PREDICTION OF THE OCCURRENCE AND DEVELOPMENT OF FATAL VASCULAR COMPLICATIONS IN COVID-19 USING FUZZY MATHEMATICAL MODELS

Целью исследования является разработка метода прогнозирования возникновения и развития тромботических осложнений (тромботических прецедентов), провоцируемых действием новой коронавирусной инфекции (COVID-19) на организм человека, позволяющего усовершенствовать лечебно-диагностические мероприятия для пациентов с данной патологией. В качестве базового математического аппарата была выбрана методология синтеза гибридных нечетких решающих правил, хорошо зарекомендовавшая себя в процессе решения задач с нечётким описанием исследуемых классов со структурой данных аналогичной решаемой в работе задачи. В ходе проводимых исследований были синтезированы математические модели прогнозирования возникновения и развития тромботических прецедентов. Экспертное оценивание и математическое моделирование показали, что уверенность в правильном принятии решений по прогнозу появления и развития исследуемого класса тромботических осложнений превышает величину 0,9. В работе получены нечёткие математические модели прогнозирования возникновения и развития тромботических прецедентов у людей с подтверждённой коронавирусной инфекцией, для которой ведущим фактором риска является вторичный антифосфолипидный синдром с возникновением микроангиопатии. В ходе проведенных исследований была показана целесообразность использования полученных результатов в практике работы таких врачей, как иммунологи, инфекционисты, пульмонологи, кардиологи и сердечно-сосудистые хирурги The aim of the study is to develop a method for predicting the occurrence and development of thrombotic complications (thrombotic precedents) provoked by the action of a new coronavirus infection (COVID-19) on the human body, which allows improving therapeutic and diagnostic measures for patients with this pathology. The methodology of synthesis of hybrid fuzzy decision rules was chosen as the basic mathematical apparatus, which proved itself well in the process of solving problems with a fuzzy description of the classes under study with a data structure similar to the problem being solved in the work. In the course of the research, mathematical models for predicting the occurrence and development of thrombotic precedents were synthesized. Expert evaluation and mathematical modeling have shown that confidence in the correct decision-making on the prognosis of the occurrence and development of the studied class of thrombotic complications exceeds 0.9. The paper presents fuzzy mathematical models for predicting the occurrence and development of thrombotic precedents in people with confirmed coronavirus infection, for which the leading risk factor is secondary antiphospholipid syndrome with the occurrence of microangiopathy. In the course of the conducted studies, the expediency of using the results obtained in the practice of such doctors as immunologists, infectious disease specialists, pulmonologists, cardiologists and cardiovascular surgeons was shown

Fuzzy remodeling and synchronization of nonlinear systems

Synchronization is one of the crucial control problems in coordinated behavior systems. A wide range of applications necessitates the development and improvement of approaches to solving the linearization problem for non-linear systems with complex behavior. Hence the paper offers an approach to solving the problem of chaotic system coordinate synchronization based on the use of fuzzy remodeling and superstability conditions. It is shown that transition from general (non-linear) synchronization problem statement to fuzzy description with fuzzy Takagi-Sugeno models allows transforming the initial task to a well understood problem of fuzzy T-S system stabilization that describes the dynamics synchronization error. As a candidate solution for the problem we offer a way of implementing superstability conditions that reduce the task of finding fuzzy regulator to solving a linear programming task. It is shown that implementation of superstability conditions not only presents a simple way of solving the problem, but also has practical value. Superstability provides the monotonesness of the transient process of synchronization without sharp spikes of solution norm. The efficiency of the offered approach is demonstrated by the example of synchronization of two hyperchaotic systems. To prove the efficiency of superstability conditions the solution of the robust synchronization problem with parametric uncertainty in system matrices is given. The presented results can be applied for synchronization of various nonlinear systems demonstrating chaotic dynamics.

