Average Time to Justice Delivery; A Case Study in the Upper West and East Regional District and Circuit Courts in Ghana

In this study, survival analysis was used to determine the average time to justice delivery in law courts for the Upper East and Upper West regions of Ghana.  The study revealed that the average time to justice delivery was 103 days. Four major factors were found to contribute significantly to the average time to justice delivery.  These were; the type of court handling the case, the type or nature of case, the occupation of the accused and the number of subsequent hearings.  Also, it was evident from the study that cases terminated faster in Upper East courts as compared to the Upper West courts.  Finally, it was found that civil cases tend to have shorter life spans than criminal cases. Keywords: Justice Delivery, Survival Analysis, Censoring, Court       

Calculating non-centrality parameter for power analysis under structural equation modelling: An alternative

Identifying the most parsimonious model in structural equation modelling is of utmost importance and the appropriate power estimation methods minimize the probabilities of Type I and Type II errors. The power of a test depends on the sample size, Type I error, degrees of freedom and effect size. In this study, a modified approach of using effect size in calculating the non-centrality parameter for power is proposed. This is compared to the approach in Maccallum et al. (1996) at different degrees of freedom and sample size specifications --- taken from 50 to 2000. As the sample size increased the difference between the power of a test for both methods reduced to zero. The results showed that the values for power of a test are the same for the modified and original approaches for large sample sizes and degrees of freedom. The findings also revealed that the sample discrepancy function ($\hat{F}$) is asymptotically unbiased

Comparative Analysis of Some Structural Equation Model Estimation Methods with Application to Coronary Heart Disease Risk

This study compared a ridge maximum likelihood estimator to Yuan and Chan (2008) ridge maximum likelihood, maximum likelihood, unweighted least squares, generalized least squares, and asymptotic distribution-free estimators in fitting six models that show relationships in some noncommunicable diseases. Uncontrolled hypertension has been shown to be a leading cause of coronary heart disease, kidney dysfunction, and other negative health outcomes. It poses equal danger when asymptomatic and undetected. Research has also shown that it tends to coexist with diabetes mellitus (DM), with the presence of DM doubling the risk of hypertension. The study assessed the effect of obesity, type II diabetes, and hypertension on coronary risk and also the existence of converse relationship with structural equation modelling (SEM). The results showed that the two ridge estimators did better than other estimators. Nonconvergence occurred for most of the models for asymptotic distribution-free estimator and unweighted least squares estimator whilst generalized least squares estimator had one nonconvergence of results. Other estimators provided competing outputs, but unweighted least squares estimator reported unreliable parameter estimates such as large chi-square test statistic and root mean square error of approximation for Model 3. The maximum likelihood family of estimators did better than others like asymptotic distribution-free estimator in terms of overall model fit and parameter estimation. Also, the study found that increase in obesity could result in a significant increase in both hypertension and coronary risk. Diastolic blood pressure and diabetes have significant converse effects on each other. This implies those who are hypertensive can develop diabetes and vice versa

The Impact of Vehicle Engine Characteristics on Vehicle Exhaust Emissions for Transport Modes in Lagos City

ABSTRACTThe study delves into how vehicle engine characteristics impact the release of air pollutants from various vehicle fleets in Lagos, Nigeria. It involved the direct measurement of emissions from the exhaust pipes of 88 vehicles using gas analyzers. The vehicle fleets encompassed motorcycles, tricycles, private cars, minibuses, large buses, and trucks. A statistical analysis was conducted on carbon monoxide (CO) and nitrogen oxide (NOx) emissions to develop a model equation based on vehicle type, engine type, vehicle age, and purchase status. Results indicate that personal cars and minibuses predominantly emit CO from gasoline engines, whereas large buses and trucks significantly contribute to NOx emissions from diesel engines. Further scrutiny revealed that 66% of the vehicles examined were over 10 years old, resulting in a 65% increase in emission levels. Approximately 60% of gasoline and 75% of diesel vehicles exceeded the permissible emission limits, leading to air quality deterioration and heightened health risks. The study underscores the risks associated with ageing vehicles and different engine types, emphasizing the imperative for a gradual transition to low-carbon or electric vehicles in developing African cities to combat air pollution and mitigate health hazards

The impact of traffic mobility measures on vehicle emissions for heterogeneous traffic in Lagos City

Emissions from vehicular traffic are one of the key drivers of urban environmental air pollution, degrading the ambient air quality in many African cities and across the globe. This study sought to assess the impact of traffic mobility measures on the emission levels of air pollutants from vehicle exhaust in Lagos, Nigeria. Traffic flow and vehicle mix were collected daily during morning, afternoon, and evening peak periods along selected arterial and two-lane collector roadways. Simultaneously, five (5) air pollutants from vehicular traffic emissions were observed at distinct monitoring stations along the roadways using portable gas detectors. The traffic flow and mobility measures were inputted into a multiple exponential model to estimate the concentration of each pollutant. The result of the vehicle fleet composition revealed 3.5%, 4.5%, 57%, 32%, 1.5%, and 2% for motorcycles, tricycles, personal cars, minibuses, large buses, and heavy goods vehicles, respectively, on the two-lane road and 10%, 35%, 27%, 10%, and 18% for motorcycles, personal cars, minibuses, large buses, and heavy goods vehicles, respectively, on the arterial. The results of a multiple exponential regression model (MER) showed significant contributions from traffic flow, speed, vehicle fleet proportion, and pollutant concentration (p <0.05). However, the impact on ambient air quality revealed severe pollution levels for CO and PM2.5, while SO2, NO2, and PM10 showed moderate, poor, and poor pollution levels. This study has provided evidence on how pollutant emissions increase rapidly during peak traffic conditions in heterogeneous traffic on different classes of roads and recommended policies for managing vehicle fleets and traffic congestion with the aim of reducing the impact on the ambient air quality and health of the public on heavily trafficked roadways in sub-Saharan African cities

Public perceptions of vehicular traffic emissions on health risk in Lagos metropolis Nigeria: A critical survey

The perception and behavior of the public is key in reducing Traffic-related air pollution health burdens which has become an increasingly alarming problem in many cities across the globe. The study assessed the perception of the public about vehicle traffic emissions and the health hazard associated with them in Lagos, Nigeria using structured questionnaires. Multivariate statistical analysis and structural equation modeling were performed to determine the factors that were associated with the participant’s perception of traffic air pollution and the health risks it presents. The findings revealed the majority (78.9%) of the respondents were aware of the haze air pollution from vehicles and its adverse effects on health. The regression model showed a significant relationship between age, education status, employment status, road proximity, vehicle ownership and air pollution awareness (P < 0.05). However, the structural equation model SEM revealed that age, gender, marital status, education, employment status, and road proximity showed statistical significance (p < 0.05) and indicated a linear relationship to vehicular emissions perception. The findings suggest the need to increase public education for all ages and especially for roadside residents on the effects of prolonged exposure and long-term effects of transport-related air pollution and associated risk. The result is applicable in many developing cities, especially in Sub-Saharan Africa