The object of the research is meat raw materials obtained from animal slaughter. The research is aimed at addressing the problem of the efficient utilization of meat raw materials under conditions of their natural variability and production uncertainty, which limits the ability to make timely and well-founded technological decisions. The research developed a mathematical model for planning the production of a diverse assortment of meat products, taking into account regional characteristics of raw materials and the production and technological potential of enterprises. The use of the model enables the formation of the consumer properties of meat products, including grade, taste, nutritional and biological value, through the rational utilization of raw materials of different origins, considering the species, breed, age, sex, and fatness of animals. The article shows that product quality should be evaluated using a comprehensive criterion integrating physicochemical, functional-technological, and structural-mechanical indicators. Particular importance is given to rheological characteristics correlating with properties of raw materials, minced systems, and finished products. Within the research, an objective function was developed that accounts for availability and quality of raw materials for different products. Sets of enterprises, assortments, animal characteristics, and supply zones were formalized considering environmental conditions of origin areas. Production constraints were described using a compatibility matrix, emphasizing water-binding processes and preservation of intramuscular fat as key quality factors. Production variants were also formalized, considering technological constraints under conditions of production uncertainty. The practical application of the developed model provides digital support for production management, optimizes raw material allocation, and enables product yield prediction. It also reduces production losses and meets the current requirements for the digital transformation of meat processing enterprises.
The object of research is the recovery of moringa powder in the production of instant functional drinks. Despite the existence of researches on the use of moringa powder in functional products, the relationship between the structural and morphological characteristics of powder particles and the kinetics of its recovery requires in-depth research, which is important for substantiating the technological parameters of the production of instant functional drinks. The microstructure of moringa powder was studied using scanning electron microscopy, and the elemental composition was determined using energy-dispersive X-ray spectroscopy. The powder had a pronounced polydispersity with a predominance of stable cell conglomerates of irregular shape with a size of 5 to 130 μm. The presence of a developed rough surface and regular porous channels (from 5 to 15 μm) provides an increase in the specific area of contact with the liquid. A high protein potential was revealed due to the significant N content (9170 mg/100 g). It was found that consuming 100 g of a drink based on moringa powder provides 17.9% of the daily requirement of K without the risk of excess salt intake (Na covers only 3.3–3.8% of the requirement). Moringa powder is an excellent source of Mg (970 mg/100 g). This confirms its functional ability to contribute to the normalization of the nervous system. In addition, the functional properties of moringa powder and its positive effect on energy metabolism are provided by the high content of P (1100 mg/100 g). Separately, a significant content of Cr (0.36 mg/100 g) was noted, which is an active regulator of carbohydrate metabolism and blood sugar levels. The relationship between the microstructure, mineral composition and hydration behavior of moringa powder has been proven, confirming its use as an ingredient in powdered functional food systems.
The object of research is the corrosion of St20 steel in aqueous electrolytes of different salt compositions under aerated and deoxygenated conditions. The problem addressed is the development and comparative evaluation of corrosion rate prediction models based on a limited experimental dataset, with the possibility of interpreting the influence of individual factors. The effects of exposure time, NaCl, Na2SO4, and Na2CO3 concentrations, and aeration and deoxygenation conditions on the corrosion rate were investigated experimentally. Three models were considered: a physics-informed LSTM model, an LSTM model with an attention mechanism, and a Random Forest model. The training dataset was formed by pseudo-temporal expansion of the experimental data using a sliding window. This approach transforms discrete measurements into sequences for model training without replacing experimental corrosion rate determination with a purely computational procedure. The models generally reproduce the experimental dependencies within the dataset. The R2 values for the physics-informed LSTM model, the LSTM model with the attention mechanism, and the Random Forest model were 0.9926, 0.9925, and approximately 1. The physics-informed LSTM model accounts for differences between aerated and deoxygenated conditions, which are important for describing corrosion processes in aqueous media. The attention mechanism provides model interpretability by estimating the contribution of individual factors to the prediction. The Random Forest model was used for comparison with neural network approaches and to assess the reproduction of dependencies in the prepared experimental data. The results confirm the feasibility of combining experimental data and machine learning methods to model complex nonlinear corrosion processes. The approach can be used for preliminary assessment of the corrosion activity of aqueous media and for planning further laboratory and industrial researches.
