Decision Support System Based on High Level Architecture

The paper describes the class of operational decision-making problems on base of distributed innovative knowledge. The purpose of research is to develop a method of constructing a dynamic subject domain. Unacceptability of use of static models of subject domain is proved. The possibility to automate the process of subject domain construction for this class of problems has been investigated. The model of a dynamic scene and methodology of its construction are proposed. The described methodology is based on the concepts of High Level Architecture (HLA) standard for distributed simulation systems and has been implemented by means of HLA Development Kit Framework

Принцип общности свойств и KD-классификация

The paper examines the actual problem of automatic detection of hidden interpretable patterns in intelligent systems. The conceptual basis of the process of learning from examples is determined by the methods of class description and separation. Three basic principles are known: enumeration of class members, generality of properties and clustering. We propose an original method for implementing the principle of generality of properties based on the search for combinations of features that provide class distinction. The eff ectiveness of the approach is confi rmed by the results of numerical experiment

Построение интеллектуальных систем на основе Knowledge Discovery in Datasets

The original method of intelligent systems construction based on technology of knowledge discovery in databases is considered. To form the knowledge base of an intelligent system, it is proposed to abandon the classical approach based on the formalization of expert knowledge in favor of an alternative approach aimed at identifying interpretable empirical patterns using Data Mining methods

Synthesis of Automatic Recognition Systems Based on Properties Commonality

The paper explores an actual applied problem related to the synthesis of automatic recognition systems. The conceptual base of synthesis is determined by the methods of describing and separating classes. Three basic principles are known: enumeration of class members, commonality of properties, and clustering. The report proposes an original method for implementing the principle of commonality of properties, based on the search for combinations of features that provide classes distinguishing. The efficiency of the approach is confirmed by the results of a numerical experiment

Pattern Recognition Based on Classes Distinctive Features

In pattern recognition, the approach where Supervised Learning is reduced to the construction of decision rules is considered to be classical. These rules should ensure an extremum of some criterion. The paper proposes an alternative solution based on the search for combinations of features that ensure classes separation. The results of a numerical experiment on model data confirm the effectiveness of the proposed approach

Специализированный KD-агент для экосистем знаний

One of the base elements of any knowledge ecosystem is a software agent. The agent receives data about the internal events of the ecosystem, interprets data and executes commands that affect the environment. The paper proposes an option for the implementation of the specialized Knowledge Discovery agent (KD-agent). The input for the agent is the a priori dictionary of features and the training set. As the outcome of the agent activity previously unknown patterns are revealed and can be interpreted within the subject domain. The effectiveness of the proposed approach is demonstrated on the example of model data analysis. Одним из базовых элементов любой экосистемы знаний является программный агент. Находясь в среде экосистемы, агент получает данные о внутренних событиях, интерпретирует их и выполняет команды, которые воздействуют затем на среду. В статье предлагается вариант реализации специализированного knowledge discovery агента (KD-агента). Входными данными для агента являются априорный словарь признаков и обучающая выборка. В результате работы агента выявляются ранее неизвестные закономерности, которые могут быть проинтерпретированы экспертами-специалистами соответствующей предметной области. Эффективность предложенного подхода демонстрируется на примере анализа модельных данных

Immunomodulatory contribution of mast cells to the regenerative biomaterial microenvironment.

Bioactive immunomodulatory biomaterials have shown promise for influencing the immune response to promote tissue repair and regeneration. Macrophages and T cells have been associated with this response; however, other immune cell types have been traditionally overlooked. In this study, we investigated the role of mast cells in the regulation of the immune response to decellularized biomaterial scaffolds using a subcutaneous implant model. In mast cell-deficient mice, there was dysregulation of the expected M1 to M2 macrophage transition typically induced by the biomaterial scaffold. Polarization progression deviated in a sex-specific manner with an early transition to an M2 profile in female mice, while the male response was unable to properly transition past a pro-inflammatory M1 state. Both were reversed with adoptive mast cell transfer. Further investigation of the later-stage immune response in male mice determined a greater sustained pro-inflammatory gene expression profile, including the IL-1 cytokine family, IL-6, alarmins, and chemokines. These results highlight mast cells as another important cell type that influences the immune response to pro-regenerative biomaterials