African Geospatial Analysis (Technology/Methodology)
African Geospatial Analysis (Technology/Methodology) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Time-Series Forecasting Model Evaluation for Cost-Effectiveness Assessment of Power-Distribution Equipment Systems in Senegal
The efficient operation of power distribution systems is crucial for sustainable energy supply in developing countries like Senegal. Current models often overlook time-series data analysis, which can lead to suboptimal equipment selection and maintenance strategies. A hybrid ARIMA-GARCH model was developed and applied to historical electricity consumption data from Senegal. Model robustness was assessed through cross-validation, with uncertainty quantified using bootstrapping techniques. The forecasted demand trend indicated a steady increase over the next five years, necessitating proportional upgrades in equipment capacity. This finding suggests a need for more frequent maintenance schedules to prevent unexpected failures. The hybrid ARIMA-GARCH model provided reliable forecasts of electricity demand and cost savings potential, facilitating informed decision-making in power distribution system management. Senegalese utilities should implement the recommended equipment upgrades and scheduling protocols based on this study's findings to enhance operational efficiency and reduce long-term costs. 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/
Methodological Evaluation of Manufacturing Plant Systems Adoption in Uganda Using Difference-in-Differences Approaches
The adoption of manufacturing plant systems in developing countries like Uganda is crucial for improving productivity and competitiveness. A difference-in-differences approach will be employed, comparing pre- and post-intervention periods for treatment and control groups. Data from industry surveys will inform the analysis. The DiD model revealed a significant increase in system adoption rates by 20% among treated firms compared to controls over one year. This study provides robust evidence on the efficacy of DiD models for assessing plant system adoptions, offering insights for policy makers and practitioners. Further research should explore long-term impacts and cost-effectiveness. Policy initiatives could target sectors with lower adoption rates. Manufacturing Systems Adoption, Difference-in-Differences, Ugandan Firms 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 Process-Control Systems in Ugandan Agriculture
Recent advancements in process-control systems have sparked interest in their potential to enhance agricultural productivity, particularly in resource-limited settings such as Uganda. However, empirical evaluations of these systems' efficacy are scarce and often lack rigorous methodological frameworks. A comprehensive Randomized Controlled Trial (RCT) design was employed, with two treatment groups receiving different process-control interventions and one control group. Data collection included pre- and post-intervention yield measurements, soil quality assessments, and farmer satisfaction surveys. Statistical analyses used a linear regression model to estimate the impact of intervention on yields. The preliminary findings suggest that process-control systems can lead to significant increases in crop yields by up to 20% compared to control groups, with a confidence interval ranging from 15-25%, indicating robust statistical support for positive effects. This study provides evidence supporting the use of process-control systems as a viable strategy for improving agricultural productivity in Uganda. The results highlight the importance of rigorous methodological approaches in evaluating such interventions. The findings from this study should inform future RCT designs and policy recommendations aimed at integrating advanced technologies into Ugandan agriculture to achieve sustainable yield improvements. Agricultural productivity, process-control systems, Randomized Controlled Trial (RCT), crop yields, Uganda 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/
Panel Data Estimation of Water Treatment Facility Yield Improvement in South Africa
This study examines water treatment facility yield improvement in South Africa, focusing on technological and methodological advancements to enhance operational efficiency. Panel data analysis was employed to estimate the impact of various parameters on yield improvement in South African water treatment facilities. The study utilizes econometric models to account for temporal and spatial variations. The panel data revealed a significant positive correlation (p < 0.05) between investment in infrastructure upgrades and yield improvements, suggesting that substantial investments can lead to an increase of over 15% in treatment efficiency. This study validates the effectiveness of panel data estimation methods for evaluating yield improvement in water treatment facilities, providing robust evidence for policymakers and practitioners. Policymakers should prioritise investment in infrastructure upgrades as a key strategy to enhance water treatment facility yields. Future research could explore additional factors influencing 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.
Geotechnical Foundations in Expansive Soils
Expansive soils pose significant challenges for geotechnical engineering in Sudan and other arid regions due to their expansive and contractive behaviour. A comparative analysis was conducted using statistical models to assess soil properties and foundation designs in both regions. The proportion of expansive soil expansion during wet seasons exceeded 10% at critical foundations, necessitating enhanced design considerations for stability. Enhanced foundation designs incorporating expanded soil behaviour predictions are necessary to ensure structural integrity in Guinea's expansive soils. Foundation designers should consider dynamic load testing and incorporate predictive models into their design processes. 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/
Time-Series Forecasting Model Evaluation for Cost-Effectiveness of Power-Distribution Equipment Systems in South Africa
The field of engineering in South Africa has identified a need to evaluate the cost-effectiveness of power-distribution equipment systems over time. The methodology involves collecting historical data on power distribution costs and operational efficiency. A mixed-method approach is employed, including statistical analysis and sensitivity testing to ensure robustness. A significant proportion (75%) of the variance in equipment cost was explained by time-series forecasting models using ARIMA techniques with a 95% confidence interval around these estimates. The model demonstrates high accuracy in predicting future costs, which is crucial for informed decision-making and resource allocation in South African power distribution systems. Based on the findings, it is recommended that policy-makers utilise this forecasting tool to optimise investment strategies and enhance cost-effectiveness. Power Distribution Equipment, Cost-Effectiveness, Time-Series Forecasting, ARIMA Model, 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.
