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Control of a certain object can be implemented using different principles, namely, a certain software-implemented algorithm, fuzzy logic, neural networks, etc. In recent years, the use of neural networks for applications in control systems has become increasingly popular. However, their implementation in embedded systems requires taking into account their limitations in performance, memory, etc. In this article, a neuro-controller for the embedded control system is proposed, which enables the processing of input technological data. A structure for the neuro-controller is proposed, which is based on the modular principle. It ensures rapid improvement of the system during its development. The neuro-controller functioning algorithm and data processing model based on artificial neural networks are developed. The neuro-controller hardware is developed based on the STM32 microcontroller, sensors and actuators, which ensures a low cost of implementation. The artificial neural network is implemented in the form of a software module, which allows us to change the neuro-controller function quickly. As a usage example, we considered STM32-based implementation of the control system for an intelligent mini-greenhouse.

In the early software development stages, the aim of estimation is to obtain a rough understanding of the timeline and resources required to implement a potential project. The current study is devoted to a method of preliminary estimation applicable at the beginning of the software development life cycle when the level of uncertainty is high. The authors’ concepts of the estimation life cycle, the estimable items breakdown structure, and a system of working-time balance equations in conjunction with an agile-fashioned sizing approach are used. To minimize the experts’ working time spent on preliminary estimation, the authors applied a decision support procedure based on integer programming and the analytic hierarchy process. The method’s outcomes are not definitive enough to make commitments; instead, they are supposed to be used for communication with project stakeholders or as inputs for the subsequent estimation stages. For practical usage of the preliminary estimation method, a semistructured business process is proposed.

The precise categorization of brief texts holds significant importance in various applications within the ever-changing realm of artificial intelligence (AI) and natural language processing (NLP). Short texts are everywhere in the digital world, from social media updates to customer reviews and feedback. Nevertheless, short texts’ limited length and context pose unique challenges for accurate classification. This research article delves into the influence of data sorting methods on the quality of manual labeling in hierarchical classification, with a particular focus on short texts. The study is set against the backdrop of the increasing reliance on manual labeling in AI and NLP, highlighting its significance in the accuracy of hierarchical text classification. Methodologically, the study integrates AI, notably zero-shot learning, with human annotation processes to examine the efficacy of various data-sorting strategies. The results demonstrate how different sorting approaches impact the accuracy and consistency of manual labeling, a critical aspect of creating high-quality datasets for NLP applications. The study’s findings reveal a significant time efficiency improvement in terms of labeling, where ordered manual labeling required 760 min per 1000 samples, compared to 800 min for traditional manual labeling, illustrating the practical benefits of optimized data sorting strategies. Comparatively, ordered manual labeling achieved the highest mean accuracy rates across all hierarchical levels, with figures reaching up to 99% for segments, 95% for families, 92% for classes, and 90% for bricks, underscoring the efficiency of structured data sorting. It offers valuable insights and practical guidelines for improving labeling quality in hierarchical classification tasks, thereby advancing the precision of text analysis in AI-driven research. This abstract encapsulates the article’s background, methods, results, and conclusions, providing a comprehensive yet succinct study overview.

The emergence of high repetition-rate X-ray free-electron lasers (XFELs) powered by superconducting accelerator technology enables the measurement of significantly more experimental data per day than was previously possible. The European XFEL is expected to provide 27,000 pulses per second, over two orders of magnitude more than any other XFEL. The increased pulse rate is a key enabling factor for single-particle X-ray diffractive imaging, which relies on averaging the weak diffraction signal from single biological particles. Taking full advantage of this new capability requires that all experimental steps, from sample preparation and delivery to the acquisition of diffraction patterns, are compatible with the increased pulse repetition rate. Here, we show that single-particle imaging can be performed using X-ray pulses at megahertz repetition rates. The results obtained pave the way towards exploiting high repetition-rate X-ray free-electron lasers for single-particle imaging at their full repetition rate.