The object of research is the process of designing and modeling intelligent agents of a neural network for the behavior of non-player characters (NPCs). Today, the field of video game development continues to progress, but the methodology for creating the “brain” of NPCs still uses primitive approaches with technological limitations. This creates the problem of ludonarrative dissonance, which actualizes this work, that offers a new solution. The research results demonstrate the potential of a new approach to creating intelligent and realistic NPCs in video games. The developed models demonstrate convergence of training, which confirms the ability to understand complex concepts, such as the psychological personality model International Personality Item Pool 50. The conclusions of the work are based on the results of theoretical analysis, technical modeling and practical experimentation. Analysis of the collected data forms the final confirmation of the feasibility of the proposed theory. An increase in the total step reward from –0.00275 to 0.03176 by the median for the best option and from –0.00733 to 0.02734 for the worst was recorded, taking into account the mathematical limit in [–1.0219; 0.9861] and aggressive normalization of the hyperbolic tangent function. The adaptation of models to the environmental conditions was confirmed by reducing the penalty by 30.47% by the median or by 23.59% by the extreme 5% of the data, for the worst configuration. The obtained results can be used in game development, to create the “brain” of NPCs. This will allow to get rid of the exponential complexity of development, inherent in current methodologies due to the proposed approach. Using reinforcement learning technologies, it is possible to make a more variable and detailed virtual context, with lower computational resources consumption, avoiding ludonarrative dissonance.
The object of research is the spatial variability of soil salinity in the Senegal River Basin (Mauritania). The problem to be solved is to identify the most reliable spatial interpolation technique for soil salinity mapping and support the appropriate soil salinity management. This problem is especially important as soil salinization is a serious environmental and socio-economic problem that severely constrains agricultural productivity, mainly in arid and semi-arid areas. The case research was carried out on two cultivated agricultural plots and one uncultivated plot having similar soil characteristics. The four spatial interpolation methods used and compared are: global polynomial interpolation (GPI), inverse distance weighting (IDW), ordinary kriging (OK) and radial basis functions (RBF). The predictive performance of the models was evaluated using mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Results showed statistically significant differences in the properties of the datasets, especially within the central tendency and distribution. Soil electrical conductivity (EC) varied substantially among the research areas, with mean values increasing from 1.35 to 5.58 dS × m–1 and spatial ranges extending up to 189 m. Across all three zones, IDW consistently provided the best interpolation performance, achieving coefficients of determination (R2) close to 0.99 and outperforming the other tested methods. This result implies that the proper mapping of soil salinity requires the selection of an interpolation method compatible with the statistical characteristics of the field data. They also stressed the need for permanent monitoring of salinity to guarantee the sustainable agricultural productivity and efficient land management in the irrigated coastal areas of the Senegal River Valley.
The object of research is the process of phlegmatization of the gaseous medium of vapor-air mixtures of cyclopentane when nitrogen is introduced into this mixture. The problem of using cyclopentane for the production of thermal insulation is to ensure explosion and fire safety, since the technology for creating foamed urethane thermal insulation includes the introduction of a liquid foaming agent, such as cyclopentane, into the reaction mass. It is characterized by a low boiling point and the creation of explosive mixtures with air. In this regard, the coordinates of the points of the phlegmatization curve were experimentally determined, the value of the lower concentration limit of flame propagation was obtained, which is 1.34% vol. of cyclopentane in a mixture with air, the upper concentration limit, which is 8.64% vol., the minimum phlegmatization concentration of nitrogen, which is 48.1% vol. and the value of the combustible substance at point D (1.45% vol.) and E (6.31% vol.). Adding a phlegmatizer to the “cyclopentane – air” system gradually increases the lower branch and lowers the upper branch of the phlegmatization curve, which converge at the point of the minimum phlegmatizing nitrogen concentration. The area intersecting the phlegmatization curve is capable of flame propagation, outside of which the gas mixture is not explosive and fire-hazardous. A method for calculating the minimum safe ratio of phlegmatizer and combustible gas (steam) in technological devices based on analytical dependencies has been developed and tested. Based on the results of determining the smallest ratio of volumetric concentrations of the phlegmatizer and the combustible substance in the gas mixture, at which it is impossible to ignite it in the environment of this oxidant, the coordinates of the contact point L (45.945% vol.; 1.7445% vol.) were established, as well as the numerical value of the coefficient k (k = 26.4). The practical significance lies in the fact that the results obtained were taken into account when creating a fire-safe technology for manufacturing thermal insulation.
