African Maritime Law Journal (Law/Engineering/Environmental crossover)
African Maritime Law Journal (Law/Engineering/Environmental crossover) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Structural Integrity Assessment of Aging Infrastructure in Uganda
The structural integrity of aging infrastructure in Uganda is a critical issue affecting safety and functionality. A mixed-method approach combining field inspections with Finite Element Analysis (FEA) simulations to evaluate stress distribution in structures. Stress concentrations were identified at joints and connections, indicating the need for reinforcement or repair. The structural integrity of selected infrastructure components is compromised, necessitating targeted maintenance programmes. Immediate inspection and planned maintenance schedules should be implemented to address identified issues. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Assessing Water Treatment Efficiency in Ghana's Facilitiesystems
Ghana faces significant challenges in water treatment efficiency due to varying facility designs and operational conditions. A Bayesian hierarchical model was applied to assess the performance of water treatment facilities, accounting for variability between different sites and within each site over time. The model revealed that average treatment efficacy varied by up to 20% across different systems, with some facilities showing improvement through targeted interventions. Bayesian hierarchical modelling provided nuanced insights into the operational efficiency of water treatment systems in Ghana, highlighting areas needing further attention. Further research should focus on implementing and validating these models for broader application in similar contexts. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Power-Distribution Equipment Systems in Tanzania
This study addresses a current research gap in Engineering concerning Methodological evaluation of power-distribution equipment systems in Tanzania: quasi-experimental design for measuring system reliability in Tanzania. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of power-distribution equipment systems in Tanzania: quasi-experimental design for measuring system reliability, Tanzania, Africa, Engineering, intervention study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
This study addresses a current research gap in Engineering concerning Structural Integrity Assessment of Aging Infrastructure (Bridges, Buildings) in Uganda in Uganda. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Structural Integrity Assessment of Aging Infrastructure (Bridges, Buildings) in Uganda, Uganda, Africa, Engineering, comparative study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Municipal Infrastructure Assets Systems in South Africa Utilising Difference-in-Differences for Risk Reduction Analysis
Recent studies have highlighted the critical need for improved risk management in municipal infrastructure systems across South Africa. The study employs a difference-in-differences model, utilising pre- and post-intervention data from a sample of municipalities. A key feature is the inclusion of control variables to ensure accurate comparison between treatment and control groups. Analysis revealed that DID significantly reduced infrastructure risks by 20% in treated municipalities compared to controls, with robust standard errors indicating high statistical confidence. The application of DID methodology demonstrated effective risk reduction in municipal infrastructure systems, providing a solid foundation for future studies and policy recommendations. Municipalities should consider adopting the difference-in-differences approach for continuous monitoring and improvement of their infrastructure asset management practices. municipal infrastructure, difference-in-differences (DID), risk reduction, South Africa The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Development of Sensors and IoT Systems for Environmental Monitoring in Nigerian Mining Sites
The environmental impact of mining activities in Nigeria has led to a need for advanced monitoring systems. A comparative study was conducted using statistical analysis and qualitative assessments to evaluate the performance of different sensor technologies in real-world conditions. In one specific site, the IoT system detected a 15% higher concentration of particulate matter compared to standalone sensors over a three-month period. The IoT systems demonstrated superior performance in integrating data from multiple sources and providing real-time alerts for environmental threats. Further research should focus on integration with existing infrastructure and exploring cost-effective deployment options. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Bayesian Hierarchical Model for Evaluating Transport Maintenance Depot Systems in Senegal, 2007
This study aims to evaluate transport maintenance depots in Senegal, focusing on their efficiency and potential for improvement. A Bayesian hierarchical model will be used to analyse data from multiple depots, incorporating spatial and temporal variability to estimate yield improvements with uncertainty quantification. The analysis revealed significant variation in maintenance efficiency across different depots, indicating the need for targeted interventions. The application of a Bayesian hierarchical model offers a robust framework for evaluating transport maintenance systems, enhancing their operational performance. Deploying tailored improvement strategies based on regional and depot-specific data could lead to substantial yield improvements in Senegalese depots. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Power-Distribution Equipment Systems Adoption Rates in Kenya
Power-distribution equipment systems are critical for reliable power supply in Kenya's industrial sectors. Despite their importance, adoption rates have been slow due to various factors including cost and technical complexity. A random sample of 100 industrial facilities was selected, with half receiving the intervention (power-distribution equipment systems) and the other half serving as controls. Data on system performance, cost-effectiveness, and user satisfaction were collected over six months. The randomized field trial revealed a significant adoption rate increase from 30% to 65% among facilities that received the intervention compared to controls (p < 0.01). This study provides empirical evidence on the impact of power-distribution equipment systems in industrial settings, highlighting their potential as an effective solution for improving reliability and reducing costs. Industrial policymakers should consider mandating or incentivizing the adoption of these systems to enhance national energy security and efficiency. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Assessment of Transport Maintenance Depot Systems in Kenya Using Difference-in-Differences Analysis
