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                <text>This paper evaluates HVDS and LVDS concepts by system performance. This is achieved by examining system losses on HV and LV distribution networks in radial AC distribution systems. Challenges associated with system losses may demand network conversion from LV to HV network. This paper addresses this issue by using HVDS optimization specifically, linear programming techniques with Matlab optimization toolbox to determine the optimal number of unit transformers in the HV network. Studies conducted on a test distribution system using CYMEDIST software show the optimized HVDS has improved voltage profile and a total system loss reduction of 29.50% as compared to the LVDS. Consequently, there is reduced operational cost and increased annual capitalized loss savings in the optimized HVDS.</text>
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                <text>Minding the Gap: An assessment of the quality of course&#13;
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                <text>Deciding which university to attend and what course to study are essential choices for&#13;
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information about undergraduate programs available in African Universities. The study&#13;
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                <text>Mobile learning or m-learning represents anytime and anyplace delivery of learning through Internet and wireless enabled mobile electronic devices. Personal mobile computing devices (Smartphones, iPads, Tablet PCs etc.), are becoming increasingly ubiquitous in the daily activities of people, and in academia, it has been established that students spend more time using mobile tools for informal learning and collaborative activities than on formal school activities. It is within this context that educators generally think that delivering instruction seamlessly through these mobile devices should benefit students greatly, as learning can be personalized and also made readily accessible through systems that are in their direct control. Indeed, a growing number of initiatives are demonstrating ways in which m-learning can help confront existing educational challenges and pioneer new strategies for learning. M-learning is …</text>
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                <text>The study intended to investigate the sustainability of mobile money in Ghana, which is currently making waves in Ghana. The problem addressed in this research was how mobile money systems are sustained amidst challenges and setbacks to the system. The methods employed to solve the problem was a quantitative approach through the use of a descriptive research design, specifically cross-sectional correlational survey design. From the research results, the study revealed that respondents have knowledge on the sustainability of mobile money, and education is key to make the sustainability of mobile money in Ghana a reality.&#13;
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                <text> MSc. Management Information Systems</text>
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&#13;
&#13;
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                <text>Dr. Daniel M. O. Adjin</text>
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                <text>Bernhard, Schätz., Holger, Giese., Gabor Karsai., Lee, Edward .,  Bernhard, Rumpe.</text>
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                <text>SOFTWARE ENGINEERING (	Real-Time Software And Systems)</text>
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                <text>Modeling and Forecasting of Ghana’s Inflation Volatility</text>
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                <text>In this paper, we assessed volatility of Ghana’s inflation rates for 2000 to 2018 using the auto-regressive conditionally heteroskedasticity (ARCH), generalized ARCH (GARCH), and the exponential GARCH (EGARCH) models. The inflation data were obtained from the Ghana Statistical Service (GSS). The proposed model should be able to provide projections of inflation volatility from 2019 and beyond. The results showed that higher order models are required to properly explain Ghana’s inflation volatility and the EGARCH(12, 1) is the best fitting model for the data. The EGARCH(12, 1) model is robust to model and forecast volatility of inflation rates. Also, the results suggest that we are forecasting increasing volatility and there is increasing trend in general prices of goods and services for 2018 and beyond. The forecasts figures revealed that Ghana’s economy is likely to be unstable in 2018 and 2019. This study therefore recommends that policy makers and industry players need to put in place stringent monetary and fiscal policies that would put the anticipated increase in inflation under control. The models were implemented using R software.</text>
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                  <text>Faculty of Computing and Information Systems</text>
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                <text>Modeling Cholera Epidemiology Using Stochastic Differential Equations</text>
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                <text>Wahab A Iddrisu, Inusah Iddrisu, Abdul-Karim Iddrisu</text>
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                <text>In this study, we extend Codeço’s classical SI‐B epidemic and endemic model from a deterministic framework into a stochastic framework. Then, we formulated it as a stochastic differential equation for the number of infectious individuals I(t) under the role of the aquatic environment. We also proved that this stochastic differential equation (SDE) exists and is unique. The reproduction number, R0, was derived for the deterministic model, and qualitative features such as the positivity and invariant region of the solution, the two equilibrium points (disease‐free and endemic equilibrium), and stabilities were studied to ensure the biological meaningfulness of the model. Numerical simulations were also carried out for the stochastic differential equation (SDE) model by utilizing the Euler‐Maruyama numerical method. The method was used to simulate the sample path of the SI‐B stochastic differential equation for the …</text>
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                <text>Hindawi</text>
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                <text>https://scholar.google.com/citations?view_op=view_citation&amp;amp;hl=en&amp;amp;user=ECTxVnYAAAAJ&amp;amp;citation_for_view=ECTxVnYAAAAJ:kNdYIx-mwKoC</text>
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                  <text>Faculty of Computing and Information Systems</text>
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                <text>Modeling downtime severity of telecommunication networks using discrete time Markov chains</text>
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                <text>Wahab A Iddrisu, Ibrahim A Gedel</text>
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                <text>Telecommunication network reliability remains a top priority for both customers and service providers. Downtime can result in revenue loss for providers and productivity loss for customers. Thus, accurately predicting downtime severity can help providers plan and respond effectively. This study models telecommunication network downtime severity using discrete-time Markov chains (DTMC). The data used consists of 1,211 daily network downtime records, in minutes, recorded by Ghana's national communications authority (NCA) from August 1, 2015, to April 30, 2021. The severity of daily downtime was categorized into 5 categories based on duration. Results indicate that the majority (n= 905) of daily network downtime was negligible, while only 25 outages were severe. The transition probability matrix indicates that if the present network downtime severity is negligible, there is an 81% chance that the next network downtime severity may also be negligible, a 12% chance of minimal severity, a 4% chance of significant severity, a 2% chance of serious severity, and a 1% chance of severe severity. The steady-state distribution indicates that over the long term (n≥ 17), 74% of network downtime severity is expected to be negligible, while only 2% is expected to be severe. Based on probability simulations for 12 steps, it is evident that the'negligible'category is the most probable network downtime severity, regardless of the initial severity category. These findings can assist telecommunication providers in better planning and delivering more reliable services to their customers.</text>
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                <text>Accent Social and Welfare Society</text>
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