African Satellite Imaging (Technology/Methodology)
African Satellite Imaging (Technology/Methodology) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Methodological Evaluation of Manufacturing Systems Yield Improvement in Ghanaic Plants
Manufacturing systems in Ghana are crucial for economic development but often face challenges related to yield inefficiencies. A randomized field trial was conducted across multiple Ghanaian plants. Key performance indicators (KPIs) such as production output, quality control metrics, and resource utilization were monitored to assess yield improvements. Significant improvements in yield were observed with a mean increase of 15% in product output compared to baseline conditions. Variance analysis indicated that these gains were statistically significant at the p < 0.05 level. The randomized field trial methodology provided robust evidence for enhancing manufacturing system yields, which could be replicated across other Ghanaian plants. Further research should explore scalability and cost-effectiveness of the identified yield improvement strategies. Manufacturing Systems Yield Improvement, Randomized Field Trial, Ghana 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 Process-Control Systems in Uganda Using Time-Series Forecasting for System Reliability Analysis
Process-control systems are critical for maintaining quality and efficiency in manufacturing processes. In Uganda, these systems play a vital role in various industries but their effectiveness varies significantly. The study employs time-series forecasting techniques such as ARIMA (Autoregressive Integrated Moving Average) to analyse historical data from manufacturing processes. Robust standard errors are used to account for model uncertainty. A significant proportion, approximately 42%, of the forecasted values deviated from actual process measurements, indicating room for improvement in system accuracy and reliability. The findings suggest that while current control systems generally function well, there is potential for enhancing their performance through targeted interventions based on forecasting results. Manufacturing companies should prioritise regular maintenance and calibration of equipment to ensure consistent process output. Training programmes for operators are also recommended to improve system 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 Evaluation of Water Treatment Facilities Systems in Nigeria
Water treatment facilities in Nigeria often face challenges related to yield, which affects their efficiency in providing safe drinking water. A randomized field trial was conducted across ten randomly selected communities in Nigeria, where participants were provided with both conventional and improved water treatment units (ITUs). Data collection included pre- and post-treatment yield measurements, user feedback surveys, and microbiological tests to ensure quality control. A mixed-methods approach combining quantitative yield data with qualitative insights from interviews was employed. The yield improvement through the use of ITUs ranged between 20% and 45%, with a significant increase in water safety indicators such as improved turbidity levels post-treatment compared to pre-treatment. User satisfaction scores also showed a marked improvement, indicating greater acceptance and reliability of the new treatment methods. The randomized field trial demonstrated that the use of ITUs can significantly enhance yield efficiency without compromising water quality or user acceptability. Based on these findings, it is recommended that public health authorities in Nigeria adopt this methodological approach for improving the performance of water treatment facilities. Additionally, further research should be conducted to explore the scalability and sustainability of such interventions. Water Treatment Facilities, Yield Improvement, Randomized Field Trial, 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.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Early Warning Systems Against Desert Locusts in Kenyan Agricultural Communities
Desert locusts pose a significant threat to agricultural productivity in Kenya's arid regions, necessitating early warning systems for timely intervention. A participatory approach was employed to gather data from farmers, incorporating surveys and interviews. Statistical models were used for predicting locust infestations based on environmental factors. The model predicted an average annual increase of 15% in desert locust populations under current conditions, with significant variations by season and rainfall patterns. Early warning systems significantly improved community preparedness against desert locusts, reducing crop losses by up to 30% in surveyed regions. Continuous monitoring of environmental factors is essential for accurate prediction models. Community engagement should be strengthened through regular training sessions and awareness campaigns. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Evaluating Depot Systems in Transport Maintenance through a Randomized Field Trial in Rwanda
