Other Volumes


2007 2008 2010



Editorial Board

Prof. Metin Demiralp (Turkey),

Prof. A.Kuri-Morales (Mexico),

Prof. V.Mladenov (Bulgaria),

Prof. G.Bognar (Hyngary),

Prof. Lotfi A. Zadeh (USA),

Prof. Leonid Kazovsky (USA),

Prof. T.Kaczorek (Poland),

Prof. L.Chua (USA),

Prof. O.Martin (Romania),

Prof. C.Udriste (Romania),

Prof. N.Mastorakis (Greece),

Prof. D.Bertsekas (USA),

Prof. R.Yager (USA),

Prof. Anping Xu (China),  

Prof. M. A. Breuer (USA),

Prof. M.Wasfy (USA)



Topics

All aspects of classic and modern applied mathematics are covered including algebra, differential equations, probability, statistics, operational research, optimization, algorthms theory, computational complexity, control . It appears quarterly.  Special Issues are specially encouraged.

Format

Download Format... 

 

Contact us

Issue 1, Volume 3, 2009

An Empirical Evaluation of CAPM's validity in the British Stock Exchange

by Nikolaos Loukeris

Abstract: The CAPM under the means of the two step regression procedure indicated that the cross section of average excess security return is positively related to beta. Under a frame of Computational Econometrics the two step regression procedure is implemented into CAPM, concluding that the strict CAPM test rejects the second H0 hypothesis on the market risk premium, hence the slope of the Security Market Line (SML) is different from the slope of SML indicated by CAPM. Consequently the CAPM has not a statistical significance in Portfolio Selection.
Keywords:
Capital Asset Pricing Model, Two Step Regressions Procedure, Financial Management
Full Paper, pp. 1-8

 

Stability of AQM Algorithms in Low Congestion Scenarios

by Pawel Mrozowski, Andrzej Chydzinski

Abstract: It is well known that maintaining a stable queue size and high throughput in routers operating in high bandwidth-delay product networks is a difficult task. Fortunately, some newly proposed active queue management solutions (by Sun et al. and Ren et al.) seem to work quite well in such environments. In this paper we demonstrate that two additional factors make the task of achieving a stable queue size and high throughput very difficult. Namely, when the congestion level is low or the target queue size is short, none of the known AQMs performs reasonably well - the queue is very unstable and the throughput often goes far below fifty percent of the link capacity. Therefore, new AQM algorithms, able to work well in such scenarios, are needed.
Keywords:
Active queue management, Internet routers, packet queueing, performance evaluation
Full Paper, pp. 9-16

 

Applications of the Four Color Problem

by Marius-Constantin O. S. Popescu, Nikos E. Mastorakis

Abstract: In this paper are followed the necessary steps for the realisation of the map’s coloring, matter that stoud in the attention of many mathematicians for a long time. It is debated the matter of the four colors, but also the way of solving by implementing of an algorithm in the MAP-MAN application. Also, it is tackled the maps drawing in real time within GPS system satellites, using more colors depending on the covered route and the landforms met.
Keywords:
The issue of the fourth colors, The MAPMAN application, Software for GPS
Full Paper, pp. 17-26

 

Issue 3, Volume 3, 2009

A Binary Search Algorithm for a Special Case of Minimizing the Lateness on a Single Machine

by Nodari Vakhania

Abstract: We study the problem of scheduling jobs with release times and due-dates on a single machine with the objective to minimize the maximal job lateness. This problem is strongly NP-hard, however it is known to be polynomially solvable for the case when the processing times of some jobs are restricted to either p or 2p, for some integer p. We present a polynomial-time algorithm based on binary search when job processing times are less restricted; in particular, when they are mutually divisible. We first consider the case when the following condition holds: for any pair of jobs, if one is longer than another then the due-date of the former job is no larger than that of the latter one. We also study cases when a slight modification of our algorithm gives an optimal solution for the version without the restriction on job due-dates.
Keywords:
Algorithm, scheduling, single processor, release date, due-date, lateness
Full Paper, pp. 45-50

 

About the Diophantine Equations (x^5 + y^5) / (x+y)=5z^5 and (x^5 + y^5) / (x+y)=z^5, in Special Conditions

by Diana Savin

Abstract: In this paper we solve the Diophantine equations (x^5 + y^5)/(x+y)=5z^5 and (x^5 + y^5)/(x+y)=z^5, in special conditions
Keywords:
Cyclotomics fields, Diophantine equations
Full Paper, pp. 51-59

 

 

 

Issue 2, Volume 3, 2009

Optimal Stopping and Restarting Times for Multi Item Production Inventory Systems with Resource Constraints

by Zaid T. Balkhi

Abstract: Most of production inventory systems is interested in determining the optimal stopping and restarting times of producing certain commodity. In this paper, a multi-item production inventory model under resource constraints is considered. For any product, each of the production, the demand, and the deterioration rates in any cycle as well as all cost parameters are treated as known and arbitrary functions of time. Shortage for each product is allowed but it is partially backlogged. All cost components are affected by both inflation and time value of money. The existence of resource constraints implies the use of Linear Programming in order to determine the optimal production rates for each item. The objective is to find the optimal production and restarting times for each product in any cycle so that the overall total inventory cost for all products is minimized. A formulation of the problem is developed and rigorous optimization techniques are used to show the uniqueness and global optimality of the solution. An illustrative example which show the applicability of the theoretical results is provided.
Keywords:
Linear programming, Inventory control, Multiitem production, Varying Parameters, Optimality
Full Paper, pp. 27-34

