Remarks on Characterizations of Malinowska and Szynal

The problem of characterizing a distribution is an important problem which has recently attracted the attention of many researchers. Thus, various characterizations have been established in many different directions. An investigator will be vitally interested to know if their model fits the requirements of a particular distribution. To this end, one will depend on the characterizations of this distribution which provide conditions under which the underlying distribution is indeed that particular distribution. In this work, several characterizations of Malinowska and Szynal (2008) for certain general classes of distributions are revisited and simpler proofs of them are presented. These characterizations are not based on conditional expectation of the kth lower record values (as in Malinowska and Szynal), they are based on: (i) simple truncated moments of the random variable, (ii) hazard function

A new 3D-beam finite element including non-uniform torsion with the secondary torsion moment deformation effect

In this paper, a new 3D Timoshenko linear-elastic beam finite element including warping torsion will be presented which is suitable for analysis of spatial structures consisting of constant open and hollow structural section (HSS) beams. The analogy between the 2ndorder beam theory (with axial tension) and torsion (including warping) was used for the formulation of the equations for non-uniform torsion. The secondary torsional moment deformation effect and the shear force effect are included into the local beam finite element stiffness matrix. The warping part of the first derivative of the twist angle was considered as an additional degree of freedom at the finite element nodes. This degree of freedom represents a part of the twist angle curvature caused by the bimoment. Results of the numerical experiments are discussed, compared and evaluated. The importance of the inclusion of warping in stress-deformation analyses of closed-section beams is demostrated

Lower bounds for polynomials using geometric programming

We make use of a result of Hurwitz and Reznick, and a consequence of this result due to Fidalgo and Kovacec, to determine a new sufficient condition for a polynomial $f\in\mathbb{R}[X_1,...,X_n]$ of even degree to be a sum of squares. This result generalizes a result of Lasserre and a result of Fidalgo and Kovacec, and it also generalizes the improvements of these results given in [6]. We apply this result to obtain a new lower bound $f_{gp}$ for $f$, and we explain how $f_{gp}$ can be computed using geometric programming. The lower bound $f_{gp}$ is generally not as good as the lower bound $f_{sos}$ introduced by Lasserre and Parrilo and Sturmfels, which is computed using semidefinite programming, but a run time comparison shows that, in practice, the computation of $f_{gp}$ is much faster. The computation is simplest when the highest degree term of $f$ has the form $\sum_{i=1}^n a_iX_i^{2d}$, $a_i>0$, $i=1,...,n$. The lower bounds for $f$ established in [6] are obtained by evaluating the objective function of the geometric program at the appropriate feasible points