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17.455  Articles
1 of 1.747 pages  |  10  records  |  more records»
The objective of this paper is to obtain an upper bound to the second Hankel determinant  for the function f and its inverse belonging to the class of pre-starlike functions of order alpha (0 = alpha = 1), using Toeplitz determinants.

Two upper bounds for ruin probability under the discrete time risk model for insurance controlled by two factors: proportional reinsurance and surplus investment are presented. The latter is of interest because of the assumption that insurers invest some ... see more

The objective of this paper is to obtain an upper bound to the second Hankel determinant  for the function f and its inverse belonging to the class of pre-starlike functions of order alpha (0 = alpha = 1), using Toeplitz determinants.

The methodology for determining the upper bounds on the homogenized linear elastic properties of cellular solids, described for the two-dimensional case in Dimitrovová and Faria (1999), is extended to three-dimensional open-cell foams. Besides the upper b... see more

In this paper, the authors introduce an upper bound of the longitudinal elastic modulus of unidirectional fibrous composites to strength of materials approach, provided that the fibre is much stiffer than the matrix. In the mathematical derivations result... see more

The objective of this paper is to obtain best possible upper bound to the third Hankel determinant for the pre-starlike functions of order a (0 = a < 1), using Toeplitz determinants.

This paper is a survey on the upper and lower bounds for the largest eigenvalue of the Laplacian matrix, known as the Laplacian spectral radius, of a graph. The bounds are given as functions of graph parameters like the number of vertices, the number of e... see more

The boundary between upper and lower seismogenic layers, below which large earthquakes tend to occur, is very important to estimate future seismic hazards. To estimate the depth of the seismogenic boundary, this study analyzes more than 38,000 earthquakes... see more

Reinforcement learning is a machine learning framework whereby an agent learns to perform a task by maximising its total reward received for selecting actions in each state. The policy mapping states to actions that the agent learns is either represented ... see more

1 of 1.747 pages  |  10  records  |  more records»