مجله ایرانی علوم و تکنولوژی-الف
سال:1389 | دوره:34 | شماره:4-الف |تعداد مقالات:9

A k-nacci sequence in a finite group is a sequence of group elements x0, x1, x2,…, xn,… for which, given an initial (seed) set x0, x1, x2,…, xj-1,each element is defined by   xn={ x0x1…xn-1                       for j£n<k,         xn-kxn-k=1…xn-1              for n³k.   In this paper, we examine the periods of the k-nacci sequences in Miller’s generalization of the polyhedral groups á2,2½2;qñ,án,2½2;qñ,á2,n½2;qñ,á2,2½2;qñ, for any n>2.

In this paper, we find the number of sides of circuits in suborbital graph for the normalizer of G0(m) in PSL (2,R), where m will be of the form 2p2, p is a prime and p º1 (mod 4). In addition, we give a number theoretical result which says that the prime divisors p of 2u2 ± 2u +1 are of the form p º1 (mod 4).

In this paper, we study L1 -solutions of the following two-direction poly-scale refinement equation   f(x) = SM-1m=1 S Nn=-N [c1m,nf(lmx-n)+ c-1m,nf(-lmx-n)]   We prove that the vector space of all L1 -solutions of the above equation is at most one-dimensional and consists of compactly supported functions of constant sign. We also show that any- L1 solution of the above equation is either positive or negative on its support under a special assumption. With regard to the L1 -solutions of the equation, some simple sufficient conditions for the existence of nontrivial L1 -solutions and the nonexistence of such solutions are given.

In this study, by using the notion of (V, l) -summability, we introduce and study the concepts of l- strongly summable and l-statistiacally convergent functions.

In this paper, we investigate the Goursat problem in the class C2+1 (D) Ç Cn+0 (D È P) Ç C0+0 (D È Q) for a third order pseudoparabolic equation. Some results are given concerning the existence and uniqueness for the solution of the suggested problem.

We develop a two phase sampling procedure to determine the sample size necessary to estimate the population mean of a normally distributed random variable and show that the resulting estimator has preassigned variance and is unbiased under a regular condition. We present a necessary and sufficient condition under which the final sample mean is an unbiased estimator for the population mean.