K. Babu Joseph

A neural network tool for analyzing trends in rainfall

Rainfall, like all other natural phenomena is highly unpredictable. Traditionally, principal component analysis and spectral analysis are used to understand trends in rainfall over long periods. In this paper, we present a new method that appears to be better than existing methods to understand the long-term behavior of rainfall phenomena. Using a case study on the rainfall in Kerala State, the southern part of Indian Peninsula, we show that a new kind of neural network known as the adaptive basis function network is a promising tool for climatic studies, especially rainfall analysis. The paper also reveals that in spite of the fluctuations resulting from the nonlinearity in the system, the trends in the rainfall pattern in Kerala state have remained unaffected over the past 87 years from 1893 to 1980. We also successfully filter out the chaotic part of the system and illustrate that its effects are marginal over long-term predictions.

Generalized Chen-Wu type cosmological model

Recent measurements require modifications in conventional cosmology by way of introducing components other than ordinary matter into the total energy density in the universe. On the basis of some dimensional considerations in line with quantum cosmology, Chen and Wu [W. Chen and Y. Wu, Phys. Rev. D 41, 695 (1990)] have argued that an additional component, which corresponds to an effective cosmological constant

On the Integrability Properties of a Perturbed Nonlinear Schrödinger Field Equation

\Lambda

Temperature dependence of coupling constants

must vary as a^{-2} in the classical era. Their decaying-

Classical stochastic approach to cosmology revisited

\Lambda

A phenomenological bag model with variable bag pressure

model assumes inflation and yields a value for q_{0}, which is not compatible with observations. We generalize this model by arguing that the Chen-Wu ansatz is applicable to the total energy density of the universe and not to

Adaptive basis function for artificial neural networks

\Lambda

A low matter density decaying vacuum cosmology from a complex metric

alone. The resulting model, which has a coasting evolution (i.e.,

Transition to chaos in a driven pendulum with nonlinear dissipation

a \propto t

Wave propagation through a 2D lattice

), is devoid of the problems of horizon, flatness, monopole, cosmological constant, size, age and generation of density perturbations. However, to avoid serious contradictions with big bang nucleosynthesis, the model has to make the predictions