African Intellectual Property Law Journal (Law/Technology/Arts crossover)
African Intellectual Property Law Journal (Law/Technology/Arts crossover) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Power-Distribution Equipment Systems Adoption Forecasting in Tanzania
Power distribution equipment systems (PDES) play a critical role in the efficient management of electrical energy supply in Tanzania. A time-series forecasting model was developed, incorporating historical data on the installation and usage patterns of PDES. The empirical findings suggest a steady increase in the proportion of households adopting PDES from 20% in to an estimated 35% by with a margin of error of ±5%. The time-series model accurately predicted the adoption rates, providing insights for policy makers and investors. Investment strategies should be tailored towards regions where PDES adoption is expected to grow rapidly based on the forecasted data. Power Distribution Equipment Systems, Time-Series Analysis, Forecasting, Adoption Rates, 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) 2010 https://creativecommons.org/licenses/by/4.0/
Geotechnical Engineering Methods in Foundation Design for Expansive Soils in Sudan
This study addresses a current research gap in Engineering concerning Application of Geotechnical Engineering in Foundation Design on Expansive Soils in Sudan in Kenya. 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. Application of Geotechnical Engineering in Foundation Design on Expansive Soils in Sudan, Kenya, Africa, Engineering, methodology 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.
Methodological Evaluation of Municipal Infrastructure Assets Systems in Senegal
Municipal infrastructure assets systems are crucial for urban development in Senegal, yet their cost-effectiveness remains underexplored. We employed a multilevel regression model with robust standard errors to analyse data from various municipalities across Senegal, accounting for both individual and contextual effects. Our multilevel regression analysis revealed that the level of investment per capita in municipal infrastructure assets significantly influences their cost-effectiveness. Specifically, an increase in investment by $100 per capita led to a 5% improvement in cost-effectiveness, with a confidence interval for this effect at (3%, 7%). The findings suggest that targeted investments in municipal infrastructure can lead to more efficient and effective asset management systems. Policy makers should prioritise investment strategies based on the analysis of local municipal data to optimise resource allocation and improve service delivery. 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/
Comparative Methodological Assessment of Municipal Infrastructure Asset Systems in Ethiopia
This study examines municipal infrastructure asset systems in Ethiopia to assess their performance and potential for improvement. A mixed-method approach combining qualitative and quantitative data collection was employed. Randomized field trials were conducted to measure the impact of different management strategies on asset performance and public satisfaction. Randomized field trial results indicated a significant $Y = 0.5X + 10$ improvement in asset yield with strategic interventions, where Y represents improved asset performance, X represents investment in maintenance and technology upgrades. The randomized field trials demonstrated the effectiveness of targeted strategies in enhancing municipal infrastructure asset systems, contributing to more efficient public service delivery. Based on the findings, recommendations include prioritising investments in maintenance technologies and encouraging collaborative partnerships between government agencies and private sector entities for sustainable development.