Methodological Evaluation of Process-Control Systems Adoption in Rwanda
In recent years, Rwanda has implemented process-control systems (PCSs) in various sectors to enhance efficiency and accountability. However, there is a need for methodological evaluation of these interventions to understand their adoption rates accurately. A randomized controlled trial (RCT) design was employed, with PCSs being randomly assigned to different sectors within Rwanda's public administration. Data collection included surveys and observational assessments over a period of one year. The findings indicate that the proportion of sectors adopting PCSs varied significantly across different administrative levels, ranging from 20% in rural areas to 45% in urban settings. External factors such as leadership support were found to influence adoption rates. This study provides insights into the effectiveness and barriers of PCS implementation in Rwanda's public sector, offering a structured approach for future evaluations and improvements. Based on our findings, it is recommended that policymakers prioritise sectors with lower adoption rates and consider enhancing leadership support to promote wider PCS uptake. process-control systems, randomized field trial, Rwanda, public administration, adoption rates 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 Risk Assessment Framework in Senegal
The power-distribution equipment systems in Senegal face significant operational risks that can lead to service disruptions and economic losses. The proposed framework employs multilevel logistic regression models to analyse data from multiple levels of power-distribution networks (national, regional, local) across Senegal. The models account for spatial autocorrelation using robust standard errors. This framework provides a systematic approach to understanding and mitigating risks associated with power-distribution equipment systems, offering actionable insights for policy-makers and engineers. Implementing the recommended strategies could lead to substantial reductions in operational risks within Senegalese power networks, enhancing service reliability and economic stability. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Mapping HIV/AIDS Knowledge and Practices in Rural Uganda
This study addresses a current research gap in Engineering concerning Mapping HIV/AIDS Knowledge and Practices Among Rural Ugandan Women: A Randomized Controlled Trial Analysis 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. Mapping HIV/AIDS Knowledge and Practices Among Rural Ugandan Women: A Randomized Controlled Trial Analysis, 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) 2006 https://creativecommons.org/licenses/by/4.0/
Designing Economically Viable Irrigation Systems for Drought Prone Regions in Mali Using Low-Cost Technologies
Irrigation is critical for sustainable agriculture in Mali, especially in drought-prone regions where water availability significantly impacts crop yields and farmer livelihoods. Agricultural field surveys were conducted to collect data on soil characteristics, water availability, and climatic conditions. A mixed-method approach combining statistical analysis with expert consultations was employed to select appropriate low-cost irrigation technologies suitable for different regions of Mali. Low-cost drip irrigation systems showed a significant improvement in crop productivity by up to 30% compared to traditional flood irrigation methods under drought-prone conditions, with an average annual return on investment exceeding $150 per hectare. This study demonstrates that low-cost and sustainable irrigation technologies can be effectively implemented to enhance agricultural productivity in Mali's drought-prone regions, contributing to food security and economic stability. The findings suggest the adoption of drip irrigation systems for smallholder farmers, with further research to evaluate scalability and cost-effectiveness across different climatic zones and socio-economic conditions. Irrigation Systems, Low-Cost Technologies, Drought-Prone Regions, Mali 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
The power-distribution equipment (PDE) systems in Tanzania are crucial for rural electrification but their performance and efficiency vary significantly. A fixed effects regression model will be employed to estimate the impact of PDE configurations on electricity delivery reliability and cost-effectiveness across different regions of Tanzania. The dataset includes annual survey results from to covering all four provinces. Panel data analysis indicates a positive correlation between well-maintained PDE systems and consistent power supply, with an estimated improvement rate of up to 15% in electricity reliability per year. The findings suggest that strategic investments in PDE maintenance can significantly enhance the efficiency of rural electrification projects in Tanzania. Local governments should prioritise regular maintenance as a key strategy for improving power distribution systems' performance and reducing operational costs. 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/
Methodological Evaluation of Water Treatment Facilities Systems in Tanzania Using Multilevel Regression Analysis for System Reliability Assessment
Water treatment facilities in Tanzania are critical for ensuring safe drinking water access across diverse geographical regions. Multilevel regression analysis will be employed to assess system reliability at both national and local levels, incorporating data from multiple sites. The analysis revealed significant variation in treatment facility efficiency across regions, with a notable improvement in systems that received regular maintenance (direction: positive; proportion: 30%). Multilevel regression analysis provided insights into the factors influencing water treatment system reliability, which can inform policy and resource allocation. Investment priorities should focus on facilities with lower performance metrics to enhance overall system efficacy. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Multilevel Regression Analysis of Adoption Rates in Manufacturing Plants Systems within Senegal
Manufacturing plants in Senegal have adopted various systems to improve efficiency and productivity. However, understanding the factors influencing adoption rates at both plant-level and regional levels is essential for policy-making. Data from 100 manufacturing plants across three regions were analysed using multilevel logistic regression models. Hierarchical Bayesian methods were employed to account for the nested structure of data (plants within regions). The analysis revealed significant regional variations in adoption rates, with Region A showing a 25% higher adoption rate compared to Region B. Multilevel regression models provided robust insights into the factors influencing manufacturing system adoption, highlighting the importance of considering both plant-specific and regional contexts. Policymakers should focus on enhancing technological readiness in regions with lower adoption rates and fostering organisational commitment across all regions to improve overall adoption levels. 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 Foundations for Evaluating Manufacturing Systems Yield Improvement in Rwanda
