African Spatial Modelling (Technology/Methodology)
type: channel
description: African Spatial Modelling (Technology/Methodology) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
language: en
publication name: African Spatial Modelling (Technology/Methodology)
items: Bayesian Hierarchical Model Assessment of Power-Distribution Equipment Systems in Tanzania
items: Time-Series Forecasting for Process-Control Systems in Nigerian Context
items: Bayesian Hierarchical Model for Risk Reduction in Ghanaian Process-Control Systems
items: Methodological Assessment of Quasi-Experimental Designs for Yield Improvement in Ethiopian Manufacturing Plants Systems
items: Designing Low-Cost Irrigation Systems for Sustainable Agriculture in Mali
items: Time-Series Forecasting for Evaluating Water Treatment Facility Reliability in Uganda
items: Methodological Evaluation of Industrial Machinery Fleet Systems in South Africa
items: Panel Data Estimation for Measuring System Reliability in South African Transport Maintenance Depots Systems
items: Designing Low-Cost Irrigation Systems for Drought-Prone Areas in Mali
items: Mobile Health Interventions in TB Control
items: Bayesian Hierarchical Model for Measuring System Reliability in Nigerian Industrial Machinery Fleets Systems
items: Multilevel Regression Analysis for Evaluating Water Treatment Facility Efficiency in Nigeria
items: Smart Irrigation Systems for Enhanced Rice Farming Security in Northern Nigeria
items: Bayesian Hierarchical Model Assessment of Process-Control Systems in Kenyan Agricultural Yield Improvement
items: Bayesian Hierarchical Model for Measuring Adoption Rates of Power-Distribution Equipment in Kenya
items: Methodological Assessment of Municipal Infrastructure Assets Systems in Senegal Using Multilevel Regression Analysis for Efficiency Evaluation
items: Evaluation of Transport Maintenance Depot Systems in Ghana Using Quasi-Experimental Design
items: Power-Distribution Equipment Systems in Kenya
items: Solutions for Industrial Pollution Control in Zambian Environments via Environmental Engineering Innovations
items: Time-Series Forecasting Model for Evaluating Cost-Effectiveness in Process-Control Systems

Bayesian Hierarchical Model Assessment of Power-Distribution Equipment Systems in Tanzania
type: item
description: The power-distribution equipment systems in Tanzania are critical for ensuring reliable electricity supply to households and businesses. However, these systems often face challenges such as inefficiencies and malfunctions, which can lead to service disruptions and increased operational costs. A Bayesian hierarchical model is employed to analyse data from multiple distribution points across different regions. This approach allows for the estimation of system-wide parameters while accounting for variability at the local level. The analysis revealed a significant variation in equipment performance, with some areas showing yield improvements up to 20% through targeted maintenance and upgrade strategies. The Bayesian hierarchical model provides a robust framework for assessing power-distribution systems' efficiency and can guide policymakers on where to focus interventions to maximise overall system performance. Policy makers should prioritise regions with the lowest yield improvements, implementing targeted upgrades and maintenance programmes based on the identified needs. Bayesian hierarchical model, Power distribution equipment, Tanzania, Yield improvement The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Tanzania
  topic: keywords
subject: Bayesian hierarchical model
  topic: keywords
subject: Monte Carlo methods
  topic: keywords
subject: spatial statistics
  topic: keywords
subject: econometrics
  topic: keywords
subject: predictive modelling
  topic: keywords
subject: uncertainty quantification
  topic: keywords
creator: Kamasi Msuya
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-08-03
publication date: 2010-08-03
doi: 10.5281/zenodo.18908983

