This paper assesses the reliability of a Nokia N1650 mobile phone charger used in Zaria, Nigeria. The Part Stress Method was employed to assess the reliability of the system. Data on the failure rate of the system components were used, with special considerations given to factors like environment of use, quality of power supply and service personnel. A comparative assessment was made on the system, when operated within the Zaria environment and when operated within the country for which it was designed for (China).The result shows that a lower reliability level is associated with the use of the system in Zaria, Nigeria as compared with the reliability level when in use in the country for which it was designed for. The Mean-Time to Failure of the system which is the time it is expected to function without failure (MTTF) in Nigeria is 1 years as against 10 years in China. The ratio is 10:1 in favour of the designed country. The ratio of the failure rate of the system is also 10:1 in favour of the designed country, meaning it fails ten times faster in Zaria environment as compared to the country for which the system was designed for. These are accounted for by higher variation in the environmental factors such as temperature, poor power quality, and poor maintenance culture in the applied environment.

The paper assesses the reliability performance of the 4.2 MW, Single Shaft Typhoon Gas Fired Turbine Power Generation Station at Beta Glass Plc (Guinea Plant), Agbara, Ogun State, Nigeria. The monthly outage data of the station for the period of 68 months (May, 2003 to December, 2008) were collected and used to compute the yearly reliability indices of the generating unit. The unit availability was found to be low, especially in 2007 and 2008. The station recorded highest number of fuel gas supply failure throughout this period. The core engine failures were frequent and repairs were not promptly effected which made the unit to operate below its rated capacity. The high forced outages recorded are indications of unreliable performance. Appropriate recommendations were proposed.

This paper deals with data transfer from one computer to another. The serial ports of the computer are used. MAX 232 is used to convert RS 232 logic to TTL logic and then an optical transmitter circuit is used to transmit data via fiber optic cable. The optical transmitter circuit has an LED which is matched with the cable. At the receiver an optical receiver circuit is used which receives data using a photo diode and a MAX 232 again to convert TTL logic to RS 232 for the serial port at the receiving end computer. The desired baud rate can be set. Although the internet can be used, but due to its time consuming nature, one can implement data transfer using wireless medium, though at a relatively high cost. The need, therefore, is felt for fiber optic communication which is cheaper and more suitable for the task. It is cheaper than wireless medium and is prone to lesser loss as compared to wireless medium.

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This paper presents the application of neural network to the electrostatic field distribution modeling using harmattan season data in Zaria, Nigeria. The data was captured through an on-line mechanism for twenty-four months by the computer using the Microsoft Office Excel Program for twenty-four months (February, 2007-February, 2009). The focus of the analysis is determining the effect of environmental factors such as temperature, pressure and relative humidity on the static electric field during the harmattan season. The plots of the electrostatic field against the variation of the environmental factors were used as the qualitative analytical tools and yielded a non-linear relationship. The data was analyzed using Neural Network version 3.24 Software, to establish predictive models for Harmattan outside and inside Scenarios. The result of the analyses yielded good neural statistical values of Root Mean Square Error (RMSE) of 0.09, and Pearson R value of 0.76 for outside Scenario. Similarly for Harmattan inside Scenario, gives a RMSE value of 0.14, and Pearson R value of 0.77 respectively, which are reflections of a good model. The result was further buttressed by the plot of the Neural Network based Electrostatic Fields distribution modeling of the experimental and the predicted parameters. With the insignificant values of the RMSE, Pearson R value which are reflections of the closeness of the predicted and the experimental parameters, hence the could be relied upon to predict the electrostatic fields during harmattan in Zaria, Nigeria.