Nowadays development of the Arctic shelf and the continental slope resources is one of the most promising areas in the world oil and gas industry growth. Russia, unlike other countries with Arctic hydrocarbon reserves, implements only a few projects on the Northern shelf due to the difficult natural and climatic conditions and ecological vulnerability of the territory. The purpose of the article is to substantiate the feasibility of hydrocarbon resources development on the Russian Federation Arctic shelf in the long term. The article analyzes the external and internal factors affecting the decline in the rate of oil and gas exploration and production in this region in recent years. It considers state support measures reflected in the Strategy for the Russian Arctic Zone Development and Ensuring National Security – 2035, and a number of other laws and regulations developed and adopted after 2014. Special attention is given to the development of high-tech imports phase-out; specialists training; science, education and business integration; reducing the tax burden; environmental issues. The tasks facing state authorities, oil and gas companies, scientific and educational organizations have been identified that will lead to the successful Arctic shelf reserves development.

Мета - визначити оптимізовану систему передопераційної підготовки пацієнток з артеріальною (АГ) та легеневою гіпертензію (ЛГ). Матеріали та методи. Обстежено 35 пацієнток. Підготовлено до оперативного лікування 25 (71,4%) пацієнток з АГ, із них 19 (54,3%) жінкам проведено планові операції, а 6 (17,1%) - оперативне втручання в ургентному порядку. Також підготовлено до операції 10 (28,6%) пацієнток із ЛГ, оперативне втручання яким виконано в плановому порядку. Пацієнти, яким виконано планові операції, обстежені згідно зі стандартами медичної установи, враховуючи протоколи Європейської асоціації кардіологів (ESC) від 2022 р. Результати. Встановлено, що АГ I стадії діагностовано в 15 (42,9%) пацієнток, а II стадії - у 10 (28,6%) жінок; ЛГ I стадії - у 7 (20%) хворих, а II стадії - у 3 (8,6%) жінок. При підготовці пацієнтів до оперативного втручання ретельно підібрано медикаментозну терапію АГ, яка впливала на різні ланки патогенезу захворювання. У пацієнтів з ЛГ, яким заплановано оперативне лікування, медикаментозну терапію в багатьох випадках чітко регламентовано та призначено відповідно до клінічних проявів захворювання і перебігу периопераційного періоду. У пацієнтів, яких прооперовано в плановому порядку з дотриманням розробленої схеми передопераційної підготовки, не виявлено ускладнень із боку серцевої-судинної системи. Показник артеріального тиску в більшості випадків був у межах вікової норми, відсутні випадки венозної тромбоемболії, а тяжкість серцевої недостатності не посилювалася. Висновки. Рекомендовано чітке дотримання схеми доопераційної підготовки, починаючи з догоспітального етапу обстеження та лікування пацієнтів з АГ та ЛГ. Визначення та дотримання оптимізованої схеми передопераційної підготовки показало свою ефективність, сприяло відсутності ускладнень із боку серцево-судинної системи, стабілізації артеріального тиску в периопераційному періоді, характеризувалося відсутністю випадків венозної тромбоeмболії, а тяжкість серцевої недостатності не посилювалася. Дослідження виконано відповідно до принципів Гельсінської декларації. Протокол дослідження ухвалено Локальним етичним комітетом зазначеної в роботі установи. На проведення досліджень отримано інформовану згоду пацієнток. Автори заявляють про відсутність конфлікту інтересів.

The electronic structure and translucent nature of lithium tetraborate (Li2B4O7) render it promising as a scintillator medium for neutron detection applications. The inherently large neutron capture cross-section due to 10B and 6Li isotopes and the ease with which Li2B4O7 can be enriched with these isotopes, combined with the facile inclusion of rare earth dopants (occupying the Li+ sites), are expected to improve the luminescent properties, as well as the neutron detection efficiency, of Li2B4O7. The electronic structure of both doped and undoped Li2B4O7 were explored, using photoemission and inverse photoemission spectroscopies, optical measurements, and theoretical computational studies such as density functional theory. The scintillation properties are further enhanced because of the wide bandgap, making Li2B4O7 extremely translucent, so that capturing the neutron scintillation output is neither hindered nor diminished. Therefore, in this review, demonstrations of the possible amplification of neutron capture efficiencies, courtesy of rare-earth dopants, along with insights into a significantly large charge production (associated with neutron capture), are presented.