Power law scaling is observed in many physical, biological and socio-economical complex systems and is now considered an important property of these systems. In general, power law exists in the central part of the distribution. It has deviations from power law for very small and very large variable sizes. Tsallis, through non-extensive thermodynamics, explained power law distribution in many cases including deviation from the power law. In case of very large steps, they used the heuristic crossover approach. In the present work, we present an alternative model in which we consider that the entropy factor q decreases with variable size due to the softening of long range interactions or memory. We apply this model for distribution of citation index of scientists and examination scores and are able to explain the distribution for entire variable range. In the present model, we can have very sharp cut-off without interfering with power law in its central part as observed in many cases.
