This paper reviews a recursive Bayesian methodology for optimal data cleaning and filtering of economic time series data with the aim of using the Kalman filter to estimate the parameters of a specified state space model which describes an economic phenomena under study. The Kalman filter, being a recursive algorithm, is ideal for usage on time-dependent data. As an example, the yearly measurements of eight key economic time series data of the Nigerian economy is used to demonstrate that the integrated random walk model is suitable for modeling time series with no clear trend or seasonal variation. We find that the Kalman filter is both predictive and adaptive, as it looks forward with an estimate of the variance and mean of the time series one step into the future and it does not require stationarity of the time series data considered.
The paper presents an experiment on selecting and identifying an icon in desktop applications. The aim of this work is to get more specific guidelines for improvement in the design of a natural user interface. By conducting an indirect survey and handling different tasks to users, the issues such as what usual patterns for overviewing an icon in desktop applications are, how these patterns change when a user has to make a decision on what icons must be selected, how the pattern for selecting the icon varies and what changes in selection time take place when a task is executed multiple times in a row have been considered. To examine the influence of decision making on icon selection, icon classification into the positive and negative ones has been employed. As users had no guidelines what a positive/negative icon was, the ability to analyze if the colour of the icon and a related type of action influenced the perception of the icon has been developed. The carried out research draws the basic patterns for overviewing the icon in desktop applications and proves that the colour of the icon or the related type of action cannot be used as a single property to indicate icon perception by users.
Each Alexandroff space X has a corresponding shadow space [X] which is T0 Alexandroff space. In this paper, we study Alexandroff spaces and their properties via their shadow spaces. The definitions and the concepts such as Artinian, Noetherian, maximal points and minimal points, that are defined on T0 Alexandroff space carry over to any Alexandroff space. We prove that an Alexandroff space X is connected (compact) iff its shadow space [X] is connected (compact). Moreover, X need not be scattered or -scattered. We give a study of preopen, semi-open, and α-open sets on X.
A right A-module M is a -module provided that M is self-small and any exact sequence
0 → N → L → Q → 0,
with L, Q ∈ Stat(M) remains exact after applying the functor HomA(M, -) if and only if N ∈ Stat(M). A right A-module M is called a -module if it is self-small, (n + 1)-quasi-projective and n-Pres(M) = (n + 1)-Pres(M). In this work we generalize the concepts of -module and -modules to the concepts of -tuple and -tuple of Contravariant Functors between abelian categories.
In this paper, a mathematical model describing the dynamics of Chlamydia trachomatis infection in a human carrier is presented. The model incorporated relevant feature such as recovery through drug administration. The existence and uniqueness of solutions of the model were examined by actual solution. We conduct local and global stability analysis for the model. The results show that it is stable under certain conditions. The system of equations were solved analytically using parameter-expanding method coupled with direct integration. The results are presented graphically and discussed. It is discovered that the influence of burst size per infected cell, rate of cell infection and recovery rate due to drug administration is quite significant.
In this paper we present a hybrid optimization algorithm for solving constrained nonlinear optimization problems. The hybrid algorithm is a combination between one of the intelligence techniques (genetic algorithm) and chaos theory to enhance the performance and to reach the optimal solution. The proposed algorithm is operates in two phases: in the first one, genetic algorithm is implemented to solve nonlinear optimization problem. Then, in the second phase, local search referred to chaos theory is introduced to improve the solution quality and find the optimal solution. The results of numerical studies have been demonstrated the superiority of the proposed approach to finding the global optimal solution.