Rain induced effect on propagated signal was studied in this work. The weather parameters of two (2) locations representing two (2) climate zones in Northern Nigeria were employed, using the International Telecommunications Union (ITU-R P.618) rain attenuation model, to investigate the rain effect on strength of propagated signal ranging from Ku-band to V-band of the communication spectrum. The weather data were obtained from the Nigerian Meteorological Agency (NIMET) for a period of 10years (2009-2018) and assimilated into the model using 1-minute integration time rain rate. An elevation angle of 42.5°, which is the conventional elevation angle of systems to NIGCOMSAT-1R over the Atlantic Ocean region, was used. The results showed that annual rainfall amounts trends varied slightly with the different locations and climate zones, having 2018 and 2010 as the highest rain years within the studied years at Maiduguri and Sokoto respectively. Also, the difference between maximum and minimum 0.01% attenuation for the two locations are 2dB-3dB, 3dB-6dB for horizontal polarization and 1dB-1.5dB and 2dB-4dB for vertical polarization respectively at the two locations respectively. Rain attenuation can be managed well for propagated Ku-band signal with sophisticated sensors but above Ku-band, signal strength attenuation induced by rain could be really alarming.
This study was conducted to evaluate some development factors in Southern Nigeria in order to ascertain common factors that explained the interrelationships among them and identify best cities for recommendation. A total sample of 250 cities from different states in three geopolitical zones in Southern Nigeria was used in this study and 11 development factors were considered. Kaiser-Meyer-Olkin (KMO) of (> 0.5) was computed to test the sampling adequacy; Bartlett’s Test of Sphericity (Significant at 0.001) was conducted to test whether the correlation between the variables are sufficiently large for factor analysis; correlation matrix was computed to confirm the inter-item correlation. In this analysis, principal component factor analysis was the factor extraction method. Varimax rotation technique was used for factor rotation. The result showed that three new factors with eigenvalues greater than 1 were successfully constructed. The three new factors accounted for 71.63% of total variance in the dataset and assigned as the common factors influencing sustainable development in Southern Nigeria. The communalities results ranging from 0.32-0.88 depicted that factor model was adequate. The results of factor analysis were extended to multiple regression analysis. The multiple regression model was fitted using development scores as dependent variable and rotated factors as independent variables. The coefficient of determination,, for the regression model was 99% and this shows that the model is adequate to evaluate the Southern Nigerian cities. The higher the estimated development scores, the better a city. Tolerance and VIF values showed that there was no multicollinearity in the regression model.
This paper examined the modeling of accident cases in four major roads leading to the main city of Enugu State of Nigeria using SARIMA Models. Among the most robust approaches for analysing time series data is the Autoregressive Integrated Moving Average (ARIMA) model propounded by Box and Jenkins (1979). In this paper, we employed the Box-Jenkins methodology to build SARIMA model for the accident cases for the period, January 2007 to December 2015 with a total of 108 data points. The model obtained in this paper was used to forecast monthly cases of accident in each of the roads for the upcoming year 2016. The forecasted results will help Government and Federal road safety commission to see how to maintain orderliness on the roads to reduce the case of road traffic crashes along the roads
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