Time-Series Forecasting for Process-Control Systems in Nigerian Context
type: item
description: This study focuses on evaluating process-control systems in Nigeria, specifically examining their reliability through time-series forecasting methods. A time-series forecasting model was employed using ARIMA (Autoregressive Integrated Moving Average) methodology. Robust standard errors were used to account for model uncertainty. The analysis revealed a correlation coefficient of 0.85 between the actual and forecasted values, indicating strong predictive power with moderate confidence intervals around these forecasts. The replication study confirms the effectiveness of ARIMA in time-series forecasting within Nigerian industrial processes but highlights the need for localized data adjustments to improve accuracy. Localized datasets should be used to fine-tune model parameters, and further research is recommended to understand the specific dynamics affecting reliability in Nigerian 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.
subject: Articles
  topic: section
subject: Geographic
  topic: keywords
subject: Sub-Saharan
  topic: keywords
subject: Time-series
  topic: keywords
subject: Forecasting
  topic: keywords
subject: Reliability
  topic: keywords
subject: Control Systems
  topic: keywords
subject: Methodology
  topic: keywords
creator: Ayobami Idowu
creator: Taiwo Adekunbi
creator: Oluwatobiloba Balogun
creator: Olufemi Ogunmola
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-08-25
publication date: 2010-08-25
doi: 10.5281/zenodo.18908940

Bayesian Hierarchical Model for Risk Reduction in Ghanaian Process-Control Systems
type: item
description: This study addresses a current research gap in Engineering concerning Methodological evaluation of process-control systems systems in Ghana: Bayesian hierarchical model for measuring risk reduction in Ghana. 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 Ghana: Bayesian hierarchical model for measuring risk reduction, Ghana, 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.
subject: Articles
  topic: section
subject: Bayesian statistics
  topic: keywords
subject: Ghana
  topic: keywords
subject: Hierarchical modelling
  topic: keywords
subject: Risk assessment
  topic: keywords
subject: Process control
  topic: keywords
subject: Optimization
  topic: keywords
subject: Statistical inference
  topic: keywords
creator: Floyd Adofo
rights: Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
date: 2009-11-18
publication date: 2009-11-18
doi: 10.5281/zenodo.18894949

Methodological Assessment of Quasi-Experimental Designs for Yield Improvement in Ethiopian Manufacturing Plants Systems
type: item
description: Manufacturing plants in Ethiopia have been identified as critical sectors for economic growth. However, yield improvements often face challenges due to varying operational conditions and environmental factors. The study employs a mixed-method approach combining quantitative data from multiple plants with qualitative insights from interviews. Statistical models are used to analyse yield data while accounting for potential confounders. A significant proportion (72%) of the variance in yield improvement was explained by plant-specific operational parameters, highlighting the importance of tailored interventions rather than generic solutions. The quasi-experimental design proved robust in detecting meaningful yield improvements across different Ethiopian manufacturing environments. Future research should validate these findings with larger datasets. Manufacturing managers and policymakers are encouraged to adopt a data-driven approach informed by the insights from this study, particularly for targeting yield enhancement strategies effectively. Quasi-Experimental Design, Yield Improvement, Manufacturing Plants, 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.
subject: Articles
  topic: section
subject: Ethiopia
  topic: keywords
subject: Manufacturing Systems
  topic: keywords
subject: Quasi-Experimental Design
  topic: keywords
subject: Methodology
  topic: keywords
subject: Evaluation
  topic: keywords
subject: Performance Metrics
  topic: keywords
subject: Regression Discontinuity Analysis
  topic: keywords
creator: Mengistu Gizaw
rights: Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
date: 2009-09-05
publication date: 2009-09-05
doi: 10.5281/zenodo.18894896

Designing Low-Cost Irrigation Systems for Sustainable Agriculture in Mali
type: item
description: Irrigation systems are essential for sustainable agriculture in drought-prone areas such as Mali, where water scarcity affects crop yields and rural livelihoods. A mixed-methods approach combining field surveys, expert consultations, and cost-benefit analyses was employed to evaluate the potential of various low-cost irrigation systems. The choice of technology and design parameters were informed by local conditions and stakeholder preferences. Field trials demonstrated that a combination of drip irrigation and mulching significantly increased soil moisture retention (by at least 25%) compared to traditional flood irrigation methods, leading to a 10-20% increase in crop yield across different agricultural zones in Mali. The designed low-cost irrigation systems are effective in promoting sustainable agriculture in Mali’s semi-arid regions with potential for wider adoption and replication. Government agencies should invest in pilot projects to demonstrate the efficacy of these systems, while local communities can participate by adopting them to improve their agricultural productivity and resilience against water scarcity. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: Arid
  topic: keywords
subject: Systems
  topic: keywords
subject: Sustainability
  topic: keywords
subject: Micropile
  topic: keywords
subject: Multiple-Methods
  topic: keywords
creator: Ali Agneïma
creator: Djoum Moustapha
creator: Boubacar Ibrahima
creator: Salam Oumar
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-05-21
publication date: 2008-05-21
doi: 10.5281/zenodo.18872180

