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                <text>Modeling the Causes of Power‐Related Network Outages Using Discrete‐Time Markov Chains</text>
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                <text>Ibrahim A Gedel, Wahab A Iddrisu</text>
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                <text>In this paper, we model the causes of power‐related network outages in Ghana using discrete‐time Markov chains. We used data consisting of 2,756 small‐scale carrier telecommunications outages occurring in Ghana, with accompanying root causes over a period of 5 years and 8 months, from August 2015 to April 2021. The results indicate that the majority (n = 1,404) of the network outages were caused by the generators while the least number (18) of outages were caused by a communication equipment. However, longer network outages were caused by fuel issues with an average outage time of 1,027.82 min over the study period. The transition probability matrix obtained from the data revealed that regardless of the present cause of the network outage, the probability that the next network outage will be caused by the generators is higher than the probability that the outage will be attributable to any other …</text>
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                <text>Assessment of Neonatal Mortality and Associated Hospital-Related Factors in Healthcare Facilities Within Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana</text>
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                <text>Kassim Tawiah, Killian Asampana Asosega, Samuel Iddi, Alex Akwasi Opoku, Iddrisu Wahab Abdul, Richard Kwame Ansah, Francis Kwame Bukari, Eric Okyere, Atinuke Olusola Adebanji</text>
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                <text>Objectives&#13;
Ghana’s quest to reduce neonatal mortality, in hospital facilities and communities, continues to be a nightmare. The pursuit of achieving healthy lives and well-being for neonates as enshrined in Sustainable Development Goal three lingered in challenging hospital facilities and communities. Notwithstanding that, there have been increasing efforts in that direction. This study examines the contributing factors that hinder the fight against neonatal mortality in all hospital facilities in the Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana.&#13;
Methods&#13;
The study utilized neonatal mortality data consisting of neonatal deaths, structural facility related variables, medical human resources, types of hospital facilities and natal care. The data was collected longitudinally from 2014 to 2019. These variables were analysed using the negative binomial hurdle regression (NBH) model to determine factors …</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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                <text>Identifying factors associated with child malnutrition in Ghana: a cross-sectional study using Bayesian multilevel ordinal logistic regression approach</text>
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                <text>Wahab Abdul Iddrisu, Opoku Gyabaah</text>
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                <text>Objective&#13;
In developing countries, malnutrition is a noteworthy concern related to the well-being of people, and this study aimed to determine the factors that affect malnutrition among children below 5 years in Ghana.&#13;
Design&#13;
The study used a secondary data source, specifically the Ghanaian Multiple Indicator Cluster Survey Six (MICS 6), which was conducted by the Ghana Statistical Service in 2017–2018. The MICS data are hierarchical, as children are categorised within households, and households are further grouped within a higher cluster, violating the independence assumption that must be addressed in the analyses. This study used a Bayesian multilevel ordinal logistic regression to model, identify and analyse the factors linked to child malnutrition in Ghana.&#13;
Setting&#13;
The setting of the study was the household level across the previous 10 administrative regions in Ghana.&#13;
Participants&#13;
Data for 8875 children …</text>
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                <text>Understanding water quality dynamics in aquaculture ponds in Sunyani, Ghana: Insights from Partial Least Squares (PLS)-Path Modeling</text>
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                <text>Nicole EG Otoo, Peter O Sanful, Wahab A Iddrisu, Solomon Amfoh, Okyere K Boateng</text>
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                <text>Oreochromis niloticus, and African catfish Clarias gariepinus, were selected for the study. Eleven fish ponds with stock ranging between 7-21 days were randomly selected and sampled at monthly intervals for five months, with the aim of capturing water quality patterns through a full production cycle. Seventeen parameters were measured and analyzed using Partial Least Squares (PLS)-Path Modeling. Most ponds had unusually shallow depths, characterized by excessive stocking densities of 200% on average above recommended rates. This necessitates high feeding rates, thereby reducing the assimilative capacity of ponds. The effect of feeding intensity on water quality increased with stock age. Persistent algal blooms, low DO, high temperatures and elevated levels of ammonia, nitrite, phosphate, TDS and conductivity were indicative of generally poor water quality. The model showed that 82% variability in water quality was due to production inputs. Standard stocking rates, feeding rates and construction of deeper ponds are recommended.</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>Success factors of pre-construction stages of Ghanaian public road construction project life cycle</text>
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                <text>The research article aims to determine the critical success factors (CSFs) of the pre-construction stages of the public road construction project life cycle in Ghana. Data on 26 CSFs and 16 success criteria were collected from key stakeholders across Ghana through a questionnaire survey. The data was then analyzed using structural equation modelling (SEM). The study showed that external environmental factors and project management factors directly influence success across the pre-construction phases of the Ghanaian public road construction project life cycle whereas procurement-related factors only influence the success of the works procurement phase. The research outcome would inspire construction industry stakeholders to be dynamic and pay utmost attention to the application of the CSFs identified across the pre-construction phases of the Ghanaian public road construction project life cycle. This is the …</text>
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                <text>Modeling and Forecasting of Ghana’s Inflation Volatility</text>
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                <text>Abdul-Karim Iddrisu, Dominic Otoo, Iddrisu Wahab Abdul, Sylvia Ankamah</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>Enhancing the success of Ghanaian public road construction projects</text>
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                <text>Chelteau Barajei, Nongiba A Kheni, Emmanuel Appiah-Kubi, Humphrey Danso, Abdul Wahab Iddrisu</text>
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                <text>The objective of this research paper is to determine the critical success factors (CSFs) influencing the success of public road construction projects from the perspective of a developing nation. A self-administered questionnaire was used to gather data on the extent to which critical success factors gathered from literature impact the success of public road construction projects from sector practitioners’ perspectives in Ghana. The data was then analysed using factor analysis. The study revealed that external environmental factors have the highest influence (46.23%) on public road construction project success, followed by project management factors (12.25%) while procurement-related factors have the least impact (6.97%). Public construction project management organisations and the government will now have a deeper understanding of CSFs that influence the success of public construction projects and use them as …</text>
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