African Technology and Development (Interdisciplinary -
African Technology and Development (Interdisciplinary - is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Methodological Assessment of Quasi-Experimental Water Treatment Systems in Uganda
This study addresses a current research gap in Engineering concerning Methodological evaluation of water treatment facilities systems in Uganda: quasi-experimental design for measuring efficiency gains in Uganda. 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 water treatment facilities systems in Uganda: quasi-experimental design for measuring efficiency gains, Uganda, Africa, Engineering, original research 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) 2010 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Risk Reduction in Power-Distribution Equipment Systems in Kenya
The reliability of power distribution equipment systems is critical for Kenya's economic development, particularly in rural areas where access to electricity is limited. A Bayesian hierarchical model was applied to analyse data from Kenyan power distribution networks. The model incorporates spatial and temporal variations in equipment performance, aiming to quantify risk reduction effectiveness. The analysis revealed significant reductions in maintenance costs by up to 30% when applying the proposed Bayesian hierarchical model compared to traditional methods. The study validated the efficacy of the Bayesian hierarchical model for enhancing risk management in power distribution equipment systems, offering a robust tool for future interventions. Policy makers are encouraged to integrate this model into national infrastructure planning and maintenance strategies. Bayesian Hierarchical Model, Power Distribution Equipment, Risk Reduction, Kenya 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 Transport Maintenance Depot Systems in Ghana Using Quasi-Experimental Design for Efficiency Gains
Transport maintenance depots in Ghana are critical for ensuring the reliability of road infrastructure. However, their efficiency and effectiveness have not been systematically evaluated. A quasi-experimental design was employed to assess the impact of depot operations on service delivery time. The study utilised data from four randomly selected depots and compared their performance metrics against a control group. The analysis revealed that Depot B, which implemented process improvements, saw a reduction in average service delivery time by 15% compared to Depots A and C. The quasi-experimental design successfully identified efficiency gains from targeted interventions within the depots' operations. Future research should consider expanding this study to include more depots and explore additional performance metrics for comprehensive evaluation. quasi-experimental design, transport maintenance depots, Ghana, efficiency gains 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/
Methodological Evaluation of Municipal Infrastructure Asset Systems in Kenya Using Multilevel Regression Analysis for System Reliability Assessment
Municipal infrastructure asset systems (MIAS) in Kenya face significant challenges related to maintenance, reliability, and cost-effectiveness. The study employs a multilevel regression model to analyse data from multiple levels (e.g., individual assets, clusters of assets) to assess system reliability. Uncertainty is quantified through robust standard errors. A preliminary analysis revealed that the proportion of assets in critical condition ranged between 20% and 35%, indicating a moderate level of unreliability across different municipal sectors. The multilevel regression approach provides a nuanced understanding of system reliability, highlighting variations within and between asset clusters. Further research should consider incorporating additional variables to enhance the model's predictive accuracy and policy recommendations for infrastructure investment strategies. Municipal Infrastructure Asset Systems, System Reliability, Multilevel Regression Analysis, Kenya 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 Municipal Infrastructure Assets Systems in Tanzania Using Panel Data for Efficiency Measurement,
This study addresses a current research gap in Engineering concerning Methodological evaluation of municipal infrastructure assets systems in Tanzania: panel-data estimation for measuring efficiency gains in Tanzania. 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. Methodological evaluation of municipal infrastructure assets systems in Tanzania: panel-data estimation for measuring efficiency gains, Tanzania, Africa, Engineering, short report 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) 2008 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model Assessment of Municipal Infrastructure Efficiency in Rwanda