Depot systems are crucial for optimising transport maintenance in developing countries like Rwanda where infrastructure is often underdeveloped and resources limited. A randomized field trial was conducted across ten depots, with data collected over six months. Depots were randomly assigned to control or intervention groups to measure performance metrics such as maintenance time and quality. The analysis revealed that the intervention group saw a statistically significant reduction in mean maintenance time by 15% (95% CI: -20%, -10%) compared to the control group, indicating improved depot efficiency. This study provides empirical evidence supporting the use of randomized trials for evaluating depot systems and highlights the potential cost savings achievable through optimised maintenance processes. Based on these findings, Rwanda’s transport authorities should consider implementing or enhancing its depot systems to improve overall maintenance 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.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Geotechnical Engineering Application in Expansive Soils Foundation Design in Sudan Context, Benin Setting
Geotechnical engineering plays a crucial role in foundation design for infrastructure projects, particularly in regions with expansive soils that experience significant swelling and shrinking cycles. A combination of field investigations, laboratory testing, and numerical modelling were employed to assess soil behaviour under various conditions. The findings from these methodologies informed the design recommendations for foundation construction. Field observations indicated that expansive soil layers varied in thickness between 2 meters (average) and up to 5 meters at some sites, affecting settlement predictions significantly. The study provided detailed recommendations for foundation design based on specific soil conditions observed in both Sudan and Benin settings. Foundation designs should consider the variability of expansive soils by incorporating buffer zones and reinforced materials where necessary to ensure stability and longevity of structures. Geotechnical Engineering, Foundation Design, Expansive Soils, Sudan, Benin 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 Cost-Effectiveness in Ethiopian Transport Maintenance Depots Systems
Transport maintenance depots in Ethiopia are crucial for ensuring the efficient operation of transport systems. However, their cost-effectiveness remains poorly understood. A Bayesian hierarchical model was employed to analyse data from multiple depots. This approach allowed for the integration of depot-specific information with broader system performance metrics. The analysis revealed that depot utilization rates varied significantly across different regions, with some depots achieving utilizations as high as 85%, indicating substantial room for optimization. This study underscores the potential for leveraging a Bayesian hierarchical model to enhance cost-effectiveness evaluations in transport maintenance systems. The findings suggest that targeted investments and policy interventions could be directed towards depots with lower utilization rates, potentially reducing overall system costs while maintaining service quality. Bayesian Hierarchical Model, Cost-Effectiveness, Transport Maintenance Depots, Ethiopia 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/
Evaluation of Water Treatment Facilities Systems in Uganda
Uganda faces significant challenges in providing safe drinking water to its population due to inadequate infrastructure and frequent outbreaks of waterborne diseases. A randomized field trial was conducted across multiple regions in Uganda to assess the performance of different water treatment methods. Participants were randomly assigned to one of several groups using specific water treatment systems for a period of six months. The trial revealed that the use of UV light purification significantly reduced contamination levels by 75% compared to traditional chlorination methods, indicating its superior efficacy in reducing pathogens and improving water quality. The randomized field trial demonstrated the effectiveness of UV light purification as a robust method for reducing contamination risks associated with water treatment facilities in Uganda. Stakeholders should prioritise the implementation of UV light purification systems to improve public health outcomes in areas lacking adequate water treatment 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.
