Gravitational Collapse of a Homogeneous Scalar Field in Deformed Phase Space

We study the gravitational collapse of a homogeneous scalar field, minimally coupled to gravity, in the presence of a particular type of dynamical deformation between the canonical momenta of the scale factor and of the scalar field. In the absence of such a deformation, a class of solutions can be found in the literature [R. Goswami and P. S. Joshi, arXiv:gr-qc/0410144], %\cite{JG04}, whereby a curvature singularity occurs at the collapse end state, which can be either hidden behind a horizon or be visible to external observers. However, when the phase-space is deformed, as implemented herein this paper, we find that the singularity may be either removed or instead, attained faster. More precisely, for negative values of the deformation parameter, we identify the emergence of a negative pressure term, which slows down the collapse so that the singularity is replaced with a bounce. In this respect, the formation of a dynamical horizon can be avoided depending on the suitable choice of the boundary surface of the star. Whereas for positive values, the pressure that originates from the deformation effects assists the collapse toward the singularity formation. In this case, since the collapse speed is unbounded, the condition on the horizon formation is always satisfied and furthermore the dynamical horizon develops earlier than when the phase-space deformations are absent. These results are obtained by means of a thoroughly numerical discussion.Comment: 17 pages, 17 figure

Trip distribution modelling using neural network

Trip distribution is the second important stage in the 4-step travel demand forecasting. The purpose of the trip distribution forecasting is to estimates the trip linkages or interactions between traffic zones for trip makers. The problem of trip distribution is of non-linear nature and Neural Networks (NN) are well suited for addressing the non-linear problems. This fact supports the use of artificial neural networks for trip distribution problem. In this study a new approach based on the Generalised Regression Neural Network (GRNN) has been researched to estimate the distribution of the journey to work trips. The advantage of GRNN models among other feedforward or feedback neural network techniques is the simplicity and practicality of these models. As a case study the model was applied to the journey to work trips in City of Mandurah in WA. Keeping in view the gravity model, the GRNN model structure has been developed. The inputs for the GRNN model are kept same as that of the gravity model. Accordingly the inputs to the GRNN model is in the form of a vector consist of land use data for the origin and destination zones and the corresponding distance between the zones. The previous studies generally used trip generations and attractions as the inputs to the NN model while this study tried to estimate the trip distribution based on the land uses. For the purpose of comparison, gravity model was used as the traditional method of trip distribution. The modelling analysis indicated that the GRNN modelling could provide slightly better results than the Gravity model with higher correlation coefficient and less root mean square error and could be improved if the size of the training data set is increased

Initial Hypotheses for Modeling and Numerical Analysis of Rockfill and Earth Dams and Their Effects on the Results of the Analysis

© 2018 Mohammad Rashidi and Habib Rasouli. Since the behavior of earth dams is unreliable in different stages of construction, impounding, and exploitation, this matter is an unavoidable and essential issue with regard to the serious dangers caused by the failure of these important structures. It is crucial to evaluate the behavior of dams and examine the consistency between the carried out analyses and the behavioral parameters under different conditions in the lifespan of dams due to the uncertainty of the principles and hypotheses which have been adopted to analyze these structures. This objective will be accomplished through the help of correct numerical analyses. A series of hypotheses are adopted to simplify the parametric analyses before starting these analyses. The aim of this research is to develop and discuss these hypotheses. And so, the number of elements and their effects on the results of analyses were examined through the consolidation of unsaturated soil method, the compressible fluid method, correlated analysis, and uncorrelated analysis. It became clear after the numerical analyses that correlated analysis is a more precise method in comparison with the uncorrelated analysis method. However, this method is not economical when it comes to high dams and the replacement method is the uncorrelated analysis. Furthermore, the displacements are not that sensitive to the bulk modulus of water while the maximum settlement of the dam transfers from the middle of the dam's core to a location higher than that the core as the bulk modulus of water increases. However, pore water pressure is very sensitive to the bulk modulus of water

Extended anisotropic models in noncompact Kaluza-Klein theory

In this paper, new exact solutions for locally rotational symmetric (LRS) space-times are obtained within the modified Brans-Dicke theory (MBDT) (Rasouli et al 2014 Class. Quantum Grav. 31 115002). Specifically, extended five-dimensional (5D) versions of Kantowski-Sachs, LRS Bianchi type I and Bianchi type III are investigated in the context of the standard Brans-Dicke theory. We subsequently extract their corresponding dynamics on a 4D hypersurface. Our results are discussed regarding others obtained in the standard Brans-Dicke theory, induced-matter theory and general relativity. Moreover, we comment on the evolution of the scale factor of the extra spatial dimension, which is of interest in Kaluza-Klein frameworks.info:eu-repo/semantics/publishedVersio