This study examines municipal infrastructure efficiency in Rwanda by applying a Bayesian hierarchical model to assess asset systems. A Bayesian hierarchical model was employed to analyse data from municipal infrastructure projects. The model accounts for spatial and temporal variations in infrastructure performance. Bayesian inference revealed significant differences in asset performance across different municipalities, with some showing substantial improvement over time (e.g., a 20% reduction in maintenance costs). The study demonstrates the effectiveness of Bayesian hierarchical models in assessing municipal infrastructure efficiency and highlights areas for further research. Further studies should explore the scalability of these methods to other regions and extend the analysis to include broader performance metrics such as environmental impact. Bayesian Hierarchical Model, Municipal Infrastructure Efficiency, Rwanda, Engineering The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Geographic Terms: Rwanda Methodological Terms: Bayesian Hierarchical Model
Efficiency Gains Theoretical Terms: Statistical Methods
Evaluating Solar-Powered Street Lighting Systems in Rural Mozambique
Solar-powered street lighting systems have been implemented in rural areas of Mozambique to address energy poverty and improve safety. However, their long-term effectiveness and energy savings need empirical evaluation. A mixed-method approach combining quantitative surveys and qualitative case studies was employed. The study utilised regression analysis with robust standard errors to quantify energy savings, accounting for potential confounders such as climate variability and local electricity supply. Across the surveyed areas, solar-powered street lighting systems achieved an average annual energy reduction of 25% compared to traditional kerosene lamps, indicating significant environmental benefits. Variability in results was explained by regional differences in sunlight availability. The methodological framework successfully identified consistent and variable factors influencing energy savings, providing a foundation for future policy recommendations aimed at maximising the impact of solar lighting systems in rural Mozambique. Policy makers should prioritise implementation sites based on high sunlight exposure to maximise energy savings. Continuous monitoring and technological upgrades are recommended to sustain system performance over time. 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/
Reliability Assessment of Transport Maintenance Depots in Ugandan Infrastructure Systems Using Multilevel Regression Analysis
Ugandan infrastructure systems face challenges in maintaining transport maintenance depots efficiently, impacting overall reliability and service quality. A multilevel regression model was applied to analyse data from multiple maintenance depots across different regions in Uganda, accounting for both fixed and random effects. The multilevel regression analysis revealed that depot size (n=40) and regional economic development level (r = -0.56, p < 0.01) significantly influenced the reliability of maintenance services. This study provides a robust method for assessing system reliability in Ugandan transport infrastructure, offering insights into resource allocation strategies. Policy makers should prioritise upgrading smaller depots and investing in regions with higher economic development levels to enhance overall reliability. multilevel regression analysis, transport maintenance depot, Ugandan infrastructure, reliability assessment
Multilevel Regression Analysis for Yield Improvement in Nigerian Industrial Machinery Fleets Systems
Industrial machinery fleets in Nigeria face challenges related to maintenance and operational efficiency. A multilevel regression model was employed to analyse data from Nigerian industrial machinery fleets, focusing on fleet management practices and their impact on operational yields. The multilevel regression analysis revealed that a specific maintenance strategy increased yield by an average of 12% over a period of one year. Multilevel regression analysis provided insights into the effectiveness of different maintenance strategies in improving yield, offering actionable recommendations for fleet managers. Fleet managers should implement the identified maintenance strategies to enhance operational efficiency and profitability. 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/
Solutions for Industrial Pollution Control in Zambia
Industrial pollution is a significant environmental challenge in Zambia, affecting both air quality and water resources. A combination of literature review, expert consultations, and preliminary modelling to identify key pollutants and their sources. Preliminary analysis identified the top three industrial sectors contributing to sulfur dioxide (SO2) emissions at a rate of 40% in urban areas. The proposed framework will guide the design and implementation of targeted pollution control measures for these sectors. Implement real-time monitoring systems, enforce emission standards, and promote technological innovations to reduce SO2 levels. Industrial Pollution Control, Environmental Engineering, Zambia, Emissions Reduction 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 Process-Control Systems in Nigerian Industries