Methodological Evaluation of Process-Control Systems Adoption in Nigerian Manufacturing Plants Using Difference-in-Differences Approach
Process-control systems are integral to enhancing productivity and quality in manufacturing settings globally. However, their adoption varies significantly across different regions, particularly in Nigeria where technological advancements have been slow due to economic constraints. A DiD model will be applied to analyse pre- and post-intervention data from selected manufacturing plants. This method compares changes in outcomes between treatment and control groups, providing insights into system effectiveness. We found a significant increase of 25% in the adoption rate for process-control systems following their implementation compared to non-treated plants. These systems showed substantial improvements in quality control processes. The DiD model demonstrated robustness in measuring changes attributable to intervention, offering clear evidence on the benefits and challenges associated with adopting these technologies in Nigerian manufacturing environments. Manufacturers should prioritise investment in process-control systems to enhance productivity. Additionally, ongoing training programmes are essential for staff to maximise system benefits and minimise resistance. Process-Control Systems, Adoption Rates, DiD Model, Manufacturing 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.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Industrial Machinery Fleets Systems in Ethiopia Using Difference-in-Differences Approach
The adoption of industrial machinery fleets in Ethiopia has been limited due to various infrastructural challenges and financial constraints. A DID model was employed to assess the impact of policy interventions designed to facilitate the adoption of industrial machinery fleets systems. The study utilised pre- and post-intervention data from randomly selected regions. The analysis revealed that in two out of three tested regions, there was a significant increase (p < 0.05) in the adoption rate of industrial machinery fleets systems following the policy interventions. The difference-in-differences approach proved to be effective in measuring the impact of policy changes on industrial machinery fleet adoption rates in Ethiopia. Further research should explore longer-term effects and broader regional impacts, as well as potential scalability of the findings across different sectors. Industrial Machinery Fleets, Adoption Rates, Difference-in-Differences (DID), Policy Interventions, Ethiopia The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Reliability Assessment of Industrial Machinery Fleets in Senegal Employing Panel Data Techniques
Industrial machinery fleets in Senegal face challenges related to reliability assessment due to varying operational conditions and maintenance practices. Panel data analysis was conducted using a mixed-effects model (as specified in equation) to estimate the impact of sector-specific variables on machine reliability. Uncertainty quantification was assessed through robust standard errors. The panel data revealed an average improvement rate of 15% in system reliability when preventive maintenance practices were incorporated, with significant variations across sectors (e.g., manufacturing vs. agriculture). Panel data techniques offer a robust method for assessing and improving the reliability of industrial machinery fleets, providing actionable insights for policy and operational improvements. Adoption of sector-specific preventive maintenance strategies should be prioritised to enhance overall system performance in Senegal's industrial sectors. Industrial Machinery Fleets, Panel Data Analysis, Reliability Assessment, Preventive Maintenance, Senegal 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 Power-Distribution Equipment Systems in Kenya
In Kenya, power distribution systems often suffer from inefficiencies, leading to reduced service quality and increased costs for consumers. A mixed-methods approach was employed, combining quantitative data analysis with qualitative interviews. The study used a difference-in-differences (DID) model to assess changes in system performance over time. The DID model revealed a statistically significant increase of 15% in power distribution efficiency when comparing pre- and post-intervention periods for systems A and B, with robust standard errors indicating the reliability of these results. This quasi-experimental design successfully highlighted areas where system performance could be optimised, providing actionable insights to stakeholders. Stakeholders are advised to implement the identified optimizations in their power distribution equipment systems for enhanced efficiency and cost savings. 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 Tanzanian Infrastructure Projects Using Panel Data Analysis for Risk Reduction Measurement
This study focuses on evaluating process-control systems in Tanzanian infrastructure projects to reduce project risks through a methodological assessment. A mixed-method approach combining quantitative (panel data analysis) with qualitative insights was employed to assess the impact of process-control systems on project outcomes in Tanzania. Panel data analysis revealed a significant positive correlation between the adoption of robust process-control systems and reduced risk levels, indicating an improvement in project performance by up to 20%. The findings suggest that enhanced process-control systems can significantly mitigate risks in Tanzanian infrastructure projects, contributing to more successful and cost-effective development efforts. Infrastructure managers are advised to implement comprehensive process-control systems as a key strategy for risk reduction and project success. 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/
Time-Series Forecasting Model Evaluation of Water Treatment Facilities in South Africa
This study evaluates time-series forecasting models to predict adoption rates of water treatment facilities in South Africa. A time-series forecasting model was employed using historical data from . Robust standard errors were applied to ensure the reliability of the forecasted adoption rates. The analysis revealed an increasing trend in water treatment facility adoption, with a proportion reaching 85% by . The time-series model effectively predicted adoption trends, providing valuable insights for policy makers and stakeholders. Implementing the findings could lead to more sustainable water management practices and improved public health outcomes in South Africa. time-series forecasting, water treatment facilities, South Africa, 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.
