The current load distribution over the surface of the emitters is investigated and current-voltage characteristics are obtained at different levels of cathode high voltage training, including an assessment of temporal current stability as well as the “orthodox” test for compliance field emission data with the classical cold field emission regime. Sufficiently high levels of emission current above 3 mA have been obtained.

The problem of finding hidden patterns in nested samples of objects, the measurement of feature values was considered. The objects of each sample were divided into representatives of two non-intersecting classes. Using a recursive method, the feature values were divided into intervals within which representatives of one of the classes dominated. The results of the partitioning were used to calculate the stability of features. The presence of restrictions on the set of admissible stability values is explained through the properties of membership functions to fuzzy sets. The results of the computational experiment were used to predict the survival of patients with chronic lymphocytic leukemia, depending on the stages of their examination.

This study investigates the role of Technical and Vocational Education and Training (TVET) in fostering green skills for sustainable economic growth in Ghana. The study adopts a quantitative cross-sectional design involving 139 respondents drawn from TVET institutions, industry partners, and government agencies. Findings indicate that government policy support, trainer competence, institutional infrastructure, and industry collaboration significantly predict the integration of green skills (R² = 0.58) and collectively enhance employability and economic growth (R² = 0.49). Although policy awareness and institutional readiness are commendable, persistent barriers such as inadequate funding, gender disparities, and weak industry engagement continue to constrain systemic transformation. This study significantly contributes to the global Sustainable Development Goals (SDGs) 4 (Quality Education), 8 (Decent Work and Economic Growth), and 9 (Industry, Innovation, and Infrastructure). The study underscores the necessity of cross-sector partnerships and sustained capacity-building to ensure an inclusive green transition. It concludes that aligning TVET curricula with sustainable industry needs and strengthening trainer development are essential to achieving Ghana's green economy aspirations. The study recommends targeted investments, policy coherence, and international collaboration to institutionalize sustainability within TVET systems and promote gender equity across technical fields.

Background: The growth of digital patient feedback presents a new opportunity for healthcare quality monitoring. This study addresses the need to automatically classify the content of patient reviews to identify primary sources of dissatisfaction. Objective: The purpose of this study is to develop a machine learning algorithm for classifying negative patient reviews into two core categories: medical content (M—pertaining to diagnosis, treatment, and outcomes) and organizational support (O—pertaining to logistics, cost, and communication). We aim to identify which type of concern prevails and to analyze variations across cities, patient gender, and medical specialties. Methods: A database of 18,680 negative patient reviews (rated 1 star) was compiled from the Russian aggregator infodoctor.ru for the period from July 2012 to August 2023. A training set was created using an independent annotation procedure with three experts. A logistic regression model was trained to classify reviews into M and O categories, demonstrating an accuracy of 88.5%. Results: The analysis revealed a significant structural shift in Moscow, where since 2021, medical (M) complaints began to prevail over organizational (O) ones. This trend was not observed in St. Petersburg or other major Russian cities. Notably, in St. Petersburg, M-type reviews were more common within the most represented medical specialties, whereas O-type reviews consistently dominated in other cities. Gender differences were most pronounced in St. Petersburg, where women were more frequently authors of M reviews and men of O reviews. Conclusions: The developed algorithm provides a valuable tool for the automated monitoring of patient feedback. It enables healthcare managers to distinguish between clinical and service-related issues, facilitating targeted improvements in medical service quality and patient satisfaction.

Unmanned technologies are being actively introduced in modern agricultural enterprises to automate processes such as spraying, fertilizing, and transporting goods. However, managing multiple autonomous agents in a dynamically changing environment remains a complex task. Existing systems often operate according to rigid algorithms, which leads to resource conflicts, equipment downtime, and suboptimal use of working time. The purpose of this paper is to develop a multi-agent control system for agricultural machinery based on a three-level rule system adapted to the specifics of the agricultural environment. The proposed approach allows coordinating the actions of various types of agents, including sprayers, fertilizer spreaders, loaders, and fuelers, taking into account their current state and task priorities. The article presents a three-level rule architecture, including reactive, coordination, and strategic meta-rules, which provide flexible control in real time. A heat map of the field is used to visualize and analyze the system's operation, allowing the operator to intervene in critical situations. The effectiveness of the proposed method was tested in a simulation environment, where it demonstrated a reduction in field processing time and minimization of equipment downtime compared to traditional approaches.

This article introduces a hybrid approach for the classification of emotions in Russian dream diary texts. The research focuses on processing semi-structured data characterized by a high subjectivity in describing real-world entities. In this context, traditional approaches often show limited effectiveness. The article analyzes contemporary techniques for tonality and emotion analysis, highlighting their limitations when applied to the subjective nature of dream content. The proposed approach integrates lexical methods, machine learning algorithms, and transformer-based models to enhance performance. The result of the article is also an annotated dataset, which includes labels for six basic emotions, according to the Paul Ekman classification scheme. Experimental results show that the hybrid model achieves a precision of 22% in classifying two emotions simultaneously. It also achieves a 56% precision in classifying a single emotion. Overall, it outperforms comparable methods. We conducted an ablation study to validate the contribution of each component within the hybrid system, confirming their significance for overall performance.

The paper presents an approach to constructing a formal description of a subject area using automated analysis of technological documentation. The first stage of the proposed approach is extracting data (terms, objects of the subject area) from technical documentation on the subject area. Then, a natural language query is formed for a large language model (LLM), which helps to extract relations between the extracted terms. At the final stage, an OWL ontology is formed by postprocessing the obtained data. Evaluation experiments were conducted on 16 documents (ISO) on the topic of “Space industry”. Experiments were also conducted to compare algorithms for extracting terms and relations using LLM and the C-value algorithm. The proposed approach has proven its effectiveness on strictly formalized texts.

ABSTRACTIn this paper, we introduce a new family of tenth‐order hybrid Numerov‐type methods designed for solving second‐order initial value problems (IVPs) with enhanced accuracy and efficiency. Our proposed methods build on the foundation of existing high‐order Numerov‐type techniques and incorporate advanced strategies for step size adaptation and error control. We provide a comprehensive analysis of the theoretical underpinnings of these methods and validate their performance through extensive numerical experiments.