We develop a biologically-plausible learning rule that provably converges to the class means of general mixture models. This rule generalizes the classical BCM neural rule within a tensor framework, substantially increasing the generality of the learning problem it solves. It achieves this by incorporating triplets of samples from the mixtures, which provides a novel information processing inte...

Phasepy is a scientifically defined open-source package in python for computational thermodynamics. indeed calculates the interfacial properties and fluid phases equilibrium using an equation of state. In addition, enables scientists to optimize relevant parameters multicomponent vapor-liquid, liquid-liquid, or vapor-liquid-liquid mixtures. The can model continuous approach (combining cubic sta...

background: general practitioners are the first who visit burn patients and they first calculate burn size and required fluid for the patient. error in calculation can be disastrous. the purpose of this study is to compare four methods of estimating burn size to teach students. methods: this is a descriptive-analytic study on 37 medical students. four methods of estimating burn percentage was t...

a second virial coefficient correlation suitable for two different temperatures ranges, (t*)-2£180 and (t*)-2>180, where t* is rt/dhvap is presented. the equation of state can be calculated for different, complicated non-polar, and slightly polar liquid mixtures at any temperature over the range from ttp to tc and up to high pressures. the latent heat of vaporization and density at the triple p...

| In this paper, a two{layer neural network is presented that organizes itself to perform blind source separation. The inputs to the network are prewhitened linear mixtures of unknown independent source signals. An unsu-pervised nonlinear hebbian learning rule is used for training the network. After convergence, the network is able to extract the source signals from the mixtures, provided that ...

The computation of two Bayesian predictive distributions which are discrete mixtures of incomplete beta functions is considered. The number of iterations can easily become large for these distributions and thus, the accuracy of the result can be questionable. Therefore, existing algorithms for that class of mixtures are improved by introducing round-off error calculation into the stopping rule....