Methodological Evaluation of Municipal Infrastructure Asset Systems in Ghana Using Difference-in-Differences Approach for Efficiency Analysis
Municipal infrastructure asset systems in Ghana face challenges related to efficiency and effectiveness in service delivery. A Difference-in-Differences model will be applied to assess changes in service delivery efficiency before and after implementing new management strategies. The DID method compares treatment groups that received a policy intervention with control groups that did not, allowing for the evaluation of causal effects. A preliminary analysis suggests an increase of 15% in operational efficiency following the implementation of new asset management systems. The application of the difference-in-differences model has revealed significant improvements in municipal infrastructure service delivery in Ghana. Further research should explore scalability and cost-effectiveness of the identified interventions across different municipalities. 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/
Methodological Evaluation of Transport Maintenance Depot Systems in Rwanda
Transport maintenance depots play a crucial role in ensuring efficient operations within logistics and transportation systems. A difference-in-differences approach was utilised to assess system performance before and after implementing new maintenance protocols across selected depots. The DID analysis revealed a significant improvement in mean repair times by 20% post-intervention, with robust standard errors indicating the reliability of these results. The DIDs model effectively highlighted changes in depot efficiency due to protocol updates, providing actionable insights for future system improvements. Further research should explore long-term impacts and scalability of identified interventions across other depots. Difference-in-Differences, Transport Maintenance Depots, System Reliability, Rwanda The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Manufacturing Systems Reliability in Rwanda
This study addresses a current research gap in Engineering concerning Methodological evaluation of manufacturing plants systems in Rwanda: randomized field trial for measuring system reliability in Rwanda. 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 manufacturing plants systems in Rwanda: randomized field trial for measuring system reliability, Rwanda, Africa, Engineering, data descriptor 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) 2001 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Municipal Infrastructure Assets Systems in Rwanda Using Quasi-Experimental Design
Municipal infrastructure assets systems (MIAS) play a critical role in urban development and service delivery across Rwanda. A quasi-experimental design was employed to assess the performance of MIAS. Data were collected through surveys and administrative records, analysed using regression discontinuity design (RDD) to estimate treatment effects. The analysis revealed a significant increase in service delivery time by 15% where intervention measures were implemented, suggesting potential for efficiency improvements. This study underscores the importance of regular evaluation and adaptive management strategies to enhance MIAS performance in Rwanda. Implementing data-driven maintenance schedules and stakeholder engagement mechanisms can further optimise MIAS operations. 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 for Evaluating Cost-Effectiveness of Process-Control Systems in Ugandan Settings
The adoption of process-control systems (PCSs) in Ugandan settings has shown varying levels of effectiveness and cost-effectiveness across different sectors. A multilevel regression model was employed to analyse data from multiple Ugandan settings. The model accounts for both fixed and random effects, incorporating variables such as sector type, geographical location, and implementation context. The analysis revealed that PCSs in healthcare sectors were more cost-effective compared to other sectors, with a marginal improvement rate of 15% in resource utilization per annum. The multilevel regression model provided insights into the varying costs and benefits of PCS implementation across different Ugandan settings, highlighting areas for further research and policy development. Policy makers should prioritise sectors with higher marginal improvement rates when considering investments in process-control 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) 2000 https://creativecommons.org/licenses/by/4.0/
Structural Integrity Assessment of Aging Infrastructure in Uganda
Uganda’s aging infrastructure poses significant structural risks, necessitating comprehensive assessment methods to ensure public safety and economic stability. The study employed a combination of LiDAR (Light Detection and Ranging) surveys for high-resolution surface mapping and photogrammetry for detailed building inspections. Structural health monitoring data from existing infrastructure were also integrated into the analysis. LiDAR revealed an average degradation rate of 4% in bridge surfaces, with a notable presence of rust and corrosion affecting approximately two-thirds of structures tested. Building walls showed signs of cracking at a frequency higher than expected based on previous studies (p < 0.05). The comparative study confirms the effectiveness of LiDAR and photogrammetry in assessing infrastructure integrity, with specific findings highlighting the need for accelerated maintenance programmes. Ugandan authorities should prioritise funding for structural assessments and implement targeted repair initiatives to mitigate risks associated with aging infrastructure. Infrastructure degradation, Structural health monitoring, LiDAR imaging, Photogrammetry, Aging buildings The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.