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In this study, we obtain scalar potential in higher dimensional FRW model via considering exponential acceleration. Also, we classify dark energy models for higher dimensional FRW model in accordance with state parameters (r, s) by considering exponential acceleration. In this respect, we obtain cosmological constant for r = 1and s = 0 as dark energy candidate. We obtain Phantom energy for r 0 dark energy candidate.

In Newtonian gravity, the tidal deformability of an astronomical body is measured by its tidal Love numbers, dimensionless coupling constants which depend on the body's composition. The gravitational Love numbers characterize the body's response to the tidal field through the change in its gravitational potential; the surficial Love numbers do likewise through the deformation of its surface. The gravitational Love numbers were promoted to a relativistic setting by Damour and Nagar, and Binnington and Poisson. We present an improved computational procedure for calculating them, and place bounds on the gravitational Love numbers of perfect fluid bodies. We also provide a covariant definition of relativistic surficial Love numbers, develop a unified theory of surface deformations for material bodies and black holes, and derive a simple relation between the gravitational and surficial Love numbers in general relativity. Additionally, we formulate a theory of Newtonian tides in higher dimensions.

A five-dimensional Bianchi type-I space-time is considered in the presence of a perfect fluid source in Barber's (Gen. Relat. Gravit. 14, 117, 1982) second self-creation theory of gravitation. The model is presented using a relation between the metric potentials and an equation of state. Some physical and kinematical properties of the model are discussed., Razmatramo Bianchijev prostor-vrijeme tipa I u pet dimenzija, uz prisustvo perfektne tekućine, u drugoj Barberovoj samotvornoj teoriji gravitacije (Gen. Relat. Gravit. 14, 117, 1982). U modelu pretpostavljamo relaciju među metričkim potencijalima i jednadžbu stanja. Raspravljamo neke fizičke i kinematičke odlike modela.

In many fields, we come across problems where we want to optimize several conflicting objectives simultaneously. To find a good solution for such multi-objective optimization problems, an approximation of the Pareto set is often generated. In this paper, we con- sider the approximation of Pareto sets for problems with three or more convex objectives and with convex constraints. For these problems, sandwich algorithms can be used to de- termine an inner and outer approximation between which the Pareto set is 'sandwiched'. Using these two approximations, we can calculate an upper bound on the approximation error. This upper bound can be used to determine which parts of the approximations must be improved and to provide a quality guarantee to the decision maker. In this paper, we extend higher dimensional sandwich algorithms in three different ways. Firstly, we introduce the new concept of adding dummy points to the inner approx- imation of a Pareto set. By using these dummy points, we can determine accurate inner and outer approximations more e±ciently, i.e., using less time-consuming optimizations. Secondly, we introduce a new method for the calculation of an error measure which is easy to interpret. The combination of easy calculation and easy interpretation makes this measure very suitable for sandwich algorithms. Thirdly, we show how transforming cer- tain objective functions can improve the results of sandwich algorithms and extend their applicability to certain non-convex problems. The calculation of the introduced error measure when using transformations will also be discussed. To show the effect of these enhancements, we make a numerical comparison using four test cases, including a four-dimensional case from the field of intensity-modulated radiation therapy (IMRT). The results of the different cases show that we can indeed achieve an accurate approximation using significantly fewer optimizations by using the enhancements.

In many fields, we come across problems where we want to optimize several conflicting objectives simultaneously. To find a good solution for such multi-objective optimization problems, an approximation of the Pareto set is often generated. In this paper, we con- sider the approximation of Pareto sets for problems with three or more convex objectives and with convex constraints. For these problems, sandwich algorithms can be used to de- termine an inner and outer approximation between which the Pareto set is 'sandwiched'. Using these two approximations, we can calculate an upper bound on the approximation error. This upper bound can be used to determine which parts of the approximations must be improved and to provide a quality guarantee to the decision maker. In this paper, we extend higher dimensional sandwich algorithms in three different ways. Firstly, we introduce the new concept of adding dummy points to the inner approx- imation of a Pareto set. By using these dummy points, we can determine accurate inner and outer approximations more e±ciently, i.e., using less time-consuming optimizations. Secondly, we introduce a new method for the calculation of an error measure which is easy to interpret. The combination of easy calculation and easy interpretation makes this measure very suitable for sandwich algorithms. Thirdly, we show how transforming cer- tain objective functions can improve the results of sandwich algorithms and extend their applicability to certain non-convex problems. The calculation of the introduced error measure when using transformations will also be discussed. To show the effect of these enhancements, we make a numerical comparison using four test cases, including a four-dimensional case from the field of intensity-modulated radiation therapy (IMRT). The results of the different cases show that we can indeed achieve an accurate approximation using significantly fewer optimizations by using the enhancements.

In many fields, we come across problems where we want to optimize several conflicting objectives simultaneously. To find a good solution for such multi-objective optimization problems, an approximation of the Pareto set is often generated. In this paper, we con- sider the approximation of Pareto sets for problems with three or more convex objectives and with convex constraints. For these problems, sandwich algorithms can be used to de- termine an inner and outer approximation between which the Pareto set is 'sandwiched'. Using these two approximations, we can calculate an upper bound on the approximation error. This upper bound can be used to determine which parts of the approximations must be improved and to provide a quality guarantee to the decision maker. In this paper, we extend higher dimensional sandwich algorithms in three different ways. Firstly, we introduce the new concept of adding dummy points to the inner approx- imation of a Pareto set. By using these dummy points, we can determine accurate inner and outer approximations more e±ciently, i.e., using less time-consuming optimizations. Secondly, we introduce a new method for the calculation of an error measure which is easy to interpret. The combination of easy calculation and easy interpretation makes this measure very suitable for sandwich algorithms. Thirdly, we show how transforming cer- tain objective functions can improve the results of sandwich algorithms and extend their applicability to certain non-convex problems. The calculation of the introduced error measure when using transformations will also be discussed. To show the effect of these enhancements, we make a numerical comparison using four test cases, including a four-dimensional case from the field of intensity-modulated radiation therapy (IMRT). The results of the different cases show that we can indeed achieve an accurate approximation using significantly fewer optimizations by using the enhancements.

A random map is a discrete time dynamical system in which one of a number of transformations is selected randomly and implemented. Random maps have been used recently to model interference effects in quantum physics. The main results of this paper deal with the Lyapunov exponents for higher dimensional random maps, where the individual maps are Jabloński maps on the n-dimensional cube.