Petrozavodsk State University

A retrospective analysis of data from 3,655 patients who received chemotherapy at a regional oncology dispensary revealed that the incidence of febrile neutropenia (FN) in 2024 was 0.9 %. Among patients with laboratory-confirmed leukopenia (n = 42), FN occurred in 38 %. Prophylactic use of granulocyte colony-stimulating factor (G-CSF) was documented in 45 % of cases within the high-risk group. The findings demonstrate a significant gap between clinical practice and international guidelines, emphasizing the need to implement standardized algorithms for risk assessment and G-CSF administration supported by clinical decision support systems (CDSS).

The article is a continuation of a series of publications by the authors on the topic of forecasting personnel needs taking into account the age and gender structure and the number of employees. The authors propose a new method for calculating forecast indicators of total and replacement personnel needs, based on the use of employment level profiles by one-year age categories. Employment level profiles are formed using indicators of the number of employed people by one-year age categories and gender factor, obtained from the results of processing and analysis of microdata from a sample survey of the labor force. The authors developed an algorithm for implementing a new method for calculating forecast indicators of general and replacement personnel needs; the proposed algorithm was tested using the example of the Republic of Karelia.

Training personnel for the development of artificial intelligence technologies is one of the strategic tasks facing Russian universities. This article aims to analyze the employment of graduates from Russian universities in educational programs related to artificial intelligence. The study tests the hypothesis about the influence of university status on the Alliance ranking in the field of AI and the presence of specialized educational programs in AI at the university on the effectiveness of graduate employment in these programs. The stud y is based on the monitoring of universities implementing educational programs in the field of artificial intelligence (n = 191). As part of the analysis of the employment of graduates with competencies in the field of artificial intelligence, the specifics of their distribution among employment channels were determined. The authors identified key universities in terms of the effectiveness of training AI personnel, the amount of wages, and the most popular positions in employment. Based on the comparison of the employment rates of university graduates, as well as alternative sources of AI personnel training (self-education and professional retraining) with the indicators of personnel needs in the field of artificial intelligence, a conclusion was made about the provision of this need by 43.9 %. The obtained data allow us to conclude that universities have successfully implemented the Russian Government’s order to increase the volume of training of AI specialists. The value of the article lies in the presentation of unique factual materials that, for the first time, describe the employment of university graduates in the field of AI. In particular, the article details the losses of human resources in the AI field on the way from the admission of applicants to universities to the direct employment of graduates. The target audience of the article is researchers, experts, analysts, employees, and managers of universities, as well as government representatives involved in the process of developing artificial intelligence in Russia.

In the conditions of sanctions, logging companies faced the problem of renewal and repair of the fleet of logging equipment. The paper presents the results of the study of harvester operation on the basis of crawler excavator SANY SY245F with harvester head WARATAH H480C in the conditions of JSC “KLPH” (Republic of Karelia) in winter and summer seasons. The analysis has shown that machines of this class can be recommended for use at the enterprises of the forestry complex of Russia under the conditions of sanctions preservation, taking into account the features of their design and operation.

This article investigates the use of short and full predicate adjectives in Russian spiritual verses. The study presents comparative analysis results from three collections of Russian spiritual poetry: V. Varentsov's Collection of Russian Spiritual Verses; “The Dove Book: Russian Folk Spiritual Verses of the Eleventh to Nineteenth Centuries”; and “Spiritual Verses of Northern Russia.” It examines the quantitative distribution between short and full predicative adjective forms as well as their stylistic distinctiveness. Drawing on grammatical criteria for distinguishing between predicative short and truncated adjective forms — namely, their syntactic function that determines an important feature such as case inflection — the authors explore morphological and syntactic factors influencing the predicativity of full adjectives: subject expression mode, position relative to the subject, presence or absence of a copula verb. A grammatical pattern is identified: when an adjective and noun are distant from each other, the degree of predicativity increases for full adjectives, whereas it decreases with close proximity. Special attention is given to describing unique syntactic constructions involving possessive relationships with full predicative adjectives, emphasizing the syntactic status of these adjectives within communicatively organized utterances.

This article attempts to answer the question of why the Russian economy has been able to withstand and adapt to the unprecedented sanctions imposed by Western countries, whose economies are many times larger than Russia’s. The authors hypothesize that this is due to the following factors: the nature of the Russian economy, the effectiveness of government and economic actors’ actions in these challenging circumstances, and the global economic system’s response to the sanctions imposed on Russia. To do this, the following parameters were analyzed: the volume of nominal GDP and the value of GDP by PPP, the structure of the national economy (the share of real production of goods in industry, construction, and agriculture), the size of the national debt and the debt burden in the form of payments on the national debt to GDP, the main export flows, and the state of the balance of payments in Russia and the G7 countries. On the basis of the numerous empirical material presented in the article (some of which was first introduced into scientific circulation in Russian publications), the analysis of the features of the Russian economy and its place in the world division of labor, the reaction of the world economic system to numerous illegal sanctions against Russia, the hypothesis expressed by the authors was confirmed.

