In this paper, we first recall the definition of a family of Koenig’s root-

finding algorithms known as Koenig’s algorithms (𝐾௣,௡) for polynomials. In the whole

paper p has degree 𝑑 ≥ 2 with real coefficients and real (and simple) zeros 𝑥௞ , 1 ≤

𝑘 ≤ 𝑑 .

Now we want to discuss Koenig’s algorithms in details where

𝑛 = 4, (𝐾௉,ସ(𝑧)).

 Abstract. We investigate harmonic Bergman spaces bp = bp(Ω), 0 < p < ∞, where Ω = Rn \Zn and prove that bq ⊂ bp for n/(k + 1) ≤ q < p < n/k. In the planar case we prove that bp is non empty for all 0 < p < ∞. Further, for each 0 < p < ∞ there is a non-trivial f ∈ bp tending to zero at infinity at any prescribed rate.

Abstract. We prove that ωu(δ) ≤ Cωf(δ), where u: Ω → Rn is the harmonic extension of a continuous map f: ∂Ω → Rn, if u is a K-quasiregular map and Ω is bounded in Rn with C2 boundary. Here C is a constant depending only on n, ωf and K and ωh denotes the modulus of continuity of h. We also prove a version of this result for Λω-extension domains with c-uniformly perfect boundary and quasiconformal mappings.

In this paper we want to show that each immediate basin of fixed points of Newton’s

function for a real polynomial is simply connected.

Keywords: Newton’s function, Immediate basin, Simply connected.

The concepts of partial size-and-shape and partial shape are defined, with motivation from a study in humanmovementanalysis. Someco-ordinates for partial shape for landmarks in three dimensions are given, and Gaussian models for the landmark co-ordinates are proposed. The main results involve the derivation of the partial size-and-shape distributions for the isotropic and general multivariate normal models for three-dimensional data. The partial shape distribution is given in the isotropic case. Maximum likelihood based inference is explored, and examples using simulated and real human movement data illustrate the methodology.

We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three-dimensional Cartesian co-ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing-the Bayesian methodology is easily adapted to cope with this situation.