The purpose of this article is to provide a methodology for calculating and predicting the quality of solution implementation in complicated multi-parametric organizational and technological challenges with control agent uncertainty. The article's study findings are centered on the practical application of formal methods in predicting the outcomes of control and decision-making risks under the uncertainty of model agents. The proposed mathematics and simulation applications use a multi-agent strategy to handle the general problem of assessing quality control based on "producer risk (project customer)" and "user risk." Computer experiments with simultaneous graphical visualization of the results improve the accuracy of mathematical modeling, increasing the study's effectiveness. Under the uncertainty of system agents, a simulation model has been designed to analyze and anticipate the dependability of control and the hazards of decision-making. The suggested model is unique in that it takes into account the statistical nature of normative values as well as the rules of equal probability. To handle a frequent problem, the proposed system technique employs a dual approach. It accomplishes this by assessing the quality of the control process based on the magnitude of the risks in the decision-making system.

The proliferation of digital public service platforms and the expansion of e-government initiatives have significantly increased the volume and diversity of citizen-generated feedback. This trend emphasizes the need for classification systems that are not only tailored to specific administrative domains but also robust to the linguistic, contextual, and structural variability inherent in user-submitted content. This study investigates the comparative effectiveness of large language models (LLMs) alongside instruction-tuned embedding models in the task of categorizing public transportation complaints. LLMs were tested using a few-shot inference, where classification is guided by a small set of in-context examples. Embedding models were assessed under three paradigms: label-only zero-shot classification, instruction-based classification, and supervised fine-tuning. Results indicate that fine-tuned embeddings can achieve or exceed the accuracy of LLMs, reaching up to 90 percent, while offering significant reductions in inference latency and computational overhead. E5 embeddings showed consistent generalization across unseen categories and input shifts, whereas BGE-M3 demonstrated measurable gains when adapted to task-specific distributions. Instruction-based classification produced lower accuracy for both models, highlighting the limitations of prompt conditioning in isolation. These findings position multilingual embedding models as a viable alternative to LLMs for classification at scale in data-intensive public sector environments.

The article considers the digital transformation of the aviation industry based on twins of maintenance and repair. As a result of digitization, a set of documents and application software are formed in a virtual reality environment. The concept of a digital twin is presented in the context of a model-based system design of the helicopter maintenance process according to the technical regulations. Based on statistical modeling technologies, a model for assembling aircraft units has been developed, in which time characteristics are qualitative estimates of learning processes and the effectiveness of digital twins. The results of experimental studies based on the method of analysis of students' certification using digital twins in the assembly of aircraft units are presented. It has been established that at the production site of an aircraft repair enterprise it is effective to apply training at the first stage using digital twins, and at the second stage using real objects. Based on analytical and experimental studies, regression models are proposed for the relationship between the optimal number of trainings at the second stage and the relative coefficient of training time for successful training and certification of mechanics and electronics engineers.

The aim of the article is to develop a formal methodology for quantitative assessment of the quality of аcontrol in a closed system with feedback in the context of digital transformation. In the proposed study, attention is focused on assessing the quality of management in organizational and technical systems on the example of the aviation industry. The following hypotheses were adopted in the study: in the digital management of business processes of an economic entity, the role of intellectual support is acquired by methods of formal description of processes: control, decision-making and corrective action on the control object. In critical situations, the psychotype of the person making the decision acquires a decisive role. The study solves two scientific and practical problems: development of a formal method for quantitative assessment of the quality of management of a complex multi-criteria organizational and technical system under the conditions of statistical uncertainty of management agents, taking into account feedback in the management of an object; formalization of the process of quantitative assessment of decision-making risks in the environment of statistical uncertainty of control agents and psychological factors of the decision maker.

Mortality prediction has a role to play in the development of a descriptive measure of the quality of care that provides a fair and equitable means of comparing and evaluating hospitals. This article describes a study of a medical text analysis algorithm for mortality prediction that used big data in the form of unstructured medical notes. The article describes the concept of using text mining technology for medical systems, a method for preprocessing medical data to predict patient mortality, an algorithm for predicting patient deaths based on the logistic regression classifier and presents a software module for implementing the proposed algorithm.

Introduction: The selection of functionalized implant coatings is a complex multi-criteria decision-making problem because biological, engineering, technological, and implementation-readiness indicators are heterogeneous and are frequently evaluated manually. This study proposes an automated and explainable computational decision-support framework for coating assessment and ranking. Methods: The framework integrates data preprocessing, indicator normalization, weighted scoring, implementation-readiness assessment, sensitivity analysis, explainable classification, and visualization. Biological effectiveness, engineering and operational reliability, and manufacturability and scalability were used as the three evaluation domains. Published experimental data from 2015 to 2025 on titanium oxide nanostructures, hydrogel coatings, hybrid nanocomposites, and bioactive surface modifications were used for validation. Results: The framework generated reproducible domain scores, integral performance scores, implementation classes, and explainable rankings. TiO₂-Sr, Grad-Ti, and TiO₂-NT achieved the highest scores because of their balanced biological performance, engineering reliability, sterilization compatibility, and technological scalability. The ranking remained stable under variations in weighting factors. Discussion: The proposed framework transforms heterogeneous biomedical data into a transparent and reproducible decision-support workflow. It can serve as the computational core of future biomedical material-selection platforms and support preliminary coating screening, technology-readiness evaluation, and data-driven engineering decisions.