EXPERIENCE WITH THE USE OF MATHEMATICAL STATISTICS METHODS FOR ASSESSMENT OF AGRICULTURAL PLANTS STATUS V.M. Bure, A.F. Petrushin, E.P. Mitrofanov, O.A. Mitrofanova, V. Denisov

Despite widespread adoption, traditional Agile project management practices often fail to ensure successful delivery of enterprise-scale software projects. One key limitation lies in the absence of a conceptually defined structure for the various types of Agile activities and their interactions. As a result, Agile methodologies typically lack formal indicators for evaluating the semantic content and progress status of project activities. Although widely used tools for Agile project management, such as Atlassian Jira, capture operational data, project status assessment interpretation remains largely subjective—relying on the experience and judgment of managers and team members rather than on a formal knowledge model or well-defined semantic attributes. As Agile project activities continue to grow in complexity, there is a pressing need for a modeling approach that captures their causal structure in order to describe the essential characteristics of the processes and ensure systematic monitoring and evaluation of the project. The complexity of the corresponding model must correlate with the causality of processes to avoid losing essential properties and to reveal the content of causal interactions. To address these gaps, this paper introduces a causal Agile process model that formalizes the internal structure and transformation pathways of Agile activity types. To our knowledge, it is the first framework to integrate a recursive, causally grounded structure into Agile management, enabling both semantic clarity and quantitative evaluation of project complexity and progress. The aim of the article is, first, to describe conceptually different Agile activity types from a causal modeling perspective, its internal structure and information transformations, and, second, to formally define the causal Agile management model and its characteristics. Each Agile activity type (e.g., theme, initiative, epic, user story) is modeled using the management transaction (MT) framework—an internal model of activity that comprises a closed-loop causal relationship among management function (F), process (P), state attribute (A), and control (V) informational flows. Using this framework, the internal structure of Agile activity types is normalized and the different roles of activities in internal MT interactions are defined. An important feature of this model is its recursive structure, formed through a hierarchy of MTs. Additionally, the paper presents classifications of vertical and horizontal causal interactions, uncovering theoretically grounded patterns of information exchange among Agile activities. These classifications support the derivation of quantitative indicators for assessing project complexity and progress at a given point in time, offering insights into activity specification completeness at hierarchical levels and overall project content completeness. Examples of complexity indicator calculations applied to real-world enterprise application system (EAS) projects are included. Finally, the paper describes enhancements to the Jira tool, including a causal Agile management repository and a prototype user interface. An experimental case study involving four Nordic EAS projects (using Scrum at the team level and SAFe at the program level) demonstrates that the Jira tool, when supplemented with causal analysis, can reveal missing links between themes and initiatives and align interdependencies between teams in real time. The causal Agile approach reduced the total number of requirements by an average of 13% and the number of change requests by 14%, indicating a significant improvement in project coordination and quality.

Previous studies have demonstrated that the rates of anxiety have been constantly increasing worldwide in recent years. To understand this phenomenon, based on the complemented cognitive model TVAPA of anxiety, the hybrid method of modeling and simulating the dynamics of anxiety in the population is proposed. The suggested method combines agent-based modeling, dynamic systems modeling with differential equations, and machine learning methods. The four-level STAI methodology is applied to assess anxiety in the proposed models. Sentiment analysis of social media content is used to identify the parameters of triggering stimuli flow. The proposed models were implemented and verified using open access data sets. Created models are characterized by simplicity, and the parameters used in them have a clear socio-informational meaning. The developed models can be calibrated by applying statistical methods according to indicators of anxiety measured at discrete sets of time intervals by associating them with parameters of the threat stimuli flow taken from statistical data and/or Internet content tracking data.

ОПЫТ ПРИМЕНЕНИЯ МЕТОДОВ МАТЕМАТИЧЕСКОЙ СТАТИСТИКИ ДЛЯ ОЦЕНКИ СОСТОЯНИЯ СЕЛЬСКОХОЗЯЙСТВЕННЫХ РАСТЕНИЙ В.М. БУРЕ, А.Ф. ПЕТРУШИН, Е.П. МИТРОФАНОВ, О.А. МИТРОФАНОВА, В. ДЕНИСОВ