The object of research is the process of ethanol dosing. The existing technology of ethanol dosing and shipment is based on actual data from a mass flowmeter, or a weight method, which is not accurate and not productive in terms of time. This requires constant control and involvement of human resources during product shipment and does not allow dosing the shipped amount of ethanol with the required accuracy. An ethanol dosing method has been developed that increases the accuracy and productivity of the accounting and shipment units at alcohol industry enterprises. The proposed method uses data from the flowmeter of alcohol-containing products and automatically controls the dosing process, which ensures accurate product shipment. An algorithm for the operation of the dispenser has been developed that changes the speed of the pump motor and predicts the time of completion of dosing in accordance with the current value of the product flow rate. A mathematical model for determining the volumetric strength of an alcohol product depending on temperature and its density has been presented. This allowed to increase the dosing accuracy to 0.5%, and improved the productivity of the ethanol dosing process. The proposed method allows, without changing the existing production facilities, to calculate the amount of pure alcohol in the mixture from 0 to 40°C and the volume strength in the range from 91 to 100%. It is shown that the proposed method provides dosing accuracy up to 0.5%, and allows for a 20% reduction in product shipment time due to automatic amount of pure alcohol accounting. The proposed method can be applied at production facilities using a preliminarily mounted Coriolis flowmeter from manufacturers such as Krohne or Emerson, or similar. Such a flowmeter is a rather expensive but effective basic tool for accurate ethanol dosing.
The object of research is model systems of gluten-free dough, minced fish with eggplant powder and ready-made frozen semi-finished products in a dough shell (pelmeni). A current scientific and technological problem is the deficiency of antioxidants and dietary fiber in most gluten-free food products, as well as the difficulty of forming a stable structure of gluten-free dough without additional hydrocolloids. The research aims to determining technological solutions for the production of gluten-free semi-finished products in a dough shell made of minced fish with the addition of eggplant powder. A recipe for gluten-free dough from a mixture of rice, corn and flax flour has been developed. When developing technological solutions for the production of gluten-free flour pelmeni, combinations with different ratios were varied in increments of 5%, and for minced meat, the replacement of fish raw materials with reconstituted eggplant powder was from 2% to 10% in increments of 2%. Based on the analysis of the organoleptic quality indicators of the dough, a rational ratio was established, which is equal to 60% rice, 30% corn, and 10% flax. A rational amount of reconstituted eggplant powder was established – 6% to the total mass of minced meat. It was established that the chemical composition of the developed gluten-free flour pelmeni (dough) and eggplant (minced meat) have a more balanced composition in terms of nutrients and essential substances. It was studied that the developed product is safe according to microbiological indicators. The prospect of expanding the range of frozen pelmeni with minced fish, as one of the most popular, has been revealed. The results of the conducted experiments prove that the selected eggplant powder and gluten-free flour are effective functional food products. Therefore, these products can be proposed for mass use in the production of flour culinary products, namely pelmeni.
The object of research is technical diagnostics process of nonlinear dynamic systems based on vibration signals. The problem arises during the formation of the diagnostic vector: time-domain, frequency-domain, and wavelet features increase its size to dozens of variables, while reducing this feature space may affect state recognition and anomaly detection in different ways. Therefore, the research examines whether PCA (Principal Component Analysis), t-SNE (t-distributed Stochastic Neighbor Embedding), and UMAP (Uniform Manifold Approximation and Projection) are equally suitable for supervised classifiers and a one-class detector. The material used in the research consisted of CWRU (Case Western Reserve University) records with a sampling frequency of 48 kHz. Based on these records, 4,630 segments were formed, representing 10 classes described by 49 features. Dimensionality was reduced to d = 2, 5, 10, and 20, after which the structure of the embeddings was evaluated using the silhouette coefficient, the performance of MLP (Multilayer Perceptron) and Random Forest was assessed using F1-score, and the performance of OC-SVM (One-Class Support Vector Machine) was evaluated using F1-score, Recall, and Precision. The highest silhouette value, 0.525, was obtained for UMAP. After compressing the feature space by a factor of 24.5, the F1-score of the supervised models remained above 0.976. For OC-SVM, the same method showed a different trend: as d increased from 2 to 20, the F1-score decreased from 0.978 to 0.094, whereas after PCA it did not fall below 0.984. This difference is associated with the fact that UMAP compacts the embedding of the Normal class. In the multi-class setting, this supports class separation; however, in the one-class case, it narrows the OC-SVM boundary, causing defective samples to be classified as normal more often. Therefore, the dimensionality reduction method should be selected in accordance with the subsequent model. For supervised diagnostics, UMAP with d = 2–5 is appropriate, while for anomaly detection, PCA with d = 5–10 is preferable. These recommendations are intended for real-time diagnostic pipelines operating on resource-constrained edge devices.
