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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>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>Research Article 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 SIB stochastic differential equation for the number of infectious individuals I (t), and the findings showed that the sample path or trajectory of the stochastic differential equation for the number of infectious individuals I (t) is continuous but not differentiable and that the SI-B stochastic differential equation model for the number of infectious individuals I (t) fluctuates inside the solution of the SI-B ordinary differential equation model. Another significant feature of our proposed SDE model is its simplicity.</text>
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                <text>Generalized additive mixed modelling of river discharge in the black Volta river</text>
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                <text>Wahab A Iddrisu, Kaku S Nokoe, Albert Luguterah, Eric O Antwi</text>
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                <text>River discharge data offer a rich source of information for reservoir management and flood control, if modelling can separate out the effects of rainfall, land use, soil type, relief, and weather conditions. In this paper, we model river discharge data from the Black Volta River, using Generalised Additive Mixed Models (GAMMs) with a space-time interaction represented via a tensor product of continuous time and discrete space. River discharge data from January 2000 to December 2009 for the four gauge stations along the Black Volta River namely, Lawra, Chache, Bui and Bamboi were obtained from the hydrological services department of Ghana and used for model fitting. Four GAMMs were explored, two with space-time interactions and two without space-time interactions. The comparison of the performance of the models with space-time interactions and those without space-time interactions based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) suggests that in this application, the former is better overall and in particular for modelling local variations. Further, a model with space and time main effects performed better compared with one without space and time main effects. After model selection, checking and validation, there is evidence for increasing river discharge from the most upstream gauge station to the most downstream gauge station for the study period.</text>
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                <text>Modelling the Impact and Effects of Climatic Variability on Electricity Energy Consumption in the Yendi Municipality of Ghana</text>
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                <text>Wahab A Iddrisu, Sampson T Appiah, Khalid Abdul-Mumin, Abdul-Rahman Abdul-Samed</text>
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                <text>One of the cherished assets of every economy is electricity since it has proven to be the major source of energy for industrialization. Developing economies like Ghana have suffered the downside of poor management of the already inadequate electrical energy at its disposal. This is as a result of limited research into factors that influences electricity energy consumption, most importantly, the effects of climatic variables on electricity energy consumption. This research work explores the causal connection between climatic variables and electricity energy consumption, and develops a Vector Auto Regression (VAR) model to determine the influence of the climatic variables in forecasting electricity energy consumption in Yendi Municipality in the northern region of Ghana. The climatic factors considered in this work are; Rainfall (Rain), maximum temperature (Tmax), Sunshine (Sun), Wind (wind) and Relative Humidity (RH). The Granger causality tests employed in this work revealed that aside from Relative Humidity, the end energy consumption is affected by the other four climatic factors under consideration. The impulse response was used to ascertain the active interaction among electricity energy consumption and the climatic variables. The impulse response of electricity energy consumption to the climatic variables indicates a maximum positive effect of Temperature and Sunshine on electricity energy consumption in March and September respectively. The VAR model was also used in forecasting future consumption of electricity energy. The results indicate excellent forecasts of electricity</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>Even though laboratory and epidemiological studies have demonstrated the effects of ambient temperature on the transmission and survival of coronaviruses, not much has been done on the effects of weather on the spread of COVID-19. This study investigates the effects of temperature, humidity, precipitation, wind speed and the specific government policy intervention of partial lockdown on the new cases of COVID-19 infection in Ghana. Daily data on confirmed cases of COVID-19 from March 13, 2020 to April 21, 2020 were obtained from the official website of Our World in Data (OWID) dedicated to COVID-19 while satellite climate data for the same period was obtained from the official website of NASA's Prediction of Worldwide Energy Resources (POWER) project. Considering the nature of the data and the objectives of the study, a time series generalized linear model which allows for regressing on past observations of the response variable and covariates was used for model fitting. The results indicate significant effects of maximum temperature, relative humidity and precipitation in predicting new cases of the disease. Also, results of the intervention analysis indicate that the null hypothesis of no significant effect of the specific policy intervention of partial lockdown should be rejected (p-value=0.0164) at a 5\% level of significance. These findings provide useful insights for policymakers and the public.</text>
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                <text>In the academic environment, players have overstretched University faculty with less available time. The task of reading and deleting electronic mail (e-mail) spam tends to consume or steal the little available time they have at their disposal. Due to the spam issue, automated processes or methods for separating spam from valid emails are becoming important. Due to the unstructured nature of the material, additional features, and a vast number of documents, the process of automatically classifying spam email presents significant difficulties. Increasing usage of the e-mail spam directly affects the performance of these spam classifications with regards to the quality and speed based on the challenges stated above. Most of the recent algorithms consider only relevant features or characteristics for the classification of the e-mails as spam or legitimate. The main objective of this work was to use a machine-learning …</text>
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