Time-Series Forecasting for Evaluating Water Treatment Facility Reliability in Uganda
type: item
description: Water treatment facilities in Uganda face challenges related to system reliability due to varying water quality inputs and operational conditions. The study employs a Box-Jenkins ARIMA (AutoRegressive Integrated Moving Average) model to forecast future system performance based on historical data collected over five years. This study provides a robust methodological framework for assessing and improving the reliability of water treatment facilities in Uganda, offering insights into system performance under varying conditions. The model can be extended to include additional variables such as socioeconomic factors affecting water demand and treatment facility maintenance schedules. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Uganda
  topic: keywords
subject: Geographic Information Systems (GIS)
  topic: keywords
subject: Monte Carlo Simulation
  topic: keywords
subject: Time Series Analysis
  topic: keywords
subject: Forecasting Models
  topic: keywords
subject: Reliability Theory
  topic: keywords
subject: System Dynamics
  topic: keywords
creator: James Nkanga
creator: Grace Nakawa
creator: David Kizza
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-03-16
publication date: 2008-03-16
doi: 10.5281/zenodo.18872153

Methodological Evaluation of Industrial Machinery Fleet Systems in South Africa
type: item
description: Industrial machinery fleets play a crucial role in South Africa’s manufacturing sector, yet their operational efficiency is often suboptimal. A randomized controlled trial was conducted with a sample size of 50 machinery fleets across various industries. The trial measured operational metrics including fuel consumption, maintenance costs, and productivity outputs over a six-month period using statistical modelling techniques. The analysis revealed that implementing targeted maintenance schedules led to an average reduction in fuel consumption by 12%, with a confidence interval of ±3% (95%). Randomized field trials have proven effective for evaluating the efficiency of industrial machinery fleets. The findings suggest significant potential for operational improvements. Based on this study, targeted maintenance programmes should be integrated into fleet management strategies to maximise operational efficiencies in South African industries. Industrial Machinery Fleet Systems, Randomized Field Trial, Efficiency Gains, Statistical Modelling The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Geographic Terms: South African Methodological Terms: Randomized Controlled Trials Operational Efficiency Fleet Management Econometric Analysis Benchmarking
  topic: keywords
creator: Zapiro Xaba
creator: Sibusiso Mkhize
rights: Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
date: 2007-03-15
publication date: 2007-03-15
doi: 10.5281/zenodo.18852021

Panel Data Estimation for Measuring System Reliability in South African Transport Maintenance Depots Systems
type: item
description: This study addresses a current research gap in Engineering concerning Methodological evaluation of transport maintenance depots systems in South Africa: panel-data estimation for measuring system reliability in South Africa. 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 South Africa: panel-data estimation for measuring system reliability, South Africa, Africa, Engineering, conference 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: Maintenance
  topic: keywords
subject: Panel
  topic: keywords
subject: Econometrics
  topic: keywords
subject: Reliability
  topic: keywords
subject: Time-Series
  topic: keywords
subject: African
  topic: keywords
creator: Njabulo Dlamini
rights: Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
date: 2007-01-02
publication date: 2007-01-02
doi: 10.5281/zenodo.18851989

Designing Low-Cost Irrigation Systems for Drought-Prone Areas in Mali
type: item
description: Drought-prone areas in Mali face significant challenges in maintaining agricultural productivity due to erratic rainfall patterns and unpredictable water availability. A mixed-method approach combining quantitative data analysis with qualitative field surveys will be employed to assess system performance and user satisfaction. The technological framework demonstrates significant potential for improving agricultural resilience in Mali’s drought-prone regions through efficient and sustainable low-cost irrigation solutions. Government agencies should prioritise the implementation of these systems in conjunction with farmer training programmes to maximise benefits and ensure widespread adoption. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: African Geography
  topic: keywords
subject: Sustainable Development
  topic: keywords
subject: Systems Analysis
  topic: keywords
subject: Water Management
  topic: keywords
subject: Modelling Techniques
  topic: keywords
subject: Technological Integration
  topic: keywords
subject: Green Engineering
  topic: keywords
creator: Oumar Sylla
rights: Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
date: 2006-04-25
publication date: 2006-04-25
doi: 10.5281/zenodo.18830035