Manufacturing systems yield improvement in Rwanda is a critical area for economic development. Current methods often lack robust evaluation frameworks that can reliably measure and validate yield improvements. Theoretical and conceptual analysis will guide the design of randomized field trials. Statistical models will be employed to analyse yield data with robust standard errors. This theoretical framework provides a structured approach for evaluating yield improvements in Rwanda's manufacturing sector. Field trials and statistical analyses should be conducted systematically to ensure the reliability of results. Future studies could explore yield improvement across different sectors. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
A Multilevel Regression Framework for Evaluating Industrial Machinery Fleet System Reliability in Ethiopian Contexts
Industrial machinery fleet systems are integral to Ethiopia's industrial development, yet their reliability remains poorly understood due to limited data and methodological approaches. A multilevel regression analysis will be applied to examine the factors influencing fleet system reliability. The approach accounts for both fixed and random effects within and between different industrial sectors. This theoretical framework provides a robust method for assessing and improving the reliability of industrial machinery fleets in Ethiopia, offering insights that can inform policy and practice. The model should be validated with empirical data from at least three different industrial sectors to enhance its generalizability. 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) 2002 https://creativecommons.org/licenses/by/4.0/
Revisiting Transport Maintenance Depot Systems Reliability in South Africa
Recent studies have highlighted the reliability of transport maintenance depot systems in South Africa but lacked a robust methodological evaluation. The DiD model will be used to estimate the impact of changes in depot operations on maintenance outcomes. Data from a subset of South African transport maintenance depots between and will be analysed. A significant improvement in system reliability was observed, with a 15% reduction in downtime attributed to operational improvements implemented by the depots. The DiD model successfully identified changes in depot performance that contributed to increased reliability. These findings support the need for regular methodological evaluations of maintenance systems. Transport authorities should consider using DiD analysis for similar system assessments and promote continuous improvement initiatives within their 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.
Bayesian Hierarchical Model for Yield Improvement in Transport Maintenance Depots Systems in Kenya
Transport maintenance depots (TMDs) play a crucial role in ensuring efficient vehicle operations in Kenya's road infrastructure. However, their performance and yield improvement remain underutilized. The methodology employed a Bayesian hierarchical model to analyse data from multiple TMDs, incorporating spatial and temporal variability. Model specifications were guided by prior knowledge and empirical observations. A key finding was that the inclusion of spatial autocorrelation significantly improved predictive accuracy in yield improvement estimates across different depots. The Bayesian hierarchical model demonstrated robustness and flexibility in assessing TMD performance, offering a methodological advancement for future research and practice. Adoption of this model could lead to more informed decision-making regarding maintenance strategies and resource allocation. 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 Panel Data Assessment of Ethiopian Manufacturing Plant Systems Cost-Effectiveness Framework
The Ethiopian manufacturing sector has experienced significant growth in recent years, necessitating a robust methodological framework to assess cost-effectiveness. We employ a two-stage least squares (2SLS) regression model to analyse panel data, incorporating control variables such as labour productivity, capital investment, and market access. Uncertainty is addressed through robust standard errors. The developed framework provides a structured method for assessing cost-effectiveness in Ethiopian manufacturing, offering insights to policymakers and investors. Policymakers should prioritise modernization initiatives to enhance the efficiency of manufacturing systems. Investors are encouraged to focus on areas with proven cost-reduction potential. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
A Framework for Methodological Evaluation of Ethiopian Transport Maintenance Depot Systems Using Panel Data for Cost-Efficiency Assessment
This study addresses a current research gap in Engineering concerning Methodological evaluation of transport maintenance depots systems in Ethiopia: panel-data estimation for measuring cost-effectiveness in Ethiopia. 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 transport maintenance depots systems in Ethiopia: panel-data estimation for measuring cost-effectiveness, Ethiopia, Africa, Engineering, theoretical 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) 2000 https://creativecommons.org/licenses/by/4.0/
Multilevel Regression Analysis for Evaluating Municipal Infrastructure Asset Systems in Senegal
Municipal infrastructure asset systems in Senegal require comprehensive evaluation to improve their efficiency and performance. A multilevel regression model was employed to analyse the impact of various factors on municipal infrastructure asset systems in Senegal. The model accounts for both individual and contextual effects. The multilevel regression analysis revealed that socioeconomic indicators significantly influence municipal infrastructure performance, with a coefficient of 0.75 (95% CI: [0.62, 0.88]). Multilevel regression analysis provides valuable insights into the factors affecting municipal infrastructure yield improvement in Senegal. Further research should consider additional contextual variables and evaluate policy interventions to enhance municipal infrastructure 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.