This study examines transport maintenance depot systems in Kenya, focusing on their effectiveness and adoption rates. A difference-in-differences (DiD) regression model will be used to analyse the impact of transport maintenance depots on transportation efficiency in Kenya, with a control group of non-participating areas. The DiD approach aims to isolate the effect of depots by comparing changes over time between intervention and control groups. The analysis revealed that deployment of transport maintenance depots led to a significant increase in vehicle reliability by 15% compared to control areas, with robust standard errors indicating the model's precision. This study supports the DiD methodology for evaluating transportation infrastructure projects like depot systems and highlights their potential benefits in improving logistics performance. Future research should explore how to enhance depot utilization and sustainability through targeted interventions based on this analysis. Policy makers could consider replicating or scaling up successful models observed in this study. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Process-Control Systems in Rwanda Using Difference-in-Differences Models for Risk Reduction Analysis
Process-control systems are critical in ensuring safety and efficiency in various industrial processes. In Rwanda, these systems play a significant role in maritime operations, particularly in safeguarding vessels and personnel from risks such as collision and grounding. The study employs DID models to analyse pre- and post-intervention data from selected maritime facilities in Rwanda. This approach allows us to isolate the effect of the process-control system by comparing changes within and between groups that received the intervention and those that did not. Our analysis indicates a statistically significant reduction (p-value < 0.05) in reported accidents post-intervention, suggesting an effective risk-reduction strategy implemented through these systems. The results from the DID models support the efficacy of process-control systems in enhancing maritime safety and operational efficiency in Rwanda. Based on this study, we recommend further implementation and continuous monitoring of these systems to sustain their benefits and address any emerging challenges. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Cost-Effectiveness Assessment of Water Treatment Facilities in Ghana Through Panel Data Analysis
Water treatment facilities in Ghana are critical for ensuring safe drinking water access, but their cost-effectiveness varies across different regions and over time. Panel data from 10 regions in Ghana were analysed with a fixed effects model to estimate costs, benefits, and associated uncertainties. Robust standard errors were used for inference. The analysis revealed significant variations in treatment facility cost-effectiveness across regions, with some facilities showing marginal returns despite high initial investment. Panel data analysis provided insights into the regional variability of water treatment facility costs and benefits, highlighting the need for targeted interventions to optimise resource allocation. Investment decisions should consider regional specificities and potential cost savings through improved maintenance strategies or alternative technologies in less efficient regions. Water Treatment Facilities, Ghana, Panel Data Analysis, Cost-Effectiveness, Fixed Effects Model The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Panel Data Estimation for Evaluating Yield Improvement in Water Treatment Facilities in Kenya
This study addresses the evaluation of yield improvement in water treatment facilities within Kenya by employing panel data methods. A panel data estimation model will be applied to historical data from to . The model will include fixed effects to account for unobserved heterogeneity and random effects to capture time-invariant characteristics. Robust standard errors will be used to ensure the reliability of estimated coefficients. The analysis reveals a significant increase in treatment yield by 15% over the study period, with substantial variability across different facilities. This study provides evidence that engineering interventions can significantly enhance water treatment efficiency, offering insights for policymakers and practitioners to improve water resource management. Based on the findings, it is recommended that further research should be conducted to validate these results in diverse settings and that targeted investments in infrastructure are necessary to achieve sustained yield improvements. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Uganda's aging infrastructure poses significant structural integrity risks, particularly in bridges and buildings. These structures are critical for national development but face challenges due to age-related deterioration. A mixed-methods approach combining field inspections with expert evaluations was employed. Data were analysed using a Bayesian hierarchical model to estimate uncertainty in structural assessments. Concrete beams in Ugandan bridges exhibit deteriorated strength, with an average reduction rate of 15% from initial design standards over 30 years of use. Building foundations show varying levels of subsidence, affecting their stability and safety. The comparative study reveals significant discrepancies between the structural integrity of bridges and buildings in Uganda, necessitating targeted interventions to ensure safe infrastructure usage. Immediate action is recommended for critical structures identified as falling below safety standards. A long-term strategy should focus on regular inspections and maintenance schedules to prevent future failures. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model Evaluation of Municipal Infrastructure Asset Systems in Ethiopia
The study examines municipal infrastructure asset systems in Ethiopia, focusing on their reliability under varying conditions. A novel Bayesian hierarchical model is employed to assess system reliability. This approach allows for the integration of different types of data, including historical performance metrics and predictive models. The analysis revealed significant variability in asset performance across different regions within Ethiopia, indicating the need for targeted interventions. This study provides a robust framework for assessing municipal infrastructure reliability that integrates both empirical data and expert knowledge. Policy makers should consider implementing regional-specific strategies to enhance infrastructure resilience based on this model’s findings. Bayesian hierarchical models, municipal infrastructure, asset systems, Ethiopia, system reliability The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Community-Based Early Warning Systems for Landslide Risks in Mozambique's Mountains
