Cochin University of Science and Technology

About: Cochin University of Science and Technology is a education organization based out in Kochi, Kerala, India. It is known for research contribution in the topics: Thin film & Natural rubber. The organization has 5382 authors who have published 7690 publications receiving 103827 citations. The organization is also known as: CUSAT & Cochin University.

Phase transition, quasinormal modes and Hawking radiation of Schwarzschild black hole in quintessence field

Abstract: Black hole thermodynamic stability can be determined by studying the nature of heat capacity of the system. For Schwarzschild black hole the heat capacity is negative, but in the quintessence field, this system shows a second-order phase transition, implying the existence of a stable phase. We further discuss the equation of state of the present system. While analyzing the quasinormal modes (QNM), we find that the massive scalar QNM frequencies in the complex ω plane shows a dramatic change when we plot it as a progressive function of quintessence state parameter. We also find the Hawking temperature of the system via the method of tunneling.

Masonry compressive strength prediction using artificial neural networks

Abstract: The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.