Rho-Omega Mixing and the Pion Form Factor in the Time-like Region

We determine the magnitude, phase, and $s$-dependence of $\rho$-$\omega$ ``mixing'' in the pion form factor in the time-like region through fits to e^+e^- \ra \pi^+ \pi^- data. The associated systematic errors in these quantities, arising from the functional form used to fit the $\rho$ resonance, are small. The systematic errors in the $\rho$ mass and width, however, are larger than previously estimated.Comment: 20 pages, REVTeX, epsfig, 2 ps figures, minor change

Introduction to Integral Discriminants

The simplest partition function, associated with homogeneous symmetric forms S of degree r in n variables, is integral discriminant J_{n|r}(S) = \int e^{-S(x_1 ... x_n)} dx_1 ... dx_n. Actually, S-dependence remains the same if e^{-S} in the integrand is substituted by arbitrary function f(S), i.e. integral discriminant is a characteristic of the form S itself, and not of the averaging procedure. The aim of the present paper is to calculate J_{n|r} in a number of non-Gaussian cases. Using Ward identities -- linear differential equations, satisfied by integral discriminants -- we calculate J_{2|3}, J_{2|4}, J_{2|5} and J_{3|3}. In all these examples, integral discriminant appears to be a generalized hypergeometric function. It depends on several SL(n) invariants of S, with essential singularities controlled by the ordinary algebraic discriminant of S.Comment: 36 pages, 19 figure

Existence of the σ\sigma-meson below 1 GeV and f0(1500)f_0(1500) glueball

On the basis of a simultaneous description of the isoscalar s-wave channel of the $\pi\pi$ scattering (from the threshold up to 1.9 GeV) and of the $\pi\pi\to K\bar{K}$ process (from the threshold to $\sim$ 1.4 GeV) in the model-independent approach, a confirmation of the $\sigma$-meson at $\sim$ 665 MeV and an indication for the glueball nature of the $f_0(1500)$ state are obtained. It is shown that the large $\pi\pi$-background, usually obtained, combines, in reality, the influence of the left-hand branch-point and the contribution of a very wide resonance at $\sim$ 665 MeV. The coupling constants of the observed states with the $\pi\pi$ and $K\bar{K}$ systems and lengths of the $\pi\pi$ and $K\bar{K}$ scattering are obtained.Comment: 13 pages, 3 figures, LaTex; submitted to Physics Letters

Diagnosis and management of cardiovascular disease with an intelligent decision-making support system

Cardiovascular disease is the principal cause of death in most European countries and may have a major negativeimpact on the patients' functional status, productivity, and quality of life. It seems an automatic decision support system couldlower these negative impacts. The current development stage of a patient-centric solution for remote management andtreatment of cardiovascular patients is described from the point of view of decision support. The principle of the DecisionmakingSupport System is presented. Our prototype experimental results with Data Mining Models are also provided

Alert rules for remote monitoring of cardiovascular patients

Cardiovascular disease is the leading cause of death in most European countries and its prevention requires major life-style changes using limited health-care resources. Remote cardiovascular decision support seems to allow cardiovascular patients to lead a productive life and to minimize the costs of treatment. In this paper, the current development stage of remote monitoring in our developing decision support system is described. It uses alert rules that can notify clinicians or other parts of the system if a patient is at risk, which is useful for prevention of malignant events. A mathematical definition of alert rules and their combination into one output, their software implementation and example data are given

Data mining applied to cardiovascular data

Medical decision support is one area of increasing research interest. Ongoing collaborations between cardiovascular clinicians and computer scientists are looking at the application of data mining techniques to the area of individual patient diagnosis, based on clinical records. An investigation of four different classification models on cardiovascular data for estimation of patient risk in cardiovascular domains is presented. Experimental results are provided showing the performance of particular models

Prediction of mortality rates in heart failure patients with data mining methods

Heart failure is one of the severe diseases which menace the human health and affect millions of people. Half of all patients diagnosed with heart failure die within four years. For the purpose of avoiding life-threatening situations and minimizing the costs, it is important to predict mortality rates of heart failure patients. As part of a HEIF-5 project, a data mining study was conducted aiming specifically at extracting new knowledge from a group of patients suffering from heart failure and using it for prediction of mortality rates. The methodology of knowledge discovery in databases is analyzed within the framework of home telemonitoring. Several data mining methods such as a Bayesian network method, a decision tree method, a neural network method and a nearest neighbour method are employed. The accuracy for the data mining methods from the point of view of avoiding life-threatening situations and minimizing the costs is discussed. It seems that the decision tree method achieves the best accuracy results and is also interpretable for the clinicians