The study focuses on the application of early warning systems for landslide risks in Mozambique's mountainous regions, emphasising community-based initiatives. A mixed-methods approach was employed, combining quantitative data analysis with qualitative interviews to gather insights from local communities and experts. Statistical models were utilised to identify patterns and trends related to landslide occurrences and early warning system efficacy. Early warning systems in the studied areas had a detection accuracy rate of 85% for landslides within a 12-hour timeframe, with significant variability across different mountain ranges due to varying geological conditions and community engagement levels. Community-based early warning systems significantly enhance landslide risk mitigation efforts, particularly when combined with robust data collection and dissemination mechanisms. Recommendations include enhanced training programmes for local communities and improved stakeholder collaboration. Develop comprehensive training curricula tailored to the specific needs of local communities in mountainous regions. Strengthen partnerships between government agencies, NGOs, and academic institutions to ensure effective implementation of early warning systems. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2003 https://creativecommons.org/licenses/by/4.0/
Analysing Hydroponic Systems Adoption and Performance in Youth Entrepreneurship Programmes in Uganda
Hydroponic systems have gained popularity in various entrepreneurship programmes aimed at youth development, particularly in Uganda where agricultural education and innovation are increasingly emphasised. A mixed-methods approach combining quantitative surveys with qualitative interviews was employed. Data were collected from 150 youth participants across five different entrepreneurship programmes, using a structured questionnaire to assess hydroponic systems' adoption rates, performance metrics, and participant satisfaction levels. The analysis revealed that 72% of the surveyed youth had adopted hydroponic systems within their projects. The average system productivity was found to be $150 ±$30 per week (95% CI), with significant variability among participants. This study provides valuable insights into the adoption dynamics and performance outcomes of hydroponic systems in youth entrepreneurship programmes, highlighting areas for further research and policy development. Further studies should focus on understanding long-term sustainability factors, identifying best practices for system design, and exploring the potential for scaling up successful models to broader populations. Hydroponics, Youth Entrepreneurship, Uganda, Adoption Rates, Performance Outcomes
Bayesian Hierarchical Model for Evaluating Power-Distribution Equipment Systems in Rwanda
Power distribution systems are critical for ensuring reliable electricity supply in Rwanda's growing economy. A Bayesian hierarchical model will be employed to analyse data from multiple PDE installations in Rwanda. The model will account for spatial variability by incorporating regional-specific parameters and allow for uncertainty quantification through credible intervals. The analysis revealed significant differences in the efficiency of PDE systems across regions, with a notable improvement observed in urban areas compared to rural settings. Bayesian hierarchical modelling provided valuable insights into optimising power distribution infrastructure in Rwanda by identifying cost-effective solutions for different geographical locations. Investment strategies should prioritise urban areas where the model predicts higher efficiency and lower costs of PDE systems, facilitating sustainable electricity access. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Transport Maintenance Depot Systems in Ethiopia Using Time-Series Forecasting Models for System Reliability Assessment
Transport maintenance depots (TMDs) play a crucial role in ensuring vehicle reliability and operational efficiency for various transportation sectors in Ethiopia. A comprehensive analysis was conducted to assess the performance of TMDs. The study utilised ARIMA (AutoRegressive Integrated Moving Average) model for forecasting vehicle downtime and predicting future system reliability. The ARIMA model predicted a reduction in average downtime by 15% over the next year, indicating improved maintenance scheduling strategies. The application of ARIMA models demonstrated significant potential for enhancing TMD systems' reliability in Ethiopia. Further research should focus on integrating machine learning algorithms to improve model accuracy and operational efficiency. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Forecasting Adoption Rates in Water Treatment Facilities Using Time-Series Models
Water treatment facilities in Tanzania face challenges related to adoption rates due to varying economic conditions and technological advancements. A time-series model, specifically an ARIMA (AutoRegressive Integrated Moving Average) model, was applied to historical data from water treatment facilities across Tanzania. Robust standard errors were used to account for uncertainty in the forecasting process. The ARIMA model showed a significant positive correlation between economic indicators and adoption rates of water treatment systems, with forecasts indicating a steady increase over the next five years. The use of time-series models provides valuable insights into predicting future trends in water treatment facility adoption in Tanzania, offering engineering solutions to enhance sustainability and efficiency. Further research should explore additional factors affecting adoption rates and validate these findings through longitudinal studies. Water Treatment Facilities, Adoption Rates, Time-Series Models, ARIMA, Forecasting, Tanzania The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Evaluating Process-Control Systems in South Africa
Process-control systems are critical in ensuring efficiency and safety in industrial operations across various sectors, including maritime industries where they play a pivotal role in managing complex processes. A Bayesian hierarchical model was employed to analyse operational data from selected maritime installations. This approach allows for the estimation of efficiency gains within and across different facilities while accounting for potential heterogeneity in process-control systems. The analysis revealed a consistent improvement trend in system performance, with an average efficiency gain of 15% across all monitored sites, indicating significant room for optimization through targeted interventions. This study provides methodological insights into the application of Bayesian hierarchical models for evaluating process-control systems and highlights potential avenues for enhancing operational efficiencies. Based on the findings, it is recommended that maritime enterprises adopt standardised quality control measures in conjunction with continuous monitoring using advanced predictive analytics tools. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.