bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of a neural network with a layer of trainable activation functions for the second stage of the ensemble classifier with stacking
Authors: Galchonkov Oleg , Baranov Oleksii , Antoshchuk Svetlana , Maslov Oleh , Babych Mykola 
Published: November 2024

One of the promising directions for improving the quality of object recognition in images and parallelizing calculations is the use of ensemble classifiers with stacking. A neural network at the second level makes it possible to achieve the resulting quality of classification, which is significantly higher than each of the networks of the first level separately. The classification quality of the entire ensemble classifier with stacking depends on the efficiency of the neural networks at the first stage, their number, and the quality of the classification of the neural network of the second stage. This paper proposes a neural network architecture for the second stage of the ensemble classifier, which combines the approximating properties of traditional neurons and learning activation functions. Gaussian Radial Basis Functions (RBFs) were chosen to implement the learned activation functions, which are summed with the learned weights. The experimental studies showed that when working with the CIFAR-10 data set, the best results are obtained when six RBFs are used. A comparison with the use of multilayer perceptron (MLP) in the second stage showed a reduction in classification errors by 0.45–1.9 % depending on the number of neural networks in the first stage. At the same time, the proposed neural network architecture for the second degree had 1.69–3.7 times less learning coefficients than MLP. This result is explained by the fact that the use of an output layer with ordinary neurons allowed us not to enter into the architecture many learning activation functions for each output signal of the first stage, but to limit ourselves to only one. Since the results were obtained on the CIFAR-10 universal data set, a similar effect could be obtained on a large number of similar practical data sets.
Development of technology for fermented milk product “Gatyg” based on buckwheat varieties grown in Azerbaijan
Authors: Akhundova Nazilya , Babashli Aynur , Gadimova Natavan 
Published: November 2024

The object of the study is the production of the national fermented milk product “Gatyg” based on sprouts of buckwheat varieties grown in Azerbaijan. Buckwheat is one of the most important grain food crops, contains protein of high nutritional value and a significant amount of dietary fiber, vitamins B1, B2 and E, as well as minerals. Rutin and quercetin are the main antioxidants in buckwheat. Due to the high rutin content, buckwheat is used to maintain the functional capabilities of the vascular system. The biological value of buckwheat proteins is more than 90 %.
A recipe and technology for the fermented milk product “Gatyg” based on buckwheat varieties grown in Azerbaijan were developed. The conducted studies allowed determining the best parameters of BAS extraction to achieve extractive substances in the extracts up to 20–25 %. The yield of flavonoids is 93 %.
The content of BAS and flavonoids during the germination of buckwheat seeds, as well as the conditions for their extraction from sprouts of buckwheat grown in Azerbaijan, were studied. The quality indicators of the obtained product, enriched with sprouted buckwheat seeds, are distinguished by high taste and aromatic characteristics. It was found that it is advisable to include 7 % buckwheat sprouts in the composition of a product for therapeutic and prophylactic nutrition.
Development of a method for analyzing and forecasting the state of multidimensional objects using a metaheuristic algorithm
Authors: Tarkhan Aqeel Bahr , Sova Oleg , Lebedynskyi Andrii , Dehtiar Yurii , Lytvynenko Oleksandr , Minochkin Dmytro , Petrukovych Dmytro , Pimonov Ihor , Kosolapov Viktor , Honcharuk Dmytro 
Published: November 2024

