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Introduction. The article deals with the analysis and coverage of statistical data related to the development and application of temporary loads and the relevant norms for the design of bridge structures. Problem Statement. Over the past 5-7 years, the highway bridges of Ukraine have undergone significant changes in the intensity and composition of traffic flows. At the same time, the load, which is established by the current regulations, does not take into account the tendency to increase the weight of cars. At the same time, a significant part of the building's life cycle takes place under conditions of overload, which leads to premature destruction and overspending on repairs. In this regard, it is not always possible to unambiguously assign the type of calculated load, which can cause significant errors in the assessment of the strength and serviceability of bridge structures. Materials and methods. Comparison of estimated vehicle loads of models H-10, H-13, H-18, NK-80, Н-30, NK-100, NG-60, A11, A15, AB-51, AB-74, AB-151 (from 1931 to 2023) with the LM1 model of Eurocode EN 1991-2 shows that there is some balance between the two. The estimated load according to Ukrainian standards is intermediate between the standards of Europe and the USA. However, the level of characteristic loads is almost twice as low as in the Eurocode. This is a significant shortcoming of state building regulations, as it reduces the level of reliability of calculations.

У статті розглядаються питання недобро­совісної конкуренції. Аналізуються практичні кейси використання недобросовісної реклами. Доведено, що недобросовісною конкуренцією є будь-які дії у конкуренції, що суперечать торго­вим та іншим чесним звичаям у господарській діяльності. Розглядається співвідношення недо­бросовісної та добросовісної конкуренції. Так, недобросовісна конкуренція є порушенням норм різного законодавства, правил і звичаїв веден­ня бізнесу, торгівельних відносин, господар­ської діяльності, підприємництва тощо. У свою чергу, добросовісна конкуренція передбачає найбільш комфортні та сприятливі умови для роботи суб’єкта господарювання. У сучасному бізнесі не всі підприємці працюють сумлінно, використовуючи різні методи. Здійснено порівняльний аналіз недобросовіс­ної конкуренції та недобросовісної реклами як реклами, що вводить або може ввести в ома­ну споживачів реклами, завдати шкоди особам, державі чи суспільству внаслідок неточності, недостовірності, двозначності, перебільшення, замовчування, порушення вимог щодо часу, місця і способу розповсюдження; порівняль­на реклама - реклама, що містить порівняння з іншими особами та/або товарами (діяльністю) іншої особи, прямо чи опосередковано ідентифі­кує конкурента або товари чи послуги, що про­понуються конкурентом. Доведено, що недобросовісна конкуренція може бути в діях як підприємців, так і суб’єк­тів некомерційної господарської діяльності, бю­джетних установ тощо. За великим рахунком, недобросовісною кон­куренцією є будь-які дії, які перешкоджають здійсню діяльності інших суб’єктів господарської діяльності та/або надають неправомірну пере­вагу конкурентам, що створює перешкоди для діяльності інших суб’єктів; це може бути і зго­вір, і «чорній» піар, і порівняльна чи недобро­совісна реклама. Розповсюдження, поширення інформації, яка може вплинути на рішення спо­живача придбати той чи інших товар, замовити послугу, при цьому така інформація викликає негативну реакцію споживача на конкретного суб’єкта, який реалізує такий товар чи послуги, може бути недобросовісною конкуренцією. Такі дії дуже ймовірно можуть нанести щонайменше репутаційну шкоду одному чи декільком суб’єк­там господарської діяльності. Водночас, таку шкоду, причинно-наслідковий зв’язок між діями та шкодою слід буде довести.

Beef production is driven by the need to ensure the country’s food security, meet the processing industry’s demand for raw materials, and increase state budget revenues from exports. The purpose of this study was to highlight the status and trends of production in the world and Ukraine, to identify issues and find areas of development considering international economic integration. The methods employed were analysis, synthesis, generalisation, specification, mathematical, and graphical. The study identified the upward trend in global beef and veal production, the main producing countries and the predominant stability of their share in global volumes over the decades. It was found that higher beef production does not always allow for higher per capita volumes (e.g., in China and India). Ukraine’s production and its share in global volumes are declining, although production exceeds consumption. It was found that the share of agricultural enterprises in the structure of economic entities is decreasing, and there is a direct dependence between their number and production volumes, which prevails in the Forest-Steppe zone. The largest producing regions (Kyiv, Vinnytsia, Kharkiv, Lviv) were highlighted, where priority development in the post-war period is expected. The study found the principal reasons for the decline in production, including a decrease in livestock numbers, negative profitability, and rising average consumer prices. Based on the SWOT analysis results, development prospects were identified and relevant proposals were developed. The reasons for the decrease in the number of cattle and beef were proposed to be distinguished as general, and those related to enterprises and household farms; their interdependence was emphasised. The study stressed the need for state support for producers by providing food aid to the population free of charge and by purchasing products at lower prices depending on their financial level. International economic integration was singled out as one of the key factors of increasing beef production. The study emphasised the need to consider the consumer preferences of the population of countries with which regional trade agreements have been concluded, specifically regarding the production of kosher and halal products. The findings of this study and the proposals provided can be used to develop government measures to increase beef production and increase its share in world exports

