Survey Of The Necessity To Increase The Quality Of Life Of Patients With Diabetes Mellitus Through A Training Program

The relevance of current problem is determined by the high incidence of diabetes mellitus. The survey revealed the need to improve the quality of life of patients with diabetes mellitus. This is achieved through a training program.Diabetes mellitus is a disease characterized by elevated blood glucose levels in the human body resulting from weakening of insulin cells or insufficient production in the human body.            The purpose of the study is to reveal and outline the need to improve the quality of life of patients with diabetes mellitus through a training program. The training program provides information to learners about: the nature of the disease; improving their quality of life; recognition of the symptoms of hypo- and hyperglycemia; type of insulin, way of application, and place of application.The tasks of the survey are: to examine the need to improve the quality of life of patients with diabetes mellitus through a training program in the city of Sliven; to develop a training program for patients with diabetes mellitus above 18 years of age; to run the training program.The methods used are: survey, program interview, and purposeful observation.The subject of the study is: patients with the diagnosis diabetes mellitus, up to 18 years old in the town of Sliven.The object of the study is: the process and conditions in which the need to improve the quality of life of patients with diabetes mellitus through a training program is proved.The analysis of the results has led to the conclusion that there is a need to improve the quality of life of the patients with diabetes mellitus by conducting a training program. The content of the program is realized through theoretical and practical forms of training. The training ensures the quality of life of these patients

A State Space Search Algorithm and its Application to Learn the Short-Term Foreign Exchange Rates

Abstract We propose the use of a state space search algorithm of the discretetime recurrent neural network to learn the short-term foreign exchange rates. By searching in the neighborhood of the target trajectory in the state space, the algorithm performs nonlinear optimization learning process to provide the best feasible solution for the nonlinear least square problem. The convergence analysis shows that the convergence of the algorithm to the desired solution is guaranteed. The stability properties of the algorithm are also discussed. The empirical results show that our method is simple and effectively in learning the short-term foreign exchange rates and is applicable to other applications. Mathematics Subject Classification: 68T05, 68Q32, 91B2