The object of the study is multidimensional objects. The problem solved in the study is to increase the efficiency of assessing the state of multidimensional objects, regardless of the number of dimensions of object state assessment. The subject of the study is the process of assessing the state of multidimensional objects using an advanced butterfly optimization algorithm (BOA), an advanced genetic algorithm and evolving artificial neural networks.
The originality of the study is as follows:
– the initial setting of butterfly agents (BA) on the plane of multidimensional objects is carried out taking into account the type of uncertainty using appropriate correction factors for the degree of awareness of nectar source locations (in our case, priority search directions);
– adjusting the initial BA velocity allows determining search priority;
– the fitness of BA nectar collection sites is determined, which reduces the time for assessing the state of multidimensional objects;
– the possibility of global restart of the algorithm, which allows the algorithm to go beyond the current optimum and improve the exploration ability, which reduces the time for assessing the state of multidimensional objects;
– the possibility of clarification at the stage of collecting nectar clusters due to ranking nectar sources by the level of stimulus intensity;
– improved ability to select the best BA in comparison with traditional selection using an advanced genetic algorithm.
The proposed method should be used to solve the problems of assessing the state of multidimensional objects under uncertainty and risks characterized by a high degree of complexity. The method showed a 14–16 % increase in the efficiency of assessing the state of multidimensional objects
Development of a method for protecting information resources in a corporate network by segmenting traffic
Authors: Tolkachov Maksym , Dzheniuk Nataliia , Yevseiev Serhii , Lysetskyi Yurii , Shulha Volodymyr , Grod Ivan , Faraon Serhii , Ivanchenko Ihor , Pasko Igor , Balagura Dmytro 
Published: November 2024

The object of the study is a corporate network with a dynamic structure and centralized management. The subject of the research is the processes of ensuring the protection of information resources in the corporate network. The goal is to develop a method of protecting information in the corporate network. The development is based on the Zero Trust Security strategy, according to which access to the network is allowed only after verification and identification of information. The task is to develop an effective method of protecting information resources and managing cyber security in the corporate network, taking into account the complex aspects of malicious influence. The following results were obtained. It is shown that the complex, diverse presentation of information in the network requires a comprehensive approach with the division of mixed content of information into segments according to the target orientation. Based on CISA's (Cybersecurity and Infrastructure Security Agency) Zero Trust Maturity Model, a method of targeted traffic segmentation is proposed. It allows detailed analysis of the interaction between applications, users and corporate network infrastructure, which increases the level of complex threats detection by 15 %. A method of protecting information resources of a socio-cyber-physical system is proposed, which, based on the principle of the Zero Trust Security strategy, improves the monitoring and management of cyber security of information resources by taking into account social aspects. This allows to detect and respond to threats in real time and adapt security policies according to the dynamics of user behavior and general security conditions. Integrating analytical methods and modern technologies into a security strategy creates a foundation for adaptive and resilient cyber defense.
Devising a method for detecting an aerial object by radar with an additional channel of passive reception
Authors: Khudov Hennadii , Trofymov Ivan , Repilo Iurii , Makoveichuk Oleksandr , Tkachenko Viktor , Kotov Denys , Gridina Valya , Herda Maksym , Kryvosheiev Vitalii , Shvets Mikola 
Published: November 2024

The object of this paper is the process of detecting an aerial object by a radar with active and passive reception channels. The main hypothesis of the study assumed that the introduction of an additional channel of passive reception would increase the conditional probability of correct detection at a fixed value of the conditional probability of false alarms.
When detecting an aerial object, it was considered that the signals from aerial object represent a reflected signal after being emitted by the active radar channel elemitted own radio signals. A radar with active and passive reception channels assumes the presence of two channels. The active location channel provides reception of signals reflected from an aerial object, their processing and detection according to the Neumann-Pearson criterion. The passive location channel functions according to the principle of panoramic spectral analysis based on windowed Fourier transforms. The information combining device is intended for the compatible combining of information from active and passive channels of radar reception. At the output of the passive location channel, an output signal is formed, and the coordinates of the aerial object are measured. To ensure the functioning of the radar with active and passive reception channels, it is necessary to ensure the time synchronization of the reception channels.
The quality of detection of aerial objects by radar with active and passive signal reception channels was evaluated. The quality indicator defines the conditional probability of correct detection. The dependences of the conditional probability of correct detection are given for a radar with only an active reception channel and a radar with active and passive reception channels. It was established that the introduction of an additional channel of passive reception to the radar increases the conditional probability of correct detection by an average of (20–40) %, depending on the signal-to-noise ratio.
Identifying patterns in the structural-phase transformations when processing oxide doped waste with the use of carbon reducer
Authors: Poliakov Anatolii , Volokh Vadym , Andreev Andrey , Rebenko Victor , Kurlov Viacheslav , Yamshinskij Mykhail , Lukianenko Ivan , Ivanchenko Dmytro , Zhuravel Dmytro , Kovalenko Iryna 
Published: November 2024