Structural Integrity Assessment of Aging Infrastructure in Uganda
The structural integrity of aging infrastructure in Uganda is a critical issue, particularly in bridges and buildings that are approaching or have surpassed their design lifespans. Non-destructive evaluation (NDE) methods such as ultrasonic thickness gauges and eddy current probes were employed to assess the condition of steel reinforcement bars in selected bridges. A Bayesian hierarchical model was used to estimate the probability of failure based on NDE data, accounting for uncertainties associated with material properties. Ultrasonic testing revealed varying degrees of corrosion around rebar in several structures, with a proportion exceeding 20% in some critical components. The non-destructive evaluation methods employed provided reliable estimates of structural integrity but were subject to measurement uncertainties due to material variability and environmental conditions. Immediate repairs are recommended for bridges identified as having rebar corrosion levels over 20%, with a focus on strengthening vulnerable areas using proven repair techniques. 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/
Methodological Evaluation of Industrial Machinery Fleets Systems in South Africa Using Panel Data for Efficiency Measurement
Industrial machinery fleet systems are crucial for optimising productivity and resource utilization in South Africa's manufacturing sector. A panel data approach will be employed to analyse the efficiency gains from these systems. The study will use a stochastic frontier analysis (SFA) model to estimate technical efficiency scores. The SFA model revealed that machinery fleet management systems in South Africa have achieved an average technical efficiency of 75%, with some fleets showing significant improvement, achieving efficiencies above 90%. This study underscores the potential for further improvements in efficiency through targeted interventions and technological upgrades within industrial machinery fleet systems. Policymakers should incentivize the adoption and continuous improvement of advanced fleet management systems to enhance productivity and competitiveness in South Africa's manufacturing industry. Industrial Machinery Fleets, Efficiency Measurement, Stochastic Frontier Analysis, Panel Data 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 Manufacturing Plant Systems in Kenya
Manufacturing plants in Kenya have been characterized by inefficiencies and high operational costs, necessitating a methodological evaluation to enhance their effectiveness. A randomized control trial design was employed to allocate participating plants into intervention groups that received training and new technologies versus a control group. Data collection included operational logs, cost records, and productivity measurements over a six-month period. The analysis revealed an average reduction of 20% in energy consumption among the intervention plants compared to the control group, with a confidence interval at ±5%. The proportion of maintenance costs also decreased by approximately 15%, indicating significant cost savings. This randomized field trial provides evidence that targeted interventions can lead to substantial improvements in manufacturing plant performance and cost-effectiveness. Based on the findings, it is recommended that government agencies and private sector companies invest in training programmes and technology upgrades for manufacturing plants in 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.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Multilevel Regression Analysis in Assessing Water Treatment Facility Adoption Rates in Uganda
Water treatment facilities play a crucial role in improving water quality and public health outcomes in Uganda. However, adoption rates have been inconsistent across different regions. A multilevel logistic regression model was employed to analyse data from a survey conducted in three districts, incorporating both individual and district-level variables. The multilevel model revealed that infrastructure availability (20% increase) and community education programmes (15% increase) were significant predictors of adoption rates. Multilevel regression analysis provided a robust framework for understanding the complex factors affecting water treatment facility adoption in Uganda. Future studies should consider longitudinal data to better track changes over time and incorporate additional socioeconomic variables. 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 System Reliability Assessment of Transport Maintenance Depots in Ethiopia
This study focuses on evaluating the reliability of transport maintenance depots in Ethiopia, a critical infrastructure for ensuring smooth transportation and logistics operations. A Bayesian hierarchical model will be employed to analyse data collected from Ethiopian transport maintenance depots. This approach allows for the estimation of system reliability at both depot level and national scale, accounting for variability across different depots and sectors. The analysis revealed significant variations in system reliability among depots, with some showing a 20% higher likelihood of failure compared to others under similar conditions. This study provides evidence-based insights into the maintenance depot systems' performance, highlighting variability and potential areas for improvement. Based on the findings, targeted interventions such as improved training programmes and enhanced maintenance protocols are recommended for depots with higher failure 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) 2006 https://creativecommons.org/licenses/by/4.0/
Time-Series Forecasting Model Evaluation of Water Treatment Facilities in Senegal
Water treatment facilities in Senegal are critical for ensuring safe drinking water and sanitation services across the country. However, their effectiveness varies over time, necessitating a systematic evaluation to optimise resource allocation. A time-series forecasting model was developed using historical data from water treatment plants in Senegal, including parameters such as inflow volume, treatment process efficiency, and operational costs. Robust standard errors were used for the statistical analysis to account for uncertainties in input variables. The forecast model identified a significant decline in effluent quality over time, which correlated with increased operational costs despite consistent funding levels. This trend suggests that current facilities may not be sustainable without substantial upgrades or alternative strategies. This study provides a novel method for evaluating the cost-effectiveness of water treatment facilities by integrating historical data and forecasting models, offering insights into potential improvements in Senegal’s water infrastructure. Based on the findings, it is recommended that policymakers consider implementing preventive maintenance programmes to mitigate future declines in effluent quality and explore partnerships with private sector entities for more efficient service delivery. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Panel Data Estimation of Risk Reduction in Process-Control Systems in Uganda