Alimentary infertility is becoming increasingly important due to the toxic effects of mycotoxins, especially zearalenone and deoxynivalenol, which leads to a decrease in the efficiency of the cattle breeding industry, so it is necessary to examine this problem. The purpose of the study was to identify the effect of the complex use of sorbents, acidifiers, and aromatase inhibitors on the reproductive capacity of cows in mycotoxicosis. During the study, clinical methods (general examination) and biochemical methods (serum content of calcium, magnesium, urea, total and direct bilirubin, malonic dialaldehyde, aminotransferase activity) were used. Cows were divided into two groups: the first group was a control group, where no treatment was used, and cows of the second group received a comprehensive treatment method. A decrease in the level of calcium to 1.68±0.53 mmol/L, phosphorus – 0.98±0.16, selenium – 0.64±0.1 mmol/L, an increase in the level of magnesium to 1.15±0.17 mmol/L, an increase in aspartate aminotransferase to 121.33±3.91 units, alataminotransferase to 79.31±6.53 units, and total bilirubin to 10.21±0.55 mmol/l, malone aldehyde up to 8.27±0.41 mmol/L, and ceruloplasmin up to mmol/L 2.14±0.39 in chronic mycotoxicosis caused by zearalenone and deoxynivalenol. When using a complex treatment regimen for animals, a substantial change and approximation to the reference levels of selenium, creatinine, AST, ALT, malonic aldehyde, and ceruloplasmin were established. The use of the proposed treatment regimen increases the fertilising capacity of cows after the 1st calving by 18.15%, cows aged 3-4 years – 30.12%, and cows of the older age group – by 40.47%. A complex method of treatment of cows for mycotoxicosis caused by zearalenone is proposed using an acidifier based on organic acids, a sorbent based on celeolite and an aromatase inhibitor, which can be used in livestock farms for the production of milk and meat of various forms of ownership to restore and normalise the reproductive ability of cows

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

ABSTRACTThis research project is designed to investigate the behaviour of fires resulting from hydrogen leakage in engine rooms and evaluate the associated fire risks, aiming to compare these risks with those posed by traditional fuels which can be used in the shipping industry today. The study employs a model of the engine room constructed according to the original dimensions of the vessel under study. The primary objective of this research was to determine if the fire risk associated with hydrogen would be higher or lower than that of traditional fuels. To achieve this, a series of simulation scenarios were meticulously executed and subsequently analysed. The outcomes of the simulations indicate that hydrogen fires do not present a higher threat when compared to fires fuelled by marine gas oil or liquefied petroleum gas. Notably, hydrogen exhibits superior fire behaviour, characterized by the absence of smoke development and relatively less harm to machinery and crew, thus outperforming conventional fuel fires. This finding suggests that hydrogen-related fire risks can be brought under control and highlights its potential as a comparatively safe fuel choice in the maritime domain.

At present, the current direction in poultry farming is the development of methods for raising poultry without the use of antibiotics to overcome antibiotic resistance. For this purpose, it is recommended to use mineral supplements of chelated forms of microelements of zinc, copper, and manganese in poultry diets. Chelated minerals are characterised by better digestibility during intensive broiler farming, which limits the use of minerals and reduces environmental pollution. The purpose of the study was to examine the quality of meat of broiler chickens of the Cobb-500 cross, provided that chelated forms of zinc, copper, and manganese are included in the diet. Experimental studies were conducted in 2021 on broiler chickens of the Cobb-500 cross. Two groups of 20 heads of poultry were formed to examine the chemical composition of meat. Poultry of the control group received a basic diet with zinc, copper, and manganese sulfates, and poultry of the experimental group – enriched with chelated compounds of these microelements. The addition of chelated compounds of zinc, copper, and manganese to the diet of broiler chickens leads to an increase in the amount of fat, calcium and zinc in white muscles by 69.6, 24.6% and 1.4 times, and in red muscles-by 41.1, 30.9% and 3.4 times, respectively. The content of copper and manganese increases by 48.0 and 95.5% in red muscles and by 28.1 and 15.2% in white muscles compared to the control group. Therewith, there is a decrease in the relative content of essential amino acids by 1.1-1.3% and an increase in non-essential amino acids by 2.6-2.7%. According to the overall assessment of organoleptic parameters of broiler chickens fed zinc, copper, and manganese chelates, the sum of points was 2.0 points higher in the femoral muscles and 1.5 points higher in the pectoral muscles. In addition, according to the tasting assessment of meat from the thigh muscles of broiler chickens, more points were obtained in terms of tenderness by 10.0%, taste by 12.2%, and aroma by 13.2%. According to the tasting assessment of meat from the pectoral muscles of broiler chickens, more points were obtained in terms of tenderness by 18.9% and aroma by 10.3%. According to the reaction with copper sulfate, the content of ammonium and ammonia salts, broiler chicken meat was fresh and obtained from healthy poultry. As a result of organoleptic, physico-chemical, and biochemical studies of broiler chicken meat under the conditions of adding microelement chelates to the diet, it was established that it belongs to fresh and high-quality for consumption. These studies argue for the use of chelated compounds of microelements in poultry farming and contribute to their further introduction into production

