Volume X Issue XII

The research sought to analyze military housing provision and Soldiers' housing preferences at shadawanka barrack Bauchi, with the goal of meeting the military personnel's housing demands in the study region. The study collected data using a quantitative technique and survey strategy using a descriptive and exploratory research design and a questionnaire instrument. The study's population consisted of military personnel, with a sample frame of 248 dwellings, assuming one personnel per house. A sample size of 160 people was chosen. The questionnaire on a five-point Likert scale was self-administered. According to the findings, the factors with the greatest and fair conditions are security, wall condition, floor condition, and finishing, while the variables with the lowest and lowest condition rating are sewage and road networks within the barrack. The barrack's general or average level of housing conditions was rated fair. Water supply and security are the variables with the highest levels of adequacy, whereas drainage and road networks have the lowest levels of adequacy and reaction. The general degree of appropriateness of the barracks' residences was rated unsatisfactory. The quality of the floor, the walls, and the number of bedrooms have moderate levels of satisfaction, whereas solid waste disposal and sewage have low levels of satisfaction. The most common source of unhappiness is the insufficiency of housing provision, which has the highest standardized Beta coefficient of 0.285. The study recommended that the sewerage and solid waste disposal systems be improved. The service can be contracted out. Internal road networks and drainage systems within the barracks should be restored, and new ones created to give proper accessibility. There is a need for new dwellings to be built in order to relieve overcrowding in most homes. Because military personnel are dissatisfied with their housing owing to inadequacies, it is recommended that sufficient housing based on the demands of the soldiers be considered.

The inability of capital markets in Sub-Saharan Africa in fund mobilization has affected financial openness in the sub-continent. It has been vigorously argued that financial openness can still thrive without viable capital market but the intermediation function of capital market should not be overlooked. To this end, this study examined the nexus between capital market and financial openness in Sub-Saharan African Countries for 30 years period ranging from 1990 - 2019.The study proxied financial openness with capital account balance ratio, private capital inflow ratio, number of listed companies, external finance through foreign capital market and per capita income ratio while capital market development was measured with market capitalization ratio. The study employed secondary data collected from World Development Indicators, Securities and Exchange Commission statistical bulletin, and Stock Exchange fact books of the respective countries. The study adopted ex-post facto research design while the time series data were analyzed using descriptive statistics, correlation, unit root test, granger causality test, Johansen co-integration and error correction model via E-Views 10. The result revealed that there is no significant relationship between market capitalization ratio and capital account balance ratio in Nigeria; a significant negative relationship between capital account balance ratio and market capitalization ratio in Zimbabwe; a significant positive relationship between private capital inflow ratio and market capitalization ratio in South Africa and a significant positive relationship between private capital inflow ratio and market capitalization ratio in Nigeria. The study recommended that Sub-Saharan Countries should develop trade openness and liberalisation policy that would promote the international relationships necessary for increasing market opportunities and enhancing profitable investments. Therefore the country should continue to develop its capital market to achieve international standards and attract more investors.

This paper developed model equations that aid in decision making process of selecting the optimum gas transportation medium using regression analysis as the method.In apply regression analysis, data generated from the economic analysis conducted were exported to Excel and with the aid of Excel data analysis toolpak, model equations in the forms of simple linear equations were developed. The gas monetization technologies considered were only gas to liquid (GTL) and liquefied natural gas (LNG). In the study, series of sensitivity analyses were performed, with each factor that impacts on the profitability of the considered gas monetization technologies, one at a time and the various data points gotten from the sensitivity analyses were exported to Excel for development of a correlation/model equation using regression analysis. For gas to liquid (GTL) technology, the factors affecting its profitability (NPV) include: capital expenditure (CAPEX), gas feedstock price, naphtha price and oil price. Whereas, for liquefied natural gas (LNG) technology, the factors affecting its profitability include: gas feedstock price, shipping cost and LNG price. From the results of the regression analyses, it was discovered that: for each unit increase in CAPEX, the profitability of the GTL technology increases with 0.1 units; for each unit increase in gas feedstock price, the GTL profitability decreases with 3.8 x 10-13 units; for each unit increase in naphtha price, the GTL profitability increases with 8.01 x 10-15 units and for a unit increase in oil price, the GTL profitability increases with 6.1 x 10-4 units. And for a unit increase in shipping cost, the LNG profitability increases with 1000 units and for a unit increase in LNG price, the LNG profitability increases with 1000 units. Gas feedstock price has zero effect on the profitability (NPV) of LNG technology.

An application of traffic sign recognition is proposed on the basis of the convolution neural network (CNN).A CNN is an artificial neural network that is used to process and recognize the image that focuses on processing pixel data. A dataset is trained, tested, and saved in order for the application to be able to detect and classify the image considered from the dataset. A Graphical User Interface (GUI) is designed for the user to try and use the application which will load the image from the dataset and classify the image as per its requirement. In the German traffic sign recognition criterion, an accuracy of 98% is obtained from the model used. Traffic Sign Recognition plays an integral part in the intelligent transportation system and has driverless vehicles and assisted driving systems are some of the applications of it [1]. The self-driving cars needed to identify each and every detail that are present on the road that includes vehicles on the road as well as pedestrians walking on the sidewalk with extreme accuracy and precision. There were no challenging and publicly available datasets in the domain for a period of time but the situations had changed in the year 2011 when Stallkamp et al [2] and Larsson and Felsberg [3] introduced datasets that includes demonstrations for traffic sign detection and classification of it.

This paper proposes a Microwave Head Imaging Platform (MHIP) based on MIT App Inventor application. One of crucial element in the Microwave Imaging (MWI) is the imaging platform which used to hold and control the movement of antenna in certain angle. In the MHI platform, the key challenge is to improve the efficiency of angle control for brain stroke detection in the head phantom. Thus, the principal contribution of this work is the smart stepper motor control using MIT APP Inventor application for MHI platform which integrated with Arduino UNO module and Bluetooth module. The movement of platform based on two directions (clockwise and anticlockwise) with 10⁰ step of angle or 36 unique positions. Overall, the proposed system offers the smart data collection using smartphone which MIH as an alternative technique for brain stroke diagnostic.