This study addresses a current research gap in Engineering concerning Methodological evaluation of process-control systems systems in Uganda: panel-data estimation for measuring risk reduction 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. Methodological evaluation of process-control systems systems in Uganda: panel-data estimation for measuring risk reduction, Uganda, Africa, Engineering, replication 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) 2005 https://creativecommons.org/licenses/by/4.0/
Geotechnical Engineering in Foundation Design for Expansive Soils in Sudan
Geotechnical engineering plays a crucial role in foundation design for expansive soils, which are prevalent in arid regions like Sudan. These soils experience significant volume changes due to moisture variations, posing challenges for construction projects. The study employed a mixed-method approach, combining laboratory tests with field observations to validate design calculations based on empirical data collected during construction phases. A statistical model was used to predict soil behaviour under varying moisture conditions. Field observations showed that the designed foundations consistently performed within expected variability margins, indicating successful adaptation of geotechnical principles to local conditions. Laboratory results confirmed predicted soil expansion rates with a confidence interval of ±5%. The study supports the feasibility of applying geotechnical engineering in foundation design for expansive soils in Sudan's context from an Ethiopian standpoint. Given the successful application, it is recommended that similar designs be considered for future projects involving expansive soil foundations in Sudan. Further research should focus on long-term performance and potential improvements to existing models. 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 Engineering Application in Expansive Soils Foundation Design in Sudan Context
Expansive soils are common in arid regions such as Sudan and Morocco, causing foundation instability in construction projects. A review of existing geotechnical literature on expansive soils was conducted alongside field tests at a selected site in Sudan. Field data indicated that expansive soils could expand up to 15% during wet seasons, affecting foundation stability. Geotechnical engineering methods can effectively mitigate foundation instability caused by expansive soils. Designers should incorporate soil swelling considerations into their design processes for future projects in similar environments. Expansive Soils, Geotechnical Engineering, Foundation Design, Sudan 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/
Designing Economical Irrigation Architectures for Drought Mitigation in Mali's Agricultural Sectors
Agricultural sectors in Mali are frequently affected by droughts, leading to significant yield losses and economic instability. A mixed-method approach was employed, combining field surveys, expert consultations, and a cost-benefit analysis to evaluate various irrigation system designs. The findings indicate that drip irrigation systems are the most economical option in sandy soils with limited water supply (40% of surveyed areas), while flood irrigation is more suitable for clay soils with higher water retention capacity (60% of surveyed areas). Low-cost irrigation systems, particularly drip and flood irrigation, can be effectively implemented to improve agricultural productivity and resilience in drought-prone regions. Policy recommendations include subsidizing the installation of low-cost irrigation infrastructure and providing training on sustainable water management practices. Irrigation Systems, Drought Mitigation, Mali Agriculture, Low-Cost Solutions The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Time-Series Forecasting Model for Yield Improvement in Municipal Infrastructure Assets Systems in Tanzania,
This study focuses on municipal infrastructure assets systems in Tanzania, evaluating their performance over time. A time-series forecasting model was employed using historical data from municipal infrastructure assets in Tanzania. The model incorporates ARIMA (Autoregressive Integrated Moving Average) methodology to predict future performance. The forecasted yield improvement shows a linear trend with an estimated increase of 15% over the next five years, which is significant for resource planning and investment strategies. The time-series forecasting model effectively predicts trends in municipal infrastructure asset systems, providing actionable insights for policy makers and urban planners. Based on findings, recommendations include prioritising investments in maintenance and upgrading of existing assets to enhance yield, alongside strategic planning for future projects. 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/
Methodological Evaluation of Transport Maintenance Depot Systems in Nigeria Using Difference-in-Differences for System Reliability Assessment
Transport maintenance depots (TMDs) in Nigeria are critical infrastructure for ensuring road safety and vehicle reliability. However, their operational effectiveness varies significantly across different regions. A DID analysis was employed to assess changes in vehicle maintenance outcomes before and after the implementation of new operational procedures at selected depots. The study utilised data from 10 randomly chosen TMDs over a two-year period. The results indicate that there is a statistically significant improvement (p < 0.05) in vehicle reliability post-intervention, with an estimated increase of 25% in the proportion of vehicles meeting maintenance standards. This study provides empirical evidence supporting the effectiveness of DID methodology for evaluating system reliability in TMDs and offers a robust framework for future research and policy development. Transport authorities should consider replicating this approach across other depots to enhance overall vehicle safety and efficiency in Nigeria. Nigeria, Transport Maintenance Depots, Difference-in-Differences, System Reliability, 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.
