TOWARDS A FUTURISTIC EDUCATIONAL DEVELOPMENT IN NIGERIA

Majority of the nations of the world were colonized and went through wars at various times, but had turned around their fortunes through economic and educational reforms. In Nigeria however, there had been series of educational reforms from: 7/6-5-4 to 6-3-3-4 and now 9-3-4 system. Similarly, the recent reform in the higher education sector which has resulted into the conversion of the nation’s higher technical educational institutions to degree awarding universities, which arose largely from the disparity between the graduates of the two institutions in the labor market and the fallen standard of education. This paper presents a critical review of the various educational reforms with a view to designing a viable educational model based on the examples of some developed and developing nations. It attempts to proffer solutions to some of the existing problems

An Improved Stock Price Prediction using Hybrid Market Indicators

In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction

PQ TREES, CONSECUTIVE ONES PROBLEM AND APPLICATIONS

A PQ tree is an advanced tree–based data structure, which represents a family of permutations on a set of elements. In this research article, we considered the significance of PQ trees and the Consecutive ones Problem to Computer Science and bioinformatics and their various applications. We also went further to demonstrate the operations of the characteristics of the Consecutive ones property by simulation, using high level programming languages. Attempt was also made at developing a PQ tree–Consecutive Ones analyzer, which could be instrumental not only as an educative tool to inquisitive students, but also serve as an important tool in developing clustering software in the field of bioinformatics and other application domains, with respect to solving real life problems

Climate models miss most of the coarse dust in the atmosphere.

Coarse mineral dust (diameter, ≥5 μm) is an important component of the Earth system that affects clouds, ocean ecosystems, and climate. Despite their significance, climate models consistently underestimate the amount of coarse dust in the atmosphere when compared to measurements. Here, we estimate the global load of coarse dust using a framework that leverages dozens of measurements of atmospheric dust size distributions. We find that the atmosphere contains 17 Tg of coarse dust, which is four times more than current climate models simulate. Our findings indicate that models deposit coarse dust out of the atmosphere too quickly. Accounting for this missing coarse dust adds a warming effect of 0.15 W·m-2 and increases the likelihood that dust net warms the climate system. We conclude that to properly represent the impact of dust on the Earth system, climate models must include an accurate treatment of coarse dust in the atmosphere

Stock Price Prediction using Neural Network with Hybridized Market Indicators

Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. The hybridized approach was tested with published stock data and the results obtained showed remarkable improvement over the use of only technical analysis variables. Also, the prediction from hybridized approach was found satisfactorily adequate as a guide for traders and investors in making qualitative decisions

Development of Electronic Government Procurement (e-GP) System for Nigeria Public Sector.

Business-to-business electronic procurement success in business organizations (private sectors) has been a major driving force for government organizations (public sectors) in developed nations to adopt electronic government procurement in order to reduce cost and improve administrative efficiency. E-procurement within the government is recognized to be the main area of government-to-business that needs to be exploited by government of developing nations. In this paper, we examined the drawbacks of existing procurement process in Nigeria with a view to offering an improved approach. A prototype e-GP system was designed and developed to eliminate the associated bottlenecks with existing system and showcase the attendant benefits of the proposed system which can lead to an improved procurement cycle process flow