METHOD OF SYNTHESIS OF MODELS FOR FORECASTING AND EARLY DIAGNOSIS OF OCCUPATIONAL DISEASES OF LOCOMOTIVE CREW EMPLOYEES

Целью работы является разработка метода синтеза математических моделей прогнозирования и диагностики профессиональных заболеваний работников локомотивных бригад позволяющего получать решающие правила, обеспечивающие приемлемое для профилактической медицины качество принятия решений. С учетом неполного и нечёткого описания исследуемого класса заболеваний в качестве базового математического аппарата выбрана технология мягких вычислений и, в частности, методология синтеза гибридных нечетких решающих правил, хорошо зарекомендовавшая себя при решении задач с аналогичной структурой данных и типом неопределенности. Предлагаемый метод синтеза позволяет учитывать мультипликативный эффект воздействия на организм человека разнородных и нестабильных эндогенных и экзогенных факторов риска в кабинах локомотива. Предложенный метод синтеза математических моделей прогнозирования и диагностики заболеваний работников локомотивных бригад апробирован на синтезе математической модели прогнозирования заболеваний сердечно-сосудистой и нервной систем, с учетом воздействия на организм человека вибрации, шума, эргономики кабины, высокого уровня психоэмоционального напряжения и утомления, загазованности кабины и других экзогенных и эндогенных факторов риска. В ходе математического моделирования и экспертного оценивания было показано, что полученная прогностическая модель обеспечивает уверенность в правильном прогнозе не ниже 0,85, что является достаточно «хорошим» результатом для задач медицинской диагностики The aim of this work is to develop a method for synthesizing mathematical models for predicting and diagnosing occupational diseases of locomotive crew employees, which allows us to obtain decisive rules that ensure the quality of decision-making acceptable for preventive medicine. Taking into account the incomplete and fuzzy description of the studied class of diseases, the soft computing technology and, in particular, the methodology for the synthesis of hybrid fuzzy solving rules, which has proven itself well in solving problems with a similar data structure and type of uncertainty, was chosen as the basic mathematical apparatus. The proposed synthesis method allows us to take into account the multiplicative effect of heterogeneous and unstable endogenous and exogenous risk factors on the human body in the locomotive cabs. The proposed method of synthesis of mathematical models for predicting and diagnosing diseases of locomotive crew employees is tested on the synthesis of a mathematical model for predicting diseases of the cardiovascular and nervous systems, taking into account the impact on the human body of vibration, noise, cabin ergonomics, high levels of psycho-emotional stress and fatigue, cabin gas pollution and other exogenous and endogenous risk factors. In the course of mathematical modeling and expert evaluation, it was shown that the obtained predictive model provides confidence in the correct forecast of at least 0.85, which is a fairly "good" result for medical diagnostics tasks

Algorithms for Instance Retrieval and Realization in Fuzzy Ontologies

Fuzzy description logics, the formalism behind fuzzy ontologies, are an important mathematical method with applications in many artificial intelligence scenarios. This paper proposes the first specific algorithms to solve two reasoning tasks with respect to a fuzzy ontology: the instance retrieval and the realization problem. Our algorithms are based on a reduction of the number of optimization problems to solve by merging some of them. Our experimental evaluation shows that the novel algorithm to solve the instance retrieval outperforms the previous algorithm, and that in practice it is common to be able to solve a single optimization problem.

Specifying a New Requirement Model for Secure Adaptive Systems

Security is a growing concern in developing software systems. It is important to face unknown threats in order to make the system continue operating properly. Threats are vague and attack methods change frequently. Coping with such changes is a major feature of an adaptive software. Therefore, designing an adaptive secure software is an appropriate solution to address software security challenges. Through estimation of maximum amount of system assets security, one can determine whether the system is protecting the assets or not; if not, reconfiguration can be employed. This paper proposes a new requirement model for secure adaptive systems using fuzzy, goal modeling and Description Logic concepts. The model contains three phases of modeling security aspects of the system, identifying formalizations and relations between the requirements and monitoring and adapting, when needed. To illustrate the relations between the requirements, goal modeling is used in the first phase and fuzzy Description Logic in the second phase. For the third phase, four algorithms are proposed to monitor and determine whether reconfiguration is needed or not. Theorems are given to prove concept satisfaction of the requirements. Furthermore, examples and case studies are discussed to evaluate and show applicability of the proposed model.