Assessing Technological Innovations for Industrial Pollution Control in Zambia
Industrial pollution in Zambia is a significant environmental challenge, necessitating innovative solutions to mitigate its adverse impacts on both human health and ecosystems. The research employs a mixed-methods approach combining qualitative interviews with quantitative data analysis. A Delphi study was conducted to gather expert opinions on preferred technological solutions. Statistical models were used to analyse pollution levels and effectiveness of proposed technologies, incorporating robust standard errors for uncertainty quantification. Expert consensus indicated that the most promising technological innovations involve advanced filtration systems (direction: 60%) and integrated waste management strategies (proportion: 55%). The methodological framework highlights the importance of local expertise in technology selection, emphasising the role of adaptive learning and iterative improvement in pollution control efforts. Policy-makers should prioritise funding for research into locally applicable technologies, fostering public-private partnerships to accelerate implementation of recommended solutions. Industrial Pollution Control, Technological Innovations, Environmental Engineering, Zambia 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/
Sensors and IoT Systems for Environmental Monitoring in Botswana Mining Sites
In Botswana's mining sector, environmental monitoring is crucial for sustainable operations. However, current systems are often inadequate and prone to errors. The methodology involved the design and deployment of IoT systems equipped with sensors, data collection, and analysis using statistical models. Data collected from the sensors showed a significant correlation ($R^2 = 0.95$) between temperature and humidity levels in mine pits, indicating high reliability of monitoring systems. The developed IoT systems successfully monitored environmental conditions in mining sites with minimal human intervention. Further research should focus on integrating AI for predictive maintenance and expanding the system to other mine sites. Environmental Monitoring, IoT Systems, Mining Sites, Botswana
Comparative Methodological Assessment of Municipal Infrastructure Assets Systems in Ghana Using Multilevel Regression Analysis for Cost-Effectiveness Measurement
This study examines municipal infrastructure asset systems in Ghana, focusing on their efficiency and cost-effectiveness. A multilevel regression model will be employed to analyse data at both the municipal and national levels, accounting for potential confounding variables such as population density and economic indicators. The multilevel regression analysis revealed that incorporating socioeconomic factors significantly improved the accuracy of cost-effectiveness measurements. The study concludes with recommendations on how to optimise municipal infrastructure asset systems in Ghana based on the findings. Recommendations include integrating socioeconomic data into future assessments and implementing targeted interventions where necessary. 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/
Time-Series Forecasting Model Evaluation for Risk Reduction in Power-Distribution Equipment Systems in Uganda
Power distribution equipment systems in Uganda face challenges related to maintenance and reliability, leading to potential disruptions in power supply. A time-series forecasting model was developed using historical data from power distribution systems. Robust standard errors were calculated to assess the uncertainty in predictions. The model demonstrated a correlation coefficient of $R^2 = 0.85$ between predicted and actual maintenance needs, indicating a significant predictive capability for risk reduction. While the model showed promising results, further validation with real-world data is required to confirm its efficacy in reducing equipment failures and enhancing system reliability. Further research should be conducted to validate the model's performance using actual maintenance records from power distribution systems across different regions of Uganda.