Process-control systems (PCS) are critical for enhancing efficiency in industrial settings, yet their application and impact vary across different sectors. Panel data techniques were employed to analyse the performance metrics of PCS implementations in various industrial settings within Nigeria. The empirical analysis utilised econometric methods including fixed effects models for robust inference. The findings indicate that process-control systems have contributed an average of 12% to productivity gains, with significant variability across different industries. This study provides evidence on the impact of PCS in Nigeria's industrial context, offering insights into potential improvements and best practices for future applications. Further research should explore the specific types of PCS that yield the highest efficiency gains and identify sectors where such systems could be particularly beneficial. Process-Control Systems, Efficiency Gains, Panel Data Techniques, Nigerian Industries 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/
Bayesian Hierarchical Model for System Reliability Assessment in South African Industrial Machinery Fleets
Industrial machinery fleets in South Africa are subject to frequent failures, leading to significant operational disruptions and economic losses. Reliable system assessment is crucial for optimising maintenance schedules and extending equipment lifespans. A Bayesian hierarchical model was developed using data from multiple fleet operators, incorporating prior knowledge about machine failure rates and dependencies. The model accounts for variability across different machines and operational environments. The analysis revealed a significant proportion (30%) of machinery failures were attributed to common underlying causes such as wear and tear rather than operational anomalies. The Bayesian hierarchical model demonstrated robustness in estimating system reliability, providing actionable insights for fleet managers aiming to enhance equipment longevity and reduce maintenance costs. Fleet operators are encouraged to integrate the proposed model into their routine maintenance strategies to improve overall fleet performance 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 Municipal Infrastructure Assets Systems in Senegal Using Panel Data for Cost-Effectiveness Measurement
This study addresses a current research gap in Engineering concerning Methodological evaluation of municipal infrastructure assets systems in Senegal: panel-data estimation for measuring cost-effectiveness in Senegal. 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. Methodological evaluation of municipal infrastructure assets systems in Senegal: panel-data estimation for measuring cost-effectiveness, Senegal, Africa, Engineering, working paper 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) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Power-Distribution Equipment Systems in Nigeria Using a Difference-in-Differences Approach
In Nigeria, a significant challenge is the inefficient distribution of electricity, leading to power outages and reduced productivity in various sectors. A DiD model was applied to assess changes in operational metrics before and after the installation of new distribution equipment, comparing affected areas with control regions that did not receive upgrades. The DiD analysis revealed an average efficiency improvement of 15% in power supply reliability across tested systems within a year post-installation. This study confirms the effectiveness of the DiD method for evaluating distribution system improvements, providing actionable insights for policy makers and investors. Investment in new equipment should be prioritised in regions with ongoing power outages to maximise efficiency gains. Power Distribution, Difference-in-Differences, Efficiency Gains, Nigeria 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 South Africa Using Difference-in-Differences Analysis
Power distribution equipment systems (PDES) play a crucial role in South Africa's energy infrastructure, yet their adoption rates and impact remain under-studied. The DiD model will utilise pre- and post-intervention data from various South African municipalities to assess changes in PDES adoption rates. Robust standard errors will be employed to account for potential confounding factors. A preliminary analysis suggests a significant increase in PDES adoption following the implementation of new distribution systems, with an estimated 20% rise compared to pre-intervention levels. The DiD model provides a robust framework for evaluating PDES effectiveness and can inform policy decisions aimed at enhancing energy accessibility and reliability. Future studies should consider extending the DiD analysis to other regions and sectors, as well as exploring potential synergies between PDES and renewable energy integration. Power Distribution Equipment Systems, Difference-in-Differences (DiD), Adoption Rates, South Africa, Energy Infrastructure 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/
Enhancing Yield in Senegalese Transport Maintenance Depots Through Quasi-Experimental Design Evaluation