The study aims to identify important milestones in the history of primary education in Karelia during the era of educational reforms of the late 18th – first third of the 19th century, using the example of the creative biographies of leading figures in public education in the Olonets Governorate. The article examines the history of primary schools in the Olonets Governorate during the Alexandrine era. The scientific novelty of the research lies in the fact that it for the first time describes and systematizes, based on archival materials and periodical press data, the main facts from the life and activities of Avraam Yegorovich Krylov, director of public schools and the Olonets Boys’ Gymnasium, who played an important role in the establishment of the system of main public schools and primary schools in Petrozavodsk and the Karelian province in the early 19th century. The study found that the development of the education system in the Olonets Governorate was closely linked with the reform processes of schools and universities during the time of Catherine II and Alexander I. Avraam Yegorovich Krylov entered the history of public education and enlightenment in Russia primarily as an enlightener and school organizer who did much for the spread of primary education in various districts (uyezds) of the Olonets Governorate. He was a director of public schools and opened 21 rural parochial schools.

The article is devoted to the study of the importance of seasonal work of the inhabitants of the White Sea Karelia, the search for ways of survival by the population of the region in difficult natural and climatic conditions. Marine fishing in Murmansk and the White Sea coast was of primary importance among the latrines for the population of Karelian volosts adjacent to Seaboards. Haulage served as another type of seasonal work, being characteristic of the population of the northern, western and southern volosts of the White Sea Karelia. The population of the western volosts, primarily Ukhta and Voknavolotsky, was more focused on peddling trade in Finland. At the same time, the occupation of haulage by the population of these regions, in our opinion, can be considered a mechanism of peddling trade. The study was carried out on the basis of domestic and foreign sources and research literature, which allow us to conclude about the predominant importance of latrines in the economy of the region. Agriculture was of secondary importance, being only an additional source of food for Karelian families.

Identifying patients at high risk for fatal cardiovascular disease (CVD) complications is a critical task in reducing preventable CVD morbidity and mortality. Various risk assessment algorithms and scores are widely used for this purpose, but their limitations include a limited set of predictors and low accuracy. Machine learning methods offer the potential to address these shortcomings and personalize cardiovascular risk assessment.Objective: To compare the accuracy of the SCORE scale and machine learning models in predicting fatal cardiovascular complications.Materials and Methods: A multicenter retrospective study was conducted (1999–2018), including 3,891 treatment cases of 1,064 patients aged 40–69 years in the Russian Federation. Logistic regression, ensemble machine learning (ML) methods, and Multi-Layer Perceptron were used for forecasting. Comparison with SCORE was performed on an independent validation set consisting of 440 records.Results: The CatBoost ML model demonstrated the best accuracy (AUROC 0.879; sensitivity 0.938; specificity 0.777). During validation, CatBoost demonstrated comparable discrimination to SCORE but outperformed the scale in specificity (0.653 vs. 0.408) and accuracy (0.673 vs. 0.45) when referencing patients to lowand intermediate-risk groups. Key predictors for the model were gender, age, smoking, systolic blood pressure, body mass index, heart rate, and lipid profile.Conclusion: The machine learning model outperformed the SCORE scale in predicting fatal cardiovascular events. The use of machine learning in predicting cardiovascular risk can improve the effectiveness of CVD prevention and facilitate personalized patient care.

A sample of a Mesoarchean banded iron formation (BIF) from the Shurlovaara Formation was studied. It consists of three types of layers: an iron-enriched (magnetite quartzite) type, a silica-enriched type (quartzites), and a type consisting of chlorite-amphibole schist. The BIF displays a sedimentary cyclicity: a cycle begins with schists, which are followed by magnetite quartzites, and then by quartzites. The mineral and chemical composition of minibeds of each type has its own distinctive features. The quartzites consist of quartz with accessory magnetite, chlorite, carbonate, pyrite, apatite and amphibole impurities. They are depleted in all minor and trace elements, except for Ba, Rb and As. Rare-earth elements (REEs) are characterized by depletion in light REEs (LREEs) and the presence of a poorly defined positive Eu-anomaly. The magnetite quartzites consist mainly of magnetite, quartz and chlorite with amphibole, apatite and pyrite impurities. The rocks of these minibeds are a major concentrator of V and Ga. They are considerably depleted in LREEs and clearly show a positive Eu-anomaly. The chlorite-amphibole schists are made up of quartz, chlorite and amphibole; pyrite, magnetite, titanomagnetite, apatite, and zircon are present as secondary and accessory elements. The schists are richer in practically all minor and trace elements than other rocks of the minibeds. REEs in the schists are enriched in heavy REEs, and their positive Eu-anomaly is the most defined. The chlorite-amphibole schists are modelled as a mixture of the destruction products of Kontokki komatiite-basaltic and felsic volcanics with addition of iron-silica substrate.