Sumy State University (SumDU) is a higher educational institution in Sumy, Ukraine. It enrolls about 12,000 students studying pre-undergraduate, undergraduate, specialist, and master degrees in 55 majors and 23 fields. About 1,900 foreign students represent almost 50 countries worldwide.
Aim. The purpose of the work is to study the functional, technological, and culinary properties of wild boar meat and to establish the influence of different heat treatment methods on sensory parameters and product yield. Methods. The study focused on wild boar meat obtained through traditional hunting in the hunting grounds of the Sumy region, Ukraine. Results. The protein content was 21.56-22.68 %, fat-2.25-2.64 %, which affected the energy value of the meat: 112-118 kcal/100 g. It was found that the meat of female wild boars under the age of one year has a slightly lower nutritional value (112 kcal/100 g) compared to the meat of males, which is due to the lower protein and fat content and higher moisture content. When comparing the functional and technological parameters of raw meat between male and female wild boar, no significant differences were found, indicating a lack of special strategies for processing or marketing meat of animals of different sexes. Cooking wild boar meat by frying (180 degrees C for 5-10 min) proved to be the most effective heat treatment method for preserving optimal quality characteristics, particularly in terms of sensory and textural characteristics preferred by consumers. Conclusion. Based on the results of the study of the functional, technological, and culinary properties of wild boar meat of both sexes and the establishment of the influence of various heat treatment methods on sensory indicators and losses during cooking, it can be concluded that wild boar meat has high nutritional value, sufficient functional indicators, and has a favorable sensory assessment during culinary processing.
The relevance of the study is to determine the chemical composition of wastewater and its impact on the technical condition of the sewage system. Aim. The aim of the work was to investigate the chemical composition of wastewater and its effect on reinforced concrete structures of the sewage system. Methods. X-ray diffractometric, colorimetric, fluorimetric, gravimetric, titrimetric. Results. The study of the chemical composition of wastewater showed a shift in pH to the alkaline side, the suspension of dry substances in water was above the norm in the fourth sample by 5.3 %, in the fifth - by 36.3 %; the dry residue in the first sample by 31.3 %, in the second - by 20.2 %, in the third - by 13.9 %, in the fourth - by 9.3 % and in the fifth - by 27.4 %. The content of ammonium nitrogen was higher in the fourth experimental sample by 52.3 %, and in the fifth by 54.1 %. The level of nitrite ions in the first sample was higher by 9.1 %, in the second by 6.0 %, in the third and fifth by 42.4 %, and in the fourth by 36.4 %; the increase in nitrate ions in the first sample was by 11.1 %, in the second by 21.6 %, in the third by 16.0 %, in the fourth by 19.5 %, and in the fifth by 22.4 %. Conclusions. The implemented measures to reduce wastewater pollution were effective and reduced the load on concrete sewage structures. The scientific novelty of the results obtained lies in the field study of wastewater from a settling well of a pharmaceutical enterprise before and after preventive measures.
This study presents a comprehensive experimental and theoretical investigation of size effects in the electrical resistivity of FexNi1−x and FexCo1−x thin-film alloys across a wide range of thicknesses (15–80 nm) and Fe atom concentrations (x = 0.2–0.8). Binary thin-film alloys were prepared by the method of electron-beam evaporation under high-vacuum conditions, followed by thermal annealing at 700 K. Transmission electron microscopy was used for composition-dependent structural transitions in Fex Ni1−x and FexCo1−x thin-film alloys. Electrical transport measurements demonstrated typical metallic thin-film behaviour, with resistivity decreasing as the thickness increased, while the temperature coefficient of resistance increased correspondingly. The experimental data were analyzed within the framework of linearized and isotropic scattering models proposed by Tosser and Tellier. Results indicate that grain boundary scattering dominates the electrical transport properties, with surface scattering parameters (p < 0.036) suggesting nearly diffuse scattering conditions. Reflection coefficients at grain boundaries varied between 0.22 and 0.40, indicating a strong dependence on grain size evolution with respect to thin-film thickness.
This paper presents a theoretical framework for predicting molten jet breakup at the outlet of a rotating granulation system operating without forced excitation. The study focuses on the critical regime in which mechanical excitation is absent, and jet disintegration is governed solely by intrinsic hydrodynamic instabilities. The analysis is based on the linear stability theory of viscous liquid jets, employing the Rayleigh-Plateau and Tomotika approaches adapted to melt conditions typical of industrial granulation processes. The Navier-Stokes equations are formulated in a cylindrical coordinate system for an axisymmetric, incompressible viscous jet with appropriate kinematic and dynamic boundary conditions at the free surface. The breakup mechanism is characterized using key dimensionless parameters, including the Ohnesorge, Weber, Reynolds, and Capillary numbers, enabling identification of the dominant instability regime. Analytical expressions are derived for the most unstable wavelength, perturbation growth rate, breakup time, and characteristic droplet diameter. These relationships are evaluated for representative thermophysical properties of molten urea. Theoretical predictions obtained from classical Rayleigh theory, viscosity-corrected models, and modern empirical correlations show strong agreement, with deviations not exceeding 7%. Sensitivity analysis indicates limited dependence of the predicted droplet diameter on moderate variations in viscosity, surface tension, and jet velocity. The proposed model provides a physically grounded basis for predicting and controlling granule size distribution in rotating granulation systems operating without external mechanical excitation.
The aim of this study is to explore the potential and limitations of artificial intelligence (AI) in psychological counseling. A comparative method was used, based on assessing the differences in interpretation between psychology students and AI systems. Specifically, the phenomenon of overinterpretation, in which conclusions lack sufficient empirical support in the source material, was analyzed. This article utilized a mixed-methods approach. Specifically, the theoretical part included a literature review on AI counseling across five parameters: methodological commitment, emotional support, therapeutic alliance, ethical considerations, and accessibility. The empirical part involved a pilot pedagogical experiment in which psychology students (N = 44) and the Claude 3.5 Sonnet system independently analyzed identical psychological interviews (N = 22). The resulting analytical texts (N = 66) were subjected to discourse analysis based on the identified markers. For a more indepth analysis, an independent expert review was used. A theoretical review found that AI is effective within structured protocols (e.g., cognitive behavioral therapy). However, AI has several limitations in establishing a therapeutic alliance and interpreting deep emotional experiences. An empirical study identified stylistic differences between student-produced texts and AI-generated texts. Specifically, student-produced texts were more than three times more likely to contain markers of epistemic caution (3.84 versus 1.20 per 1,000 words). AI-generated texts showed a twofold increase in markers of causality (4.58 versus 1.92) and recommendations (3.61versus 1.60). Furthermore, AI was more likely to overinterpret type A texts, drawing categorical conclusions based on the absence of information in the original text. Thus, integrating AI into psychology education requires the development of a new professional competency, the essence of which lies in the ability to critically evaluate AI-generated content. Also important is the ability to identify interpretive errors and apply strict evidence boundaries. Artificial intelligence can serve as an effective didactic tool for developing critical thinking, provided it is used as a supplementary educational resource. Using artificial intelligence as a standard for professional analysis is not pedagogically incorrect.
