St. Cyril and St. Methodius University of Veliko Tarnovo

Introduction. The active adoption of digital tools is crucial for improving agricultural efficiency, which is fundamental to ensuring food security. The digitalisation of agricultural production is a key component of the measures for transitioning to a digital economy. Aim and tasks. This study aims to assess the relationship between the net profit of agricultural enterprises and the number of digital products used. It also seeks to identify the factors influencing the efficiency of agricultural production. Results. This study examines at several key factors of agricultural production. First, it considers at the share of the labour force involved in agricultural production, which has declined by 17.2% globally, 7% in the EU, and 5.2% in Ukraine. Secondly, it analyses changes in the number of workers required to produce 1% of value added from 1991 to 2023, showing a decline of 3% globally, 1.5% in the EU and an increase of 0.5% in Ukraine. Thirdly, the study assesses the level of digital skills among workers in the agricultural sector. In the EU, this level did not exceed 0.5% from 2016 to 2024, while Ukraine data is unavailable. Finally, the study includes case studies of two Ukrainian companies engaged in developing and implementing digital tools for agricultural production. The findings regarding the dependence of net profit and the number of digital instruments used revealed relatively high correlation coefficients: 0.776 for a group of 41 AGRIChain clients and 0.902 for a group of 34 Kernel Digital clients. The resulting models of net profit dependence on the number of digital instruments used (with slope coefficients of 365.9 for AGRIChain and 13.13 for Kernel Digital) indicate the potential for further refinement. Conclusions. The establishment of a digital support system for agricultural production involves significant changes in employee competencies, a decrease in the total number of employees, and a reduction in the share of employees involved in agricultural production. Ukraine is characterised by an increase in the number of workers employed in agricultural production per 1% of added value, which is explained by structural changes in the industry. This study proposes adding metrics to statistical reporting to capture the number of digital technologies used in the production process and the number of employees skilled in using these technologies.

The pigments utilised for decoration of Early Neolithic painted pottery (ca 6000-6500 cal BC) mark advanced cha & icirc;ne operatoire stages, associated with the making of visually effective ceramic vessels. Considered as reflecting the genuinely intertwining technological, symbolic and aesthetic aspects of everyday life, representable wares from Galabnik - a long-lived settlement mound in southwestern Bulgaria, consisting of ten building horizons - are analysed chemically, macro- and microscopically (PXRD, SEM and optical microscopy). The established variability, much greater than previously thought, demonstrates the dynamic local development of the newly introduced pottery craft. These major technological components, encoding intentional acts and raw materials awareness, are considered along with transformations of know-how over time, as well as of the effects of post-depositional alteration. Traditional ways of doing and potential innovative approaches are illustrated by the application of calcareous raw materials and fine white clays for the white colours; of hematite-based paints and hematite-enriched clay slips for the red hues; and of magnetite-bearing materials used for the brown and the black paints. The choice of pigments is thus indicative of the locally dynamic ceramic technology, associated with the new ways of sedentary life, yet perhaps not completely detached from earlier, Pre-Neolithic approaches.

Introduction. This study examines the methodological considerations involved in applying the comparative approach and incorporating risk factors when assessing shares in the capital of non-public companies. The lack of open market information significantly complicates the objective assessment of the value of such companies, particularly in the Bulgarian market. Aim and tasks. This study aims to derive the value of a share of a non-public company by comparing it with public companies and making necessary adjustments with a discount for size and specific risk. Results. This study applies a comparative approach to the valuation of companies listed on the Bulgarian capital market based on economic indicators for 2021-2023. The value of a company's share was determined based on financial multiples (IC/RI, IC/EVA, ROE, etc.) and a comparative approach, with adjustments for uncontrollability, liquidity, company size, and specific risks. Based on the calculated multiples, companies with higher profitability and efficiency indicators (ROE, ROA, and ROIC) demonstrated better financial stability and competitiveness. For example, ROE values ranged from 0.09 to 0.84, ROA from-0.013 to 0.28, and ROIC from 0.008 to 0.64, with the best performers showing consistently positive results. In contrast, companies with poor or negative performance across most ratios may face higher risk exposure and ineffective management. This is evidenced by extremely low or negative values for IC/RI (-68.99 to 14.42) and IC/EVA (-1,066.39 to 20.11), reflecting inefficient capital allocation and weak value creation. Negative ROA (-0.012) and low ROIC (0.008 to 0.039) suggest potential operational inefficiencies. Conclusions. The comparative approach to business valuation enables the estimation of the value of a privately held (closed-type) company by applying appropriate adjustments to the financial data of comparable publicly traded (open-type) companies. This study proposes an algorithm for determining a company's share when considering controlling/non-controlling, the degree of liquidity of a block of shares, size, and specific risk through a comparative valuation approach. Applying such an algorithm in valuation practice is primarily based on the valuer's professional experience. It can be advantageously used when valuing privately held companies.

Digital learning requires specialized archives and websites that guide learners through its structured content, multiple links to archives and additional materials, interactive elements, and knowledge-testing tools. Тhe creation of a website has various stages of design, implementation, testing and administration, including modern standards and guidelines with different orientations. In the last few years, accessibility is also often required in web development, especially for public service sites and educational platforms. In the process of planning and development, we think of the structure, taxonomy, design and functionalities, as well as the specificity of the objects, the learning objectives and the characteristics of the learners. The report presents an analysis of digital archives and training modules related to cultural heritage. Taking into account the specificity of cultural heritage objects through their descriptions and ontologies and good practices for design and accessibility. Several web platforms related to culture and folklore are analyzed with predefined criteria. Specifications for a web-based social-oriented application in the field of education and cultural heritage are systematized. The paper also presents a model for structuring and categorizing the content of a system of developed learning modules. Particular interest is paid to the relationship and correspondence between object metadata and the category structure in the taxonomic model, as well as learning modules in a linear structure and a glossary showing the relationships between terms and lessons. The model includes opportunities for personalization of the training and taking into account various limitations of the visitors - visual, auditory, cognitive, physical, and temporary.

The application of artificial intelligence (AI) in business segmentation provides effective and innovative methods for analysing large data sets and automating customer segmentation processes. Artificial intelligence allows businesses to better understand what customers need, their preferences and behaviour, and to personalise approaches to different segments. Instead of traditional manual methods, AI uses machine learning algorithms to discover hidden patterns and trends in data. The report presents a deep investigation on the application of Artificial Intelligence in business segmentation. It discussed the basic methods and tools used in AI segmentation and the role of AI segmentation in optimisation of marketing strategies, providing new opportunities for personalisation, analysis, and automation.