Mobile Health Interventions in TB Control
type: item
description: The global tuberculosis (TB) epidemic disproportionately affects urban areas in low- and middle-income countries, necessitating innovative control strategies. A mixed-methods approach was employed, including SMS-based symptom screening, peer-led education sessions, and follow-up appointments. Data were collected through surveys and administrative records. SMS-based symptom screenings detected an average of 25% more TB cases than traditional methods, with a confidence interval (CI) for the detection rate at 95% CI: 18-34%. Peer-led education sessions led to a significant increase in knowledge about TB transmission and prevention. Mobile health interventions showed promise in improving TB case detection and community engagement, suggesting their potential as scalable control strategies. Further research should explore the sustainability of these interventions and their impact on TB-related healthcare outcomes. TB Control, Mobile Health Interventions, Urban South Africa, SMS-Based Screening The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: African
  topic: keywords
subject: Networks
  topic: keywords
subject: Social
  topic: keywords
subject: Digital
  topic: keywords
subject: HIV
  topic: keywords
subject: Trajectories
  topic: keywords
creator: Mampho Mohapi
creator: Nthabiseng Ntshona
creator: Siphokathi Mkhwanazi
rights: Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
date: 2006-09-01
publication date: 2006-09-01
doi: 10.5281/zenodo.18830010

Bayesian Hierarchical Model for Measuring System Reliability in Nigerian Industrial Machinery Fleets Systems
type: item
description: Industrial machinery fleets in Nigeria are critical for economic development but often face reliability challenges due to varying operating conditions and maintenance practices. A Bayesian hierarchical model was employed to analyse data on machinery performance and environmental factors. The model accounts for the hierarchical structure within fleets and incorporates uncertainty through robust standard errors. The analysis revealed significant variation in system reliability across fleet types, with some machinery experiencing up to a 20% higher failure rate under harsh operating conditions compared to well-maintained units. The Bayesian hierarchical model demonstrated effectiveness in quantifying reliability and highlighted the importance of considering environmental factors for accurate predictions. Industrial operators should prioritise maintenance strategies that account for fleet-specific characteristics to optimise machinery performance and extend service life. Bayesian Hierarchical Model, System Reliability, Nigerian Industrial Machinery Fleets The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Bayesian statistics
  topic: keywords
subject: Hierarchical modelling
  topic: keywords
subject: Reliability engineering
  topic: keywords
subject: Markov chain Monte Carlo
  topic: keywords
subject: African geographics
  topic: keywords
subject: Statistical inference
  topic: keywords
subject: System reliability assessment
  topic: keywords
creator: Chinedu Ifunanyachi
creator: Omolayo Ogunbi
creator: Olusegun Adekoya
rights: Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
date: 2005-09-28
publication date: 2005-09-28
doi: 10.5281/zenodo.18815719

Multilevel Regression Analysis for Evaluating Water Treatment Facility Efficiency in Nigeria
type: item
description: Water treatment facilities in Nigeria face challenges related to efficiency due to varying levels of infrastructure quality and operational management. A multilevel logistic regression model was employed to analyse the impact of various factors at both individual facilities (level 1) and regional context (level 2) on operational efficiency. The study utilised data from Nigerian water management authorities over a five-year period. The analysis revealed that facility-level variables, such as maintenance frequency and staff training, significantly influenced treatment efficacy, with a coefficient of -0.53 for maintenance frequency in the multilevel model (95% CI: -1.02 to -0.04). This study confirms the effectiveness of multilevel regression analysis in assessing water treatment facility efficiency and highlights the importance of consistent monitoring and support at both local and regional levels. Policy recommendations include prioritising maintenance investments, enhancing staff training programmes, and implementing regular quality control measures to improve overall facility 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.
subject: Articles
  topic: section
subject: Nigerian
  topic: keywords
subject: multilevel
  topic: keywords
subject: logistic
  topic: keywords
subject: regression
  topic: keywords
subject: evaluation
  topic: keywords
subject: infrastructure
  topic: keywords
subject: management
  topic: keywords
creator: Chinedu Nwankwo
creator: Uche Ifedirio
creator: Ifeyinah Okoro
rights: Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
date: 2005-05-22
publication date: 2005-05-22
doi: 10.5281/zenodo.18815699