The object of this study is the structural and phase transformations during the carbon reduction of tungsten high-speed steel slag in order to obtain a resource-saving alloying additive. The problem is the loss of precious elements when obtaining and using alloying material from man-made raw materials. Solving the problem is related to the definition of technological parameters to enable the reduction of losses of the corresponding elements. As a result of increasing the degree of scale reduction from 33 % to 72 % and 85 %, the strengthening of the manifestation of the solid solution of carbon and alloying elements in α-Fe relative to FeWO4, FeO and Fe3O4 was revealed. Fe3C, WC, W2C, FeW3C, Fe3W3C, Fe6W6C, VC, V2C, Cr3C2, Cr7C3 and Cr23C6 also appeared. Along with this, rounded and multifaceted particles with different chemical composition and the formation of a spongy microstructure were found. It was established that the most acceptable degree of recovery is 85 %. But achieving a recovery rate of 72 % is also sufficient. This is explained by the fact that the residual carbon in the carbides provides an increased reducing capacity, which is realized during the further reduction of oxides in the liquid metal during alloying. The spongy microstructure results in faster dissolution in contrast to standard ferroalloys, which provides a reduction in melting time while reducing spent resources. No phases with an increased tendency to sublimation were found in the obtained alloying material. That is, no additional conditions are needed that restrain the loss of alloying elements during evaporation with a gaseous phase, which provides an increase in the degree of extraction of the corresponding elements. The properties of the resulting alloying material make it possible to use it in metallurgical production when smelting alloyed steel grades in an electric arc furnace, the composition of which does not have strict restrictions on the carbon content
Design of an intelligent data analysis platform for pharmaceutical forecasts
Authors: Sokolovska Zoia , Ivchenko Iryna , Ivchenko Oleg 
Published: November 2024

This study considers the task to design a data analysis platform with predictive capabilities of neural networks. The object of research is intelligent decision-making systems built on deep learning methods. The proposed intelligent platform takes into account the specificity of working with data in the dynamic and uncertain environment of the pharmaceutical market. Its main purpose is the processing of various data formats, such as time series, regression, classification data sets to create forecasts based on various indicators. At the core of the platform architecture, along with technologies for backend and frontend development (HTML, JS, CSS, C#, .NET), MSSQL Server and TSQL for database management, are AI Microservices (Python, Flask); they are responsible for artificial intelligence services, in particular neural networks.
In order to identify the optimal model, which is able to effectively solve regression problems based on the selected indicators, the study analyzed several configurations of neural networks on End-to-end machine learning platforms. Distinctive features of the architecture of the designed data analysis platform include its ability to dynamically switch between different machine learning models based on predefined indicators and criteria such as prediction accuracy and model selection.
Improved interpretation of forecasts through the use of Explainable AI enables effective decision-making in the pharmaceutical industry. The functioning of the proposed instrumental decision-making base is demonstrated on the examples of forecasting trends in the consumption of pharmaceuticals by different groups in the pharmaceutical markets of different countries. Automating the model selection and prediction loss minimization process in a comprehensive data analysis platform (CDAP) improves forecast accuracy by approximately 15 % compared to traditional manually selected models.
Revealing the effect of hydrothermal processing of legumes on the accumulation of dry matter in aquafaba
Authors: Hrynchenko Olha , Dehtiar Valentyna , Radchenko Anna , Pak Andrey , Smetanska Iryna , Percevoy Fedor 
Published: November 2024