The object of research is the technology of pomegranate juice production. The problem addressed in the study is the need to improve the technology of pomegranate juice production, ensuring maximum preservation of biologically active substances, increasing the yield of finished juice, improving organoleptic indicators and increasing the shelf life while maintaining its natural properties. Pomegranate varieties grown in local conditions were selected as the research material. The main technological stages of pomegranate processing were studied: raw material preparation, grain separation, heat treatment and pressing using a membrane press. Particular attention is paid to the influence of temperature conditions and pressing parameters on the organoleptic indicators, the content of biologically active substances and the antioxidant activity of the juice. The yield of opaque juice from pomegranate varieties under normal conditions without heat treatment of the seeds was 39.6–44.4%, with heat treatment of pomegranate seeds at a temperature of 70–75°C for 5–8 min, the yield of opaque juice varied from 43.4 to 50.6%. When pomegranate seeds were heat-treated at 80–90°C for 4–6 min, the yield of opaque juice ranged from 44.1 to 51.2%. The pulp yield in the first treatment was 16.8–19.4%, in the second treatment, this figure was 14.7–16.3%, and in the third treatment, 14.3–15.8%. The Gesheng and Iridene pomegranate varieties demonstrated high yields and are recommended for natural juice production. The resulting waste was used in bakery to ensure waste-free technology. The results of the study can be used in food processing enterprises specializing in the production of fruit and berry juices and functional beverages, as well as in the development of new types of pomegranate-based functional beverages.
The object of this research is the decision-making processes in network traffic anomaly detection under conditions of uncertainty. The relentless growth in the number of Zero-day attacks within network traffic necessitates the development of novel detection methods. These methods must be effective in identifying both known anomalies and objects for which no a priori information exists in the training sample. The problem addressed in this work is the development of a decision-making method under uncertainty regarding the detection of Zero-day attacks in network traffic. A key requirement was to incorporate inter-model discrepancies into the decision-making process, thereby enhancing the stability of anomaly detection. As a result of the research, a decision-making method has been developed that accounts for the consistency level of estimates and adapts classification rules based on the degree of discrepancy between them. These estimates are obtained by applying base network traffic anomaly detection methods. The core idea of the proposed method is the introduction of an adaptive threshold value calculated by solving an optimization problem, with the F1-score serving as the optimization criterion. Experimental verification demonstrated that the application of the developed method improves the efficiency of decision-making processes compared to base models. The ensemble of methods consisted of Isolation Forest, One-Class SVM, and NCC; a comparison was also conducted with Random Forest. Specifically, the proposed method improved the F1-score by approximately 0.009 relative to the best-performing base model. The values of other metrics were as follows: Precision = 0.9991 ± 0.0012, Recall = 0.8024 ± 0.0292, and AUC = 0.9673 ± 0.0046. The practical application of the developed method is possible through its integration into real-world Intrusion Detection Systems (IDS).