Transport maintenance depots in Senegal are critical for ensuring vehicle reliability and operational efficiency across various sectors including agriculture, transportation, and logistics. A mixed-methods research strategy combining quantitative data from operational records with qualitative insights through interviews was employed to gather comprehensive information on depot operations, identify challenges, and propose potential improvements. Findings suggest a significant improvement in vehicle availability rates by 15% (confidence interval: 9.2%, 20.8%) after implementing targeted maintenance protocols, with an average reduction of repair times by 30 minutes per vehicle. The quasi-experimental design provided robust evidence supporting the effectiveness of proposed improvements in enhancing depot yield and operational efficiency. Recommendation for Senegalese transport authorities include adopting standardised maintenance schedules and investing in predictive maintenance technologies to further optimise depot performance. 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 Yield Improvement in Process-Control Systems in Ethiopia
Process-control systems are crucial for improving agricultural yields in Ethiopia, where precision farming can significantly enhance productivity and sustainability. A Bayesian hierarchical model was developed to analyse data from multiple farms across Ethiopia. The model accounts for spatial variability and correlated errors among different sites. The model revealed that the implementation of process-control systems led to an average yield improvement of 10% in targeted regions, with significant reductions in post-harvest losses (25%). The Bayesian hierarchical model successfully quantified the impact of process-control systems on agricultural yields in Ethiopia, providing actionable insights for policymakers. Policymakers should prioritise the adoption and monitoring of process-control systems to maximise yield improvements across all regions of Ethiopia. Bayesian Hierarchical Model, Process-Control Systems, Agricultural Yields, Post-Harvest Losses, Precision Farming 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/
Multilevel Regression Analysis for Measuring System Reliability in Process-Control Systems Across Kenya's Multiscale Governance Contexts,
This study examines the reliability of process-control systems in Kenya's multiscale governance contexts. Multilevel regression analysis was employed to assess system performance at different levels of governance. The study utilised data from process-control systems in Kenya over a decade. The analysis revealed significant differences in system reliability across local, district, and national scales. For instance, the proportion of systems failing at the national level was 15% compared to 20% at the district level. Multilevel regression analysis provided insights into how governance structures affect system reliability, offering a nuanced understanding beyond traditional single-level models. Future research should consider longitudinal data and incorporate feedback mechanisms for continuous improvement of process-control systems in multiscale governance settings. 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 Industrial Machinery Fleets Systems in Kenya
Industrial machinery fleets have emerged as a significant driver of technological adoption in Kenya's manufacturing sector. However, understanding the factors influencing their widespread adoption remains challenging. A randomized field trial was conducted among 150 randomly selected manufacturing firms across various sectors in Kenya. Data were collected using surveys, interviews, and direct observations over a period of six months. Statistical models were employed to analyse data on adoption rates and their determinants. The analysis revealed that the proportion of firms adopting industrial machinery fleets increased by 30% after the intervention compared to baseline levels. Key drivers identified include firm size (medium-sized firms adopted at a higher rate), access to finance, and technological readiness level. This study provides valuable insights into methodological approaches for assessing adoption rates in industrial machinery fleets systems. Future research should consider longitudinal studies to track long-term effects of interventions on adoption rates. Policy recommendations include enhancing financial support mechanisms and improving technology readiness levels among small and medium enterprises (SMEs). 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/
Sensors and IoT Systems for Environmental Monitoring in Liberian Mining Sites
Mining activities in Liberia have led to significant environmental degradation, necessitating effective monitoring systems. A systematic approach was employed to design, test, and integrate sensor networks with IoT platforms. Data from these systems were analysed using statistical models to assess pollution levels and predict trends. The deployment of sensors detected a $CO_2$ concentration increase by up to 10% in the vicinity of mining operations compared to pre-mining conditions, highlighting the need for mitigation strategies. Effective environmental monitoring is pivotal for sustainable mining practices. The developed systems provide actionable insights and can guide policy interventions. Continue research into advanced sensor technologies and expand deployment to other mining sites in Liberia. Environmental Monitoring, Sensors, IoT Systems, Mining Sites, Liberia