 

Mathematical Model for Sounding Rockets, using Attitude and Rotation Angles

by Teodor-Viorel Chelaru, Cristian Barbu

Abstract: The paper purpose is to present some aspects regarding the calculus model and technical solutions for multistage sounding rockets used to test spatial equipment and scientific measurements. The calculus methodology consists in numerical simulation of sounding rocket evolution for different start conditions. The rocket model presented will be with six DOF and variable mass. At this item, as novelty of the work we will use simultaneously the rotation angles and the attitude angles for describing the kinematical equations of the movement. The results analyzed will be the flight parameters and the ballistic performances. The conclusions will focus technical possibilities to realize sounding multi-stage rocket recycling military rocket engines.
Keywords:
Multi-stage, Mathematic model, Sounding rocket, Simulation, Rotation angles
Full Paper, pp. 35-44

 

Issue 4, Volume 3, 2009

Parallel Finite Difference Methods for Phase Change Problems in Materials

by Chr. A. Sfyrakis

Abstract: The great complexity of the problems in phase change materials to us to develop from a fast and methods to solve with parallel programming techniques.
Keywords:
Finite difference methods, simplified phase-field models, Parabolic system, explicit Euler scheme, Crank-Nicolson-ADI method, Error estimates, parallel implementation
Full Paper, pp. 61-69

 

Classification of the Students' Scores based on some Artificial Neural Networks

by Hu Hongping, Bai Yanping

Abstract: In this paper, the data of students’ scores are analyzed by using the nonlinear BP neural network algorithm with a hidden layer, the probabilistic neural network algorithm, the perceptron algorithm and self-organizing compete neural network algorithm. We take 3000 students’ scores on only course to be analyzed and classified. Among these scores, 121 students’ scores are trained and 2879 students’ scores are tested by the probabilistic neural network algorithm, the nonlinear BP neural network algorithm and the perceptron neural network algorithm. By comparing these three kinds of neural network algorithms, we can get the following results: the train errors of these three neural network algorithms are all zero, but the test errors of these three neural network algorithms are different.and the test error of the probabilistic neural network algorithm is less than those of the nonlinear BP neural network algorithm and the perceptron algorithm; the train time of the BP neural network algorithm is longer than those of the probabilistic neural network algorithm and the perceptron algorithm;the test time of the probabilistic neural network algorithm is longer that those of the nonlinear BP neural network algorithm and the perceptron algorithm. The correct rate of the probabilistic neural network algorithm heads to 99.06% when net.spread. The correct rate of the BP neural network algorithm changes from 98.51% to 99.06%. But the correct rate of the perceptron neural network algorithm is too low and changes from 20% to 30%. Therefore by considering the correct rate and the whole time of classification, we obtain that the probabilistic neural network algorithm is more suitable for solving the classification of the students’ scores on only one course. And we take 1680 students' scores on five course to be analyzed and classified. Among these scored, 179 students' scores are trained and 1501 students’ scores are tested by the nonlinear BP neural network algorithm with the momentum factor, the nonlinear BP neural network algorithm with the gradient descent method, the probabilistic neural network algorithm and the self-organizing complete network algorithm. By comparing these kinds of neural network algorithms, we can get the following results: the train errors of the probabilistic neural network algorithm are all zero,those of the BP neural network algorithm with the momentum factor are all less than 0.0089, those of the BP neural network algorithm with the gradient descent method are all less than 0.0536, and those of the self-organizing compete neural network algorithm are all less than 0.4 and are all more than 0.2436; the test errors of the probabilistic neural network algorithm all equal to 0.0799, but those of the BP neural network algorithm with the momentum factor are all less than 0.0738, those of the BP neural network algorithm with the gradient descent method are all less than 0.1332, and those of the self-organizing compete neural network algorithm are all less than 0.3888 and are all more than 0.1871; the train times of the the probabilistic neural network algorithm are all less than 0.0469,those of the BP neural network algorithm with the momentum factor are all less than 33.0156 and are all more than 29.6875, those of the BP neural network algorithm with the gradient descent method are almost 24.3594 and are mostly less than 7.1875, and those of the self-organizing compete neural network algorithm are all less than 332.9219 and are all more than 310.0156; the test times of the probabilistic neural network algorithm are the least and are all less than 0.1719 and more than 1406, but those of the other neural network algorithms are all less than 0.0938; the train correct rates of the probabilistic neural network algorithm are all 100% when net.spread , those of the BP neural network algorithm with the momentum factor are all less than 99.44% and are all more than 97.77%, those of the BP neural network algorithm with the gradient descent method are all less than 93.30% and are all more than 86.59%, and the those of the self-organizing compete neural network algorithm are all less than 40%;the test correct rates of the probabilistic neural network algorithm are all 80.01%, those of the BP neural network algorithm with the momentum factor are all less than 87.67% and are all more than 81.55%, those of the BP neural network algorithm with the gradient descent method are all less than 82.41% and are all more than 66.69%, and those of the self-organizing compete neural network algorithm are all less than 53.23%.Therefore by considering the correct rates and the whole times of classification, we obtain that the probabilistic neural network algorithm and the BP neural network algorithm are more suitable for solving the classification of the students’ scores on five courses.
Keywords: The Students’ Scores, BP Neural Network, Probabilistic Neural Network, Perceptron Neural network, Self-organizing Compete Neural Network, Train error, Test error, Train time, Test time,Train Correct Rate, Test Correct Rate
A
Full Paper, pp. 70-78