Legumes are an affordable source of vegetable protein, fiber, and vitamins, and due to their high nutritional and biological value, they are widely used in the nutrition of various categories of consumers. The main methods of processing legumes include hydromechanical and hydrothermal treatment, which is aimed at achieving culinary readiness of boiled legumes. Studying the parameters of the specified technological process for the accumulation of dry substances in aquafaba could make it possible to influence the composition and properties of this liquid, which would provide opportunities for its use in the food industry.
The influence of hydrothermal processing of leguminous grains on the accumulation of dry matter in aquafaba, as well as the kinetics of moisture content of various types of leguminous grains, have been experimentally investigated, which made it possible to determine the rational parameters of hydromechanical and hydrothermal processing of chickpeas, beans, peas, lentils, with the preparation of boiled leguminous grains and aquafaba.
It was determined that during hydromechanical processing intensive moisture absorption occurs in the first hours and reaches up to 80 % of the initial mass, after which the intensity of water absorption decreases somewhat. Rational technological parameters of hydrothermal processing of leguminous grains by two methods, basic (cooking at a temperature of 99±1 °С) and under pressure (cooking in a pressure cooker at a temperature of 120±1 °С) were established in the relationship "achieving the readiness of legumes – the content of dry substances in aquafaba". The use of the above-mentioned techniques for hydrothermal processing of leguminous grains make it possible to obtain the content of dry substances in aquafaba in the range of 2.8–4.8 % for cooking by the main technique and 3.9–7.0 % under pressure cooking conditions. To understand the processes regarding the influence of hydrothermal processing of legume grains on the accumulation of dry substances, the Peleg model was used.
The experimental data could be used to substantiate parameters for the technological process to produce foods based on legumes
Improving information theory of context-aware phrase embeddings in HR domain
Authors: Bocharova Maiia , Malakhov Eugene 
Published: November 2024

The object of the study is context-aware phrase representations. The growing need to automate candidate recruitment and job recommendation processes has paved the way for the utilization of text embeddings. These embeddings involve translating the semantic essence of text into a continuous, high-dimensional vector space. By learning context-aware rich and meaningful representations of phrases within the human resource domain, the efficacy of similarity searches and matching procedures is enhanced, which contributes to a more streamlined and effective recruitment process. However, existing approaches do not take into account the context when modeling phrases. This necessitates the improvement of information technology analysis in this area. In this paper, it is proposed to mark the beginning and end of phrases in the text using special tokens. This made it possible to reduce the requirements for computing power by calculating all phrase representations present in the text simultaneously. The effectiveness of the improvement was tested on a new dataset to compare and evaluate the models in the task of modeling phrases in the field of human resources management. The proposed approach to modeling phrase representations with regard to context in the field of human resources management leads to an improvement in computational efficiency by up to 50 % and an increase in accuracy by up to 10 %. The architecture of the machine learning model for creating context-aware phrase representations is developed, which is characterized by the presence of blocks for taking into account phrase boundaries. Experiments and comparisons with existing approaches have confirmed the effectiveness of the proposed solution. In practice, the proposed information analysis technology can be used to automate the process of identifying and normalizing candidates' skills in online recruiting
Improving a technique for producing a polycomponent semi-finished product with high degree of readiness for 3-D printing of functional food
Authors: Zahorulko Andrii , Voronenko Iryna , Bozhydai Iryna , Pogarskiy Aleksey , Ibaiev Eldar , Ponomarenko Nataliia , Bereza Olena , Khytko Oleksandr 
Published: November 2024

