Homogeneous Isotropic Superfluid Turbulence in Two Dimensions: Inverse and Forward Cascades in the Hall-Vinen-Bekharevich-Khalatnikov model

We present the first direct-numerical-simulation study of the statistical properties of two-dimensional superfluid turbulence in the Hall-Vinen-Bekharevich-Khalatnikov two-fluid model. We show that both normal-fluid and superfluid energy spectra can exhibit two power-law regimes, the first associated with an inverse cascade of energy and the second with the forward cascade of enstrophy. We quantify the mutual-friction-induced alignment of normal and superfluid velocities by obtaining probability distribution functions of the angle between them and the ratio of their moduli. Our study leads to specific suggestions for experiments

Use of Modular Neural Network for Heart Disease

The medical field is very versatile field and one of the interested research areas for the scientist. It deals with many medical disease problems starting with the diagnosis of the disease, preventing from the disease and treatment for the disease. There are various types of medical disease and accordingly various types of treatment methods. In this paper we mostly concern about the diagnosis of the heart disease. Mainly two types of the diagnosis method are used one is manual and other is automatic diagnosis which consists of diagnosis of disease with the help of intelligent expert system. In this paper the modular neural network is used to diagnosis the heart disease. The attributes are divided and given to the two neural network models Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) for training and testing. The two integration techniques are used two integrate the results and provide the final training accuracy and testing accuracy. The modular neural network with probabilistic product method gave an accuracy of 87.02% over training data and 85.88% over testing accuracy and with probabilistic product method gave an accuracy of 89.72% over training data and 84.70% over testing accuracy, which was experimentally determined to be better than monolithic neural networks