The object of research is the process of hardware/software co-design and computational load distribution for digital signal processing (DSP) algorithms in heterogeneous systems-on-chip (SoC). The problem solved in this work is the lack of effective methods for distributing computational functions at the early design stages of architectures combining a processing system (PS) and programmable logic (PL). Unlike approaches relying on intuitive distribution or a full synthesis cycle, causing hardware overhead or timing violations, a hardware/software co-design method for DSP algorithms which accounts for the technological parameters of the SoC platform and enables analytical selection of the optimal implementation route at the system level, has been developed. A computing distribution model for the PS and PL parts is proposed, integrating platform characteristics, design object parameters, and specification constraints. A system of sub-criteria has been defined to minimize algorithm execution time under strict platform constraints. Based on these criteria, quantitative thresholds have been established to choose between software, hardware, or hybrid implementation routes, depending on the size of the design object. Using the Zynq-7000 as an example, it is proven that software implementation on the PS side is effective for small- and medium-sized objects, while transitioning to the PL part is critical for large-scale objects. It was established that transitioning from native code to high-level synthesis (HLS) and structural C code optimization strategies (specifically the C2 method) improves system metrics, cutting execution time in half while reducing resource utilization by 30%. The method is recommended for designing embedded real-time DSP systems, processing media streams, analyzing high-dimensional sensor data, and creating intelligent decision support systems.
The object of research is traditional medicine as important component of Indonesia’s cultural heritage and national identity. Traditional medicine known as jamu is one of Indonesia's cultural heritages that has become a national identity. Each region in Indonesia has its own distinctive characteristics, such as Madurese jamu, which marketing has expanded internationally. The widespread marketing requires the small and medium enterprises of traditional medicine to maintain its production. One way to ensure that traditional herbal medicine production is maintained is by predicting future production. In predicting traditional medicine, several components are needed that can be used over a certain period of time, such as inventory and previous sales. One prediction method that can be used is Autoregressive Integrated Moving Average (ARIMA), with the stages of parameter identification, parameter estimation, model verification, and prediction. However, there are several problems with the dataset, namely irrelevant features and outlier data. Therefore, statistical significance is used in feature selection using Pearson correlation and Variance Inflation Factor (VIF), as well as K-Means Clustering for outlier detection. From the several scenarios conducted, the results show that the ARIMA method with statistical significance and K-Means Clustering has the lowest MAPE of 10.252%. Meanwhile, ARIMA with statistical significance has a MAPE of 21.264%, ARIMA with K-Means Clustering has a MAPE of 31.263%, and ARIMA alone has a MAPE of 38.558%. From the research shows that the ARIMA combination with statistical significance and K-Means Clustering is able to provide better performance in predicting traditional medicine production.
The object of research is the catalytic reforming process, for which the reformate octane number is used as an efficiency indicator. The research addressed the scientific and practical problem of rapidly determining the octane number, which is an important parameter for control tasks. The proposed solution is based on the soft sensor, which is developed based on the Random Forest ensemble model. This approach allows quantifying the uncertainty of the forecast through the variance of the model’s ensemble, which is used to build an integrated anomaly detection system. The input vector consists of ten parameters, critical to the technology process dynamic and are available for direct measurement: reactor temperatures, pressures, flow rates, raw material and reformate densities, and the catalyst activity index. The training data for the soft sensor is generated on a simulation model with technological parameters varying within the nominal operating range. The data volume corresponds to 30 days of reforming process operation with a 5-minute interval. The soft sensor parameters are optimized by random search method with five-fold cross-validation. The resulting soft sensor provides high prediction accuracy (MAE = 0.41, RMSE = 0.54, R² = 0.974) on the validation sample. The research used three criteria for detecting anomalies: monitoring the forecast interval width, checking the forecast output over the historical range, and the Mahalanobis distance in the input feature space. Five abnormal process scenarios are developed and tested, including a sudden change in feedstock composition and gradual catalyst deactivation, etc. An anomaly detection rate of 92–96% is achieved, with a false alarm rate not exceeding 2.1%. The results confirm the feasibility of using ensemble methods for constructing soft sensors and for the early detection of anomalies in oil refining processes.