This study investigates stress recognition using galvanic skin response (GSR) and photoplethysmography (PPG) data and machine learning, with a new focus on air raid sirens as a stressor. It bridges laboratory and real-world conditions and highlights the reliability of wearable sensors in dynamic, high-stress environments such as war and conflict zones. The study involves 37 participants (20 men, 17 women), aged 20–30, who had not previously heard an air raid siren. A 70 dB "S-40 electric siren" (400–450 Hz) was delivered via headphones. The protocol included a 5 min resting period, followed by 3 min "no-stress" phase, followed by 3 min "stress" phase, and finally a 3 min recovery phase. GSR and PPG signals were recorded using Shimmer 3 GSR+ sensors on the fingers and earlobes. A single session was conducted to avoid sensitization. The workflow includes signal preprocessing to remove artifacts, feature extraction, feature selection, and application of different machine learning models to classify the "stress "and "no-stress" states. As a result, the best classification performance was shown by the k-Nearest Neighbors model, achieving 0.833 accuracy. This was achieved by using a particular combination of heart rate variability (HRV) and GSR features, which can be considered as new indicators of siren-induced stress. [ABSTRACT FROM AUTHOR]

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The use of digital technologies in modern educational environments is an urgent requirement. This article aims to study mobile technologies as an effective mechanism for supporting and developing inclusive learning in higher education institutions. To achieve this goal, the article employs methods of scientific content analysis of literature and comparison, which allow for identifying the positive aspects of integrating mobile technologies into inclusive education and the difficulties that exist on this path. The study results indicate a need to utilize both traditional and specialized technologies in the modern higher education landscape. The analysis reveals that traditional technologies supporting inclusion primarily include applications for organizing learning, mobile communication technologies, and applications for creating specialized learning content, including multimedia and virtual technologies for individuals with special educational needs. Among the specialized tools for supporting inclusive learning are apps that enhance vision and hearing. This category also includes mobile applications that support lectures through various subtitle options. The study found that such mobile technologies improve knowledge acquisition and enhance the efficiency of the learning process. It concludes that these applications contribute to greater student integration, reduce social isolation, develop social skills, and increase self-confidence. [ABSTRACT FROM AUTHOR]

(1) Background: Detecting people and technical objects in various situations, such as natural disasters and warfare, is critical to search and rescue operations and the safety of civilians. A fast and accurate detection of people and equipment can significantly increase the effectiveness of search and rescue missions and provide timely assistance to people. Computer vision and deep learning technologies play a key role in detecting the required objects due to their ability to analyze big volumes of visual data in real-time. (2) Methods: The performance of the neural networks such as You Only Look Once (YOLO) v4-v8, Faster R-CNN, Single Shot MultiBox Detector (SSD), and EfficientDet has been analyzed using COCO2017, SARD, SeaDronesSee, and VisDrone2019 datasets. The main metrics for comparison were mAP, Precision, Recall, F1-Score, and the ability of the neural network to work in real-time. (3) Results: The most important metrics for evaluating the efficiency and performance of models for a given task are accuracy (mAP), F1-Score, and processing speed (FPS). These metrics allow us to evaluate both the accuracy of object recognition and the ability to use the models in real-world environments where high processing speed is important. (4) Conclusion: Although different neural networks perform better on certain types of metrics, YOLO outperforms them on all metrics, showing the best results of mAP-0.88, F1-0.88, and FPS-48, so the focus was on these models. [ABSTRACT FROM AUTHOR]

Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera.

Since European society is experiencing an aggravation of the issue of energy security, the production of renewable energy is becoming increasingly important. The advantages of biofuel—in particular, biogas—and the positive effects of the development of its production are summarized within the framework of the problem statement. It is emphasized that the production of biogas from various renewable raw materials causes economic, ecological, and social effects. The development of biogas production can be especially active in combination with the development of the agricultural sphere. In response to today’s demand, the authors in this research present a model of the correlation between the output of biogas from different types of organic mass and specify the factors affecting it. In particular, a multiple econometric model of the relationship between the output of biogas from different types of organic mass and the content of dry organic matter and the share of possible methane content in organic matter was built; the density of the connection between the factors and the resulting feature was evaluated; the tightness of the general relationship (influence) of independent variables on the dependent variable was checked using the coefficient of determination; and the reliability of the correlation characteristics was estimated using Fisher’s and Student’s tests. As a result, with the use of convincing evidence—in particular, taking into account the potential of the Ukrainian agricultural sector—the feasibility of further development of biogas production in combination with the development of agricultural production is substantiated.

Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world’s population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks.