Maja Atanasijević-Kunc

Modeling the influence of risk factors and chronic diseases on the development of strokes and peripheral arterial-vascular disease

Abstract Several chronic diseases and risky lifestyles have become an important social burden in many countries around the world, a problem which should not be underestimated. The situation is even more worrying when we take into account their influence on serious health complications that can threaten patients’ lives or significantly reduce their quality of life. The presented modeling-and-simulation study enables us to estimate the number of patients with type-2 diabetes, hypercholesterolemia and hypertension, obese and smoking people and the influence of these conditions on the development of strokes and peripheral arterial-vascular diseases. In addition, an estimate of treatment costs makes it possible to evaluate the social burden and provide information about the potential savings resulting from treating intensive chronic diseases.

MODELLING OF THE RISK FACTORS AND CHRONIC DISEASESTHAT INFLUENCE THE DEVELOPMENT OF SERIOUS HEALTHCOMPLICATIONS

BACKGROUND Some chronic diseases, like diabetes type 2 and hypertension, and risk factors, such asobesity, hypercholesterolemia, and smoking, are strongly correlated with the potentialdevelopment of serious health complications that can threaten a patient’s life or significantly influence the quality of life, while at the same time representing an enormous economic burden. Such complications include, for example, stroke, coronary heart disease,peripheral arterial vascular disease, end-stage renal disease and congestive heart failure. METHODS For a quantitative evaluation of the mentioned patient groups, the age distribution andan estimation of the treatment expenses a dynamic mathematical model was developed,where special attention was devoted to its structure, as it should enable the sequentialconstruction and representation of different forms of data information. The model wasrealized in the Matlab program package with the Simulink Toolbox.

ANALYSIS TOOLBOX STRESSING PARALLELISM OF SISO AND MIMO PROBLEMS

Abstract In the paper the toolbox for dynamical system analysis is presented and illustrated for the model of real multivariable plant. Its possibilities can be observed in several different ways. The functions realized in Matlab program package can treat similarly SISO and MIMO systems in several different design phases, so they are organized in four groups: in open and closed-loop analysis and in absolute and relative validation. The access to all options is enabled also through windows oriented graphical environment. Good experiences with toolbox usage confirmed correct orientation of the discussed software.

Toolbox Environment for Analysis and Design of Multivariable Systems

Abstract In the paper some ideas of studying multivariable control design are presented and illustrated through the usage of toolbox environment which should enable inclusion of already existing toolboxes, illustration of some interesting aspects explained by teacher and the possibility of developing, testing and presentation of solutions, prepared by students. Good experiences with graphically oriented toolbox environment usage confirm correct orientation of the discussed software.

Educational aspects of CAD supported real time control

Educational aspects of CAD supported real time control are discussed. The historical development of the equipment and appropriate software concerning laboratory exercises in the field of real-time control is briefly described. The hardware and software equipment used in the education process at Ljubljana University is briefly described. Emphasis is given to the equipment developed: a hydraulic-thermal pilot plant, two CACSD program packages with their real-time capabilities, and a special program for real time control in addition to emphasizing their connection in a powerful educational system. Some typical exercises from undergraduate courses, e.g., some experiments on the connection of analog and digital computer, identification of the hydraulic process and its level control, as well as some exercises from a graduate course, e.g., the implementation of adaptive digital filtering in real-time are described. Some difficulties encountered when applying the theory to practical problems are discussed. >

Efficacy analysis of a body-mass-reduction treatment using mathematical modelling

Obesity was categorized as a chronic disease by the World Health Organization (WHO) in 1997 because the problem had reached at pandemic level and presented a serious risk factor with respect to people who have been developing type-2 diabetes, hyperdyslipidaemia and hypertension. These four risk factors tend to accelerate the development of cardiovascular diseases. As a result, obesity has also become a significant social and economic burden. An important observation with regard to the population exhibiting increased body mass is the fact that they would like to decrease their weight, although they are frequently not successful. In this paper, a modelling-and-simulation approach is used for the presentation, analysis and explanation of the reasons that have an important influence on the treatment’s efficacy. The presented modelling results are based on the observation and treatment experiences of patients in a specialized institution over a period of 15 years, where each year around 100 new patients enter the treatment programme. The introduction of the motivation principle in the model structure allows strong support for the actions of the therapeutic team in a closed-loop manner, as the checking of different scenarios for the individual patient is easily realized. The efficacy of the treatment can, in this way, be significantly improved. In the past 6 years, which mainly involved open-loop treatments, the dropout rate was 70%. After the introduction of proposed closed-loop approach, this was reduced to 27% in the past year. As a consequence, the effect on body-mass control was significantly improved.

Balance Group Model with Smart Grid Elements

Abstract Rising demand for electrical power and environmental awareness has triggered new types of electrical power producers like solar and wind power plants. They cause a problem in electrical power system because of their stochastic nature of energy production. Smart grid technology offers a solution to these problems. In this paper a smart grid solution made for balance groups responsible party is presented. It includes model of balance group. concept of balance group internal market and scheduling algorithm developed by evolutionary algorithms method called Cell based Genetic Programming (CGP).

Knowledge Structure Modelling for Control System Design and Redesign

Abstract The paper represents an expert system for computer aided control system design (CACSD). Problems concerning CACSD and effective computer help are indicated which motivated design of the proposed expert system. Its structure allows for implementation of different design methods and provides facilities for documenting the derived control system solutions. The implementation model of the expert system is given in which the modules that require exact numerical computation are separated from the main expert system. The prototype of expert system was realised within G2 and Matlab environment. Ideas of its usage are illustrated on the case study.

Multipurpose modelling in the evaluation of laboratory pilot plant

Abstract In the paper some modelling and simulation results are presented which have been obtained dealing with a pressure-level laborator pilot plant. It has been built for control system investigations. The described model is therefore multipurpose. It gives the basis for analysis and synthesis procedures. At the same time it helps to indicate some problems concerning the realisation and suitability of the plant for education and research.

Scheduling of Electric Energy in Smart Grids Using a Combination of Neural Networks and Local Optimization

In this paper a new scheduling algorithm is presented that enables fast calculation times with a combination of neural network and local optimization. Properly learned neural network is used to calculate schedule results that can be used as initial conditions for local optimization method. If scheduling results from neural network are located relatively close to optimal solution, local optimization method converges very fast and additionally improve scheduling result. To calculate results from neural network that are relatively close to optimal solution a learning dataset has to be obtained that contains optimal solutions. For this purpose genetic algorithms were used with large number of generations and several repetitions of scheduling process. Scheduling algorithm was tested on scheduling of the electric energy flows in smart grids to balance the electric energy consumption and production. Simulation results showed that a combination of neural network and local optimization converge faster than genetic algorithm method. This makes method useful where times for calculation of schedules are short.