The object of research is the digital transformation processes and organizational readiness of Ukrainian universities to implement strategic development projects. The research analyzes the alignment of organizational structures, management processes, and strategic planning at universities, which affects the effectiveness of digital transformation projects. This paper presents the results of a comparative organizational audit of digital transformation projects at Ivan Franko National University of Lviv (IFNUL) and Kyiv National University of Construction and Architecture (KNUCA). Two complementary international standards, IPMA OCB and PMI OPM3, were used for the assessment. This allowed for a comprehensive evaluation of five areas of organizational competence and management maturity at the portfolio, program, and project levels. It also enabled the determination of the stages of process improvement and levels of organizational maturity at universities. The combined approach to auditing the digital transformation of universities is proposed, which integrates the assessment of organizational competence and management process maturity. The objective of the research was to identify the universities' strengths and weaknesses in key areas of competence and management. A comparative analysis revealed that IFNUL exhibited notable individual staff competencies and well-developed international ties, while KNUCA demonstrated effective educational processes in technical and engineering fields. However, both institutions face a significant challenge: organizational fragmentation. In the case of IFNUL, the consequences are evident in fragmented management and inadequate integration of digital systems. In the case of KNUCA, there are difficulties with the prioritization of the project portfolio and the monitoring of implementation results. The research's findings indicate that the primary impediments to digital transformation are structural and strategic, rather than technological, and it proffers recommendations for enhancing the management and strategic planning processes within academic institutions.
The object of research is the process of online identification of wagon motion parameters during the sorting process within the framework of its application in the digital twin structure of a sorting complex. The problem is the insufficient efficiency of existing sorting technologies, which fail to adequately account for the individual dynamic properties of wagons. Traditional methods rely on averaged standard values of resistance parameters or simplified characteristics of wagon rolling qualities. Inadequate speed regulation leads to either short rollouts of wagons or their collision at unsafe speeds. In this paper, a vector model of wagon motion is formulated, which adequately accounts for the dynamics of resistance forces depending on the dynamics of wagon position changes on the track and its spatial orientation. The observability conditions for two hidden resistance parameters – the specific rolling resistance coefficient and the specific aerodynamic coefficient – are determined based on kinematic measurements. It is proven that curved track sections act as a natural excitation factor, enabling separate identification of both parameters. An approach to the online identification of these parameters has been developed based on the adaptation of the Unscented Kalman Filter (UKF) algorithm, which, unlike other similar algorithms, allows for the adequate consideration of wagon motion model non-linearity without simplifications or linearization. Experimental researches on the digital twin proved that even with an initial parameter error of 90%, the algorithm ensures stable convergence over a 200 m observation section with final errors of 2.2% and 1.2%, respectively. Thus, during the simulation, it was established that the proposed approach is capable of providing a prediction error for the free-rolling wagon trajectory length to the stopping point at a level of approximately ~22 m. Such accuracy is sufficient to ensure the efficient and safe operation of sorting systems under real conditions.
The object of research is the processes of dissemination and blocking of disinformation narratives in the Ukrainian cyberspace during the first 100 days of the full-scale invasion by the Russian Federation. The research problem is the need to create an effective mechanism for counteracting disinformation that can reduce the psychological impact of manipulative messages on the population and ensure the information resilience of the state. The paper analyzes the functioning of a model for counteracting disinformation penetration based on monitoring the national cyberspace, blocking sources of disinformation, tracking Russian information resources, and promptly refuting fake messages. To evaluate the effectiveness of the model, a social experiment using online questionnaires was conducted. The research involved 14,053 respondents from different regions of Ukraine. The obtained results showed that after the 37th–38th day of the full-scale invasion, the number of respondents who trusted Russian disinformation narratives about the possible capitulation of Ukraine began to steadily decrease. It was found that the key factors reducing trust in disinformation were the successful defense of Kyiv, international support for Ukraine, the blocking of Russian information resources, and the activities of the Ukrainian IT Army. According to the analysis, by the 83rd day of the war, the accessibility of most websites spreading disinformation had been almost completely minimized. The model employs an empirical 70:20:10 distribution of operational efforts among information space monitoring, tracking and information response activities. The obtained results can be used for the development of information security systems, strategies for counteracting disinformation, and cybersecurity models in countries facing external information influence or hybrid confrontation.
