Volume 14, Nº 2 (2024)

The concept of a norm on the scale of an object's quality indicator in the context of determining the most preferable object or its state is explored. The notions of permissible and maximum permissible deviations from the norm are utilized, with maximum permissible deviations defining the boundaries of the indicator scale. The fundamental version of the norm is represented as a segment on the indicator scale that excludes these boundaries. Point and semi-interval norms (“no more”, “no less”) are considered special cases of the interval norm, where the semi-interval norm reflects the alignment of the original interval norm's boundary with the indicator scale's boundary. For certain indicators, deviation in one direction from the norm is not only acceptable but also beneficial. An indicator satisfying this condition is termed optimized, while an indicator with undesirable deviations from both ends of the norm is termed neutral. In the model of belonging to the norm, three classes are distinguished: “norm”, “no more” and “no less” than the norm. Piecewise linear and nonlinear membership functions for these classes are proposed. In the nonlinear model, the boundaries of the interval norm are expanded to acceptable limits, leading to the intersection of membership functions of adjacent classes. Classification into these three classes is conducted separately for neutral and optimized indicators. Interpreting deviations from the norm, both unacceptable and acceptable, necessitates introducing two fictitious classes: “worse than the norm” and “better than the norm.” These classes are formed from the “not less than” and “not more than” classes. To calculate the membership function for each class across all indicators, a weighted average function is employed. Aggregated indices of belonging to the classes “norm,” “better than norm,” and “worse than norm” across all indicators are referred to as indices of stability, development, and deterioration of an object, respectively. These indices are used for a comprehensive assessment of the object, termed an indicator of its condition. An example of analyzing deviations from the norm, implemented in the modified SVIR-M selection and ranking system, is provided.

The article offers an ontological conceptualization of the institutional design of social movements and outlines its functioning mechanism in modern society. The purpose of the article is to identify the specifics of the institutional design of contemporary social movements. Institutional design is described as an integrated unity of virtual and real practices, incorporating a technological component. The core of this design is the project activity of social movement participants, and the construction of the reality through their participation in social and political life in both online and offline spaces. An ecosystem of design is presented, which includes a structure of opportunities and conditions for the creation, functioning, and institutionalization of social movements. Within this ecosystem, participants interact with an institutional environment that contains rules and regulations, as well as opportunities for participation in socio-political life. In a digital society, social movement participants have tools to create new algorithms for constructing reality using mobile applications and social networks. The ecosystem of institutional design expands as social movements gain political opportunities and organize both offline and online interactions with government authorities to address social issues.

An integrated approach to solving the problems of managing the adaptive training process for groups of specialists is proposed, allowing for the consideration of changing external and internal factors, as well as the dynamics of changes in the specialists' training levels. This approach enables rapid adjustment of the training scenario to current situations. The implementation is based on ontological and predictive modeling of the adaptive training process. The article describes the meta-ontology for adaptive training of specialist groups in organizational and technical systems for automated training process management. It discusses an approach to solving problems of collecting, summarizing, and analyzing the cycle of intelligent management of adaptive training for specialist groups using meta-ontology. The developed meta-ontology enables automatic determination of trainees' knowledge and skills, stored in their profiles and updated based on completed training stages. This increases the efficiency of forming control actions (educational and training tasks) and improves the quality of training.

The features of developing mobile applications for users with intellectual disabilities are examined. A technology for developing mobile applications is proposed, utilizing a template multi-module architecture that allows for the selection of ready-made functional solutions from a module repository. A method for developing an adaptable mobile application interface is described, including the creation of screen templates with interface elements, categorizing template elements into mandatory and optional, and matching each element with sets of possible images. A web system is outlined that supports the stages of creating and operating mobile applications with an adaptable interface. Examples of developed mobile applications demonstrate their effectiveness for users with intellectual disabilities. A method is proposed for adapting the mobile application interface using a configuration panel, which supports the life cycle of mobile applications for this user category. An ontology was selected as a formal model for representing knowledge, enabling the extraction of knowledge for developing mobile applications with an adaptable interface and applying it to create applications accessible to people with intellectual disabilities.

Ontological models are extensively used in information support systems that offer resources and services for solving management, design, and scientific and technical problems. Specifically, domain ontologies are commonly utilized in decision support systems. In the ontological modeling of complex systems, there arises a need to automate the processes of handling ontological resources. This work discusses the main software systems and methodologies for ontological modeling, approaches to automating the processes of creating, populating, and using ontological models, and considers the temporal aspect of the ontological representation of objects. The aim of the work is to explore methods for automating the life cycle of ontological resources and to analyze the extent of their adaptation in applied ontologies. The work highlights a relatively high degree of automation in the process of populating ontologies and the use of large language models in this process. However, it points out the lack of methods for automating the conversion of information from tables and diagrams into ontological models, as well as for validating and processing the content of the model. Promising directions for automating work with ontological resources are also indicated.

The construction of an information repository for a clinical decision support system (CDSS) in the weakly formalized subject area of treating bronchopulmonary diseases is considered. An overview of approaches to creating knowledge bases in this subject area is provided. A method for extracting knowledge is described, which is based on rules from clinical recommendations and the search for dependencies between words in sentences, taking into account the sequence of rule application. The information repository of the CDSS is populated with ontological and production knowledge bases using the proposed knowledge extraction method. An ontology for the selected subject area was developed, and studies of its quality were conducted through an analysis of the graph topology using cognitive ergonomics metrics. The effectiveness of the described knowledge extraction method is demonstrated. An original architecture for a clinical decision support system has been developed.