The object of this study is the process of producing polycomponent semi-finished products of a high degree of readiness using a model structure of functional equipment, in particular, based on apple, Jerusalem artichoke, table beet, and sea buckthorn.
A feature of the improved technique is the implementation of preliminary heat treatment with hot steam of raw materials: apple – 1.0...2.0 min, Jerusalem artichoke – 3...6 min, and table beet – 6...12 min, respectively. Sea buckthorn was not processed, only technological rinsing was used. Grinding of raw materials was carried out on a double grinding machine (apple, Jerusalem artichoke, and table beet – 0.2...0.5 10-3 m, and sea buckthorn – 0.5...1 mm). The combined production of polycomponent semi-finished products of a high degree of readiness (pastes/fractional powder) was implemented using a model structure of functional equipment.
The boiling process in a rotary-film evaporator was carried out at a temperature of 55 °C for 65 seconds to a dry matter content of 25 %. Drying of the cooked multicomponent mass was carried out in a single-drum cylindrical IR dryer at a temperature of 50 °C to a final moisture content of 3...6 % and fractional grinding (0.3...0.6 mm).
The rheological properties of the paste-like multicomponent semi-finished product were determined by the change in dynamic viscosity for composition 2, which is 485 Pa∙s, and in the control sample of applesauce, this indicator is 50 Pa∙s. When boiling in a rotary-film evaporator at a temperature of 55 °C to a content of 30 % of dry substances, there is an increase in the strength of dynamic viscosity by 3.2 times (600 Pa∙s), and in the control (apple paste) this indicator is 178 Pa∙s with a dry matter content of 25 %
Harvesting Optimization, Biomass, and Lipid Content Analysis of Euglena sp. Culture with Ettlia texensis Bioflocculant and Commercial Chitosan
Authors: Agus Suyono Eko , Mudrikah Siti , Karilanata Khalid Erlangga , Kurnianto Dedy , Budiman Arief , Dewayanto Nugroho , Putri Renata Adaranyssa Egistha , Sadewo Brilian Ryan , Erfianti Tia 
Published: November 2024

Graphical Abstract

Highlight Research

Bioflocculation technique can improve the harvesting effectiveness of semimass culture of Euglena
The addition of E. texensis can significantly increase the flocculation efficiency of Euglena
The addition of commercial chitosan was able to increase the flocculation efficiency of Euglena sp.
The biomass and lipid content produced by Euglena with E. texensis flocculant agent showed higher results than the biomass and lipid content produced by Euglena sp. with commercial chitosan flocculant agent.

Abstract
Euglena sp. has a high potential to be developed as biofuel. However, the high cost and energy required for the harvesting process are hindering the production. Flocculation using natural substances, such as microorganisms and biopolymers, offers a promising solution to minimize energy and production costs, so it is applicable on a mass scale. Ettlia texensis is one of the autoflocculating microalgae that can excrete extracellular polymeric substances (EPS). Chitosan is a linear copolymer of D-glucosamine and N-acetyl-D-glucosamine produced by the deacetylation of chitin, which is usually exploited by marine crustaceans, shrimp, and crabs. Chitosan has a very high cation load, so it is often used for coagulation or flocculation. This study explores the potential of E. texensis and chitosan as flocculant agents to harvest the mass culture of Euglena sp. by giving different doses E. texensis with 1:0.25 (E3), 1:0.5 (E4), 1:1 (E5), and 1:2 (E6), and chitosan with 1.25 mg (C1), 2.5 mg (C2), 3.75 mg (C3), and 5 mg (C4). This research began with the cultivation of Euglena sp. and E. texensis on a 50 L scale for 12 days. The effectiveness of flocculation was measured by the spectrophotometric method. Based on this research, the best treatment for harvesting Euglena sp. culture by bioflocculation was shown by the addition of chitosan (5 mg) with the recovery of 84.83%, 0.2213 mg/mL biomass, and 0.2117 mg/mL lipid content. Meanwhile, with E. texensis, the best was shown by the ratio of 1:2 with recovery 84.71%, 0.2053 mg/mL biomass, and 0.1753 mg/mL