The object of research is the automatic control system for the boiler-unit combustion process, which coordinates fuel and air supply, stabilizes furnace draft, and provides slow correction of excess air based on the measured oxygen content in flue gases. The system coordinates the supply of fuel and air, applying a gradual correction of excess air based on the measured oxygen content in the flue gases. The main problem is to ensure coordinated control of the fuel, air and draft channels under the conditions of plant inertia, transport delays, actuator limitations, changes in fuel properties and load effects. To solve this problem, a control structure is proposed with a division into fast and slow levels, coordinated with each other. In this structure, the fast control level generates a setpoint air flow rate using the fuel flow rate signal. The slower control level applies a limited correction of the fuel-air ratio coefficient in accordance with the deviation of the oxygen concentration from its setpoint. The proposed control structure was tested using simulation in MATLAB/Simulink (The MathWorks, Inc., USA). The obtained transient characteristics demonstrate the system's response to a change in the set value of the thermal load. The control signal of the fuel channel remained within the permissible range, which confirms the correct operation of the constraints and the absence of accumulation of the integral component beyond the physically permissible limits. The system shows effective compensation of load disturbances and restoration of the required oxygen level without persistent fluctuations. The obtained results demonstrate a clear separation between fast flow coordination and slow oxygen-based correction. Thanks to this separation, the gas analyzer channel does not degrade the dynamic characteristics of the faster control loops. The research results can be used for modernization and tuning of combustion control systems in boiler units of thermal power plants and industrial boiler houses.
The object of research is the production of a polymer cosmetic product with fungicidal and cosmetic-masking effects for the onychomycosis treatment in men. This work addresses the problem of the lack of products for men to treat onychomycosis and simultaneously mask the negative aesthetic changes of the nail caused by it. Onychomycosis is a common nail infection, which is an acute problem for military personnel due to moisture in shoes, lack of hygiene and stress. The existing therapy has disadvantages: low permeability of local agents, hepatotoxicity of systemic drugs and aesthetic defect, which reduces the motivation of patients to treatment. Zinc pyrithione (0.25%) and urea (7.0%) were used as active ingredients, which acts as a keratolytic and plasticizer of the polymer matrix. The fungicidal activity of the developed varnish was evaluated by the agar diffusion method against reference strains, the physicomechanical properties of the films were investigated according to the DSTU ISO 1519:2015 standard, and the optical masking properties were determined by the turbidimetric method according to the McFarland scale. It was established that the concentration of zinc pyrithione 0.25% provides pronounced zones of pathogen growth inhibition: 1.9 ± 0.1 cm for T. mentagrophytes, 2.2 ± 0.2 cm for C. albicans and 1.7 ± 0.1 cm for A. niger. The incorporation of insoluble zinc pyrithione microparticles increases the optical turbidity of the system by 24 times, providing a matte opalescent effect. The synergistic effect of urea and zinc pyrithione on the mechanical properties of the coating was revealed: the relative elongation of the film increases from 11% to 29%, and the tensile strength increases more than 4 times when applying fungicidal varnish in 2–3 layers. The developed product eliminates the need for additional cosmetics, which increases patient compliance and makes it promising for use among military personnel.
The object of research is the process of waste-free, resource-saving processing of local plant raw materials into multicomponent functional matrices of a high degree of readiness. The problem that was solved was aimed at increasing the complexity of processing plant raw materials in conditions of minimizing secondary waste. Classical approaches to raw material processing are focused on obtaining a limited number of semi-finished products in conditions of the formation of secondary raw materials that are not fully processed, increasing resource efficiency. Heat and mass exchange equipment with intermediate coolants is used for processing plant raw materials, which leads to an increase in the cycle duration and specific costs. The concept was substantiated within the framework of improving the waste-free, resource-saving technology of processing local raw materials (Jerusalem artichoke, pumpkin, carrot, sweet potato and chaenomeles). The technology is focused on the comprehensive use of functional and technological properties of raw materials during processing on equipment with local heat supply: preliminary heat treatment is carried out in the range of 65–90°C (2–10 min) in a sectional cassette-capacitor apparatus. Concentration of the multicomponent matrix in a rotary film evaporator at a temperature of 45–65°C to a content of 30–45% dry matter. IR drying of the concentrated semi-finished product in the range of 45–60°C in a roller IR dryer. The recipe ratio: 25% Jerusalem artichoke, 30% pumpkin, 15% carrot, 20% sweet potato and 10% chaenomeles forms a multicomponent matrix with inulin, pectin, carotenoid, starch and antioxidant properties. The technology fully processes pumpkin, pectin-containing residues of chaenomeles and unpeeled Jerusalem artichoke. The difference is the comprehensive use of local plant raw materials to expand the range of functional semi-finished products and products obtained using resource-saving equipment and technological solutions.
