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  Застосування акумуляторних батарей в системах власних потреб на сучасних електричних станціях

  (Національний технічний університет "Харківський політехнічний інститут", 2014) Нікітін, Д. М.; Артюх, Станіслав Федорович

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  Аналіз енергозберігаючих рішень щодо теплопостачання медичного комплексу. Частина 1

  (Національний технічний університет "Харківський політехнічний інститут", 2014) Логачова, Д. О.; Лисенко, Людмила Іванівна

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  Експериментальне дослідження роботи автономної фотоелектричної системи

  (Національний технічний університет "Харківський політехнічний інститут", 2014) Миронова, А. В.; Махотіло, Костянтин Володимирович

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  A machine learning screening model for predicting the development of cervical dental lesions

  (Державна наукова установа "Центр інноваційних технологій охорони здоров'я" Державного управління справами, 2026) Zabolotna, Iryna I.; Bogdanova, Tatiana L.; Azarenkov, Volodymyr Illich; Genzytska, Olena S.; Komlev, Andrii A.

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  Introduction. Predicting the risk of development of cervical dental pathology is a difficult task due to the multifactorial nature of its etiopathogenesis and limited knowledge of risk factors. Aim. To develop and test a computer model for predicting the development of cervical dental lesions in young patients. Materials and methods. The survey consisted of 272 patients (mean age 24.3 ± 6.9 years), in whom risk factors for the development of a wedge- shaped defect, cervical caries and enamel erosion were determined, which became the input data for the computer model. The Extreme Gradient Boosting (XGBoost) tree-based machine learning method implemented in the Python programming language using the scikit- learn and XGBoost libraries was used. Synthetic Minority Over-sampling Technique (SMOTE) was additionally applied to increase the efficiency of predicting less common enamel erosion among the examined individuals. Results. When developing the models, the priority was given to recall over accuracy and specificity. This contributed to reducing the number of missed cases for each pathology. The highest discriminatory ability (ROC-AUC) = 0.84 (Receiver Operating Characteristic curve – Area under the curve) in combination with a high level of recall (recall = 0.82) corresponded to the model for predicting cervical caries of teeth. This confirmed the feasibility of using the XGBoost algorithm to identify complex relationships in nonlinear combinations of the indicators. The model for predicting a wedge- shaped defect of teeth also had high recall (recall = 0.83) but the moderate value of ROC-AUC (0.64) that emphasizes the presence of nonlinear dependent predictors. Particular scientific interest has the model for predicting erosion of tooth enamel which was created under conditions of a low prevalence of pathology among the examined. However, the results showed an acceptable level of recall (recall = 0.47) and moderate discriminatory ability (ROC-AUC = 0.72). This allowed us to determine that the problem of small sample was successfully solved. Conclusions. The presented machine learning screening model helps identify patients with increased risk of developing cervical dental lesions. Its use will make it possible to justify the prescription of preventive measures to young patients.

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  Transforming the cognitive conditions of online learning in higher education

  (Харківський національний педагогічний університет ім. Г. С. Сковороди, 2026) Tverdokhliebova, Natalia; Yevtushenko, Nataliia

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  The current stage of higher education development is characterized by a deep digital transformation, accompanied by the active implementation of online and distance learning. The availability of information, the increase in the number of digital stimuli and multitasking significantly change the nature of the cognitive activity of higher education students, which significantly affects the cognitive conditions of the educational process, in particular, on concentration of attention, cognitive load, information processing and the ability to self-regulate educational activity. The growth of information noise, multitasking, the constant presence of digital distractions, and the use of artificial intelligence tools are shaping a new cognitive reality of learning that requires scientific understanding. Therefore, the task of higher education is to reorient the educational process from the transmission of development of knowledge to the students' cognitive competencies, in particular the skills of conscious thinking, concentration, information hygiene, and self-regulation in the digital educational environment. This problem becomes particularly important in conditions of long-term distance learning, which increases the cognitive load and the risks of superficial educational material. assimilation of The purpose of the article is to study changes in the cognitive conditions of higher education students in online format in order to substantiate recommendations for organizing the educational process in conditions of increasing information load and digital transformation of education. The research methodology is based on a combination of systemic, interdisciplinary and cognitively oriented approaches, which made it possible to carry out a holistic analysis of changes in cognitive conditions of learning in higher education in the online learning environment. In the research process, general scientific (comparative analysis, systematization, classification, generalization) and empirical methods (search, questionnaire, quantitative and qualitative analysis) were used. The following results were achieved as part of the study: a theoretical analysis of modern scientific approaches to understanding the transformation of cognitive conditions of online learning in higher education was carried out; the results of a survey of bachelor's degree applicants at the National Technical University "Kharkiv Polytechnic Institute" were provided, the purpose of which was to determine the features of concentration of attention, the level of information overload, the prevalence of multitasking, and the specifics of the use of digital technologies, in particular artificial intelligence tools, in educational activities. Changes in cognitive requirements for the organization of the educational process were identified. Based on the generalization of the results obtained, practical recommendations were formulated for optimizing online learning, aimedat developing cognitive selfregulation skills, forming information hygiene, and supporting conscious learning in the digital educational environment. The conclusions substantiate that modern online learning creates contradictory cognitive conditions, combining the expansion of autonomy with increased cognitive risks, which highlights the need to revise approaches to organizing the educational process for the purposeful development of cognitive self-regulation and conscious thinking of higher education students.

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