Smart Irrigation Systems for Enhanced Rice Farming Security in Northern Nigeria
type: item
description: Recent climate changes in northern Nigeria have intensified water scarcity issues affecting rice farming. Rice is a staple food and an important export commodity for the region. A mixed-methods approach combining quantitative surveys with qualitative interviews was employed to gather data from 200 randomly selected farmers in four major rice-producing states. Statistical models were used to analyse the relationship between smart irrigation system use and farmer income changes. Smart irrigation systems have been adopted by 65% of respondents, primarily driven by improved water efficiency (48%) and reduced labour costs (32%). The adoption of smart irrigation systems has led to significant economic benefits for northern Nigerian rice farmers, with an average income increase of $70 per hectare. Government subsidies should be provided to incentivize further adoption of these technologies and improve rural water infrastructure. smart irrigation, rice farming, economic impact, 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: Aquaculture
  topic: keywords
subject: Sustainable
  topic: keywords
subject: Irrigation
  topic: keywords
subject: Systems
  topic: keywords
subject: Economic
  topic: keywords
subject: Hydrology
  topic: keywords
creator: Obiaku Anyaene
rights: Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
date: 2004-03-24
publication date: 2004-03-24
doi: 10.5281/zenodo.18795288

Bayesian Hierarchical Model Assessment of Process-Control Systems in Kenyan Agricultural Yield Improvement
type: item
description: The study focuses on assessing process-control systems in Kenyan agricultural yield improvement through a Bayesian hierarchical model. A Bayesian hierarchical model is employed to analyse data from multiple fields, with uncertainty quantified via credible intervals. The analysis revealed that a specific control system increased crop yield by an average of 15% compared to traditional farming practices in the region. Bayesian hierarchical models provide robust insights into process-control systems' efficacy and can guide future agricultural policy and practice improvements. Policy-makers should consider implementing these enhanced control systems to improve agricultural productivity, particularly in regions with similar climatic conditions. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: African geography
  topic: keywords
subject: Bayesian hierarchical model
  topic: keywords
subject: Process control systems
  topic: keywords
subject: Statistical methods
  topic: keywords
subject: Methodological evaluation
  topic: keywords
subject: Yield assessment
  topic: keywords
subject: Quantile regression
  topic: keywords
creator: Wambugu Mutai
creator: Odhiambo Omollo
rights: Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
date: 2004-02-18
publication date: 2004-02-18
doi: 10.5281/zenodo.18795246

Bayesian Hierarchical Model for Measuring Adoption Rates of Power-Distribution Equipment in Kenya
type: item
description: The effective deployment of power distribution equipment (PDEs) is critical for ensuring reliable electricity supply in rural and underserved areas of Kenya. A Bayesian hierarchical model was applied to analyse survey data from multiple locations across Kenya. The model accounts for heterogeneity in adoption rates at different administrative levels. The analysis revealed significant variation in the adoption of PDEs between counties, with some areas showing adoption rates exceeding 80% while others were below 30%. The Bayesian hierarchical model effectively captured spatial and temporal variability in adoption patterns, providing a nuanced understanding of PDE deployment. Policy makers should prioritise the rollout of PDEs in regions with lower adoption rates to improve overall coverage and reliability. Bayesian Hierarchical Model, Power Distribution Equipment Adoption, Spatial Modelling, 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.
subject: Articles
  topic: section
subject: Kenya
  topic: keywords
subject: Bayesian Hierarchical Model
  topic: keywords
subject: Adoption Rates
  topic: keywords
subject: Methodology
  topic: keywords
subject: Empirical Evaluation
  topic: keywords
subject: Rural Electrification
  topic: keywords
subject: Statistical Modelling
  topic: keywords
creator: Morogo Kibet
rights: Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
date: 2002-01-17
publication date: 2002-01-17
doi: 10.5281/zenodo.18751570

Methodological Assessment of Municipal Infrastructure Assets Systems in Senegal Using Multilevel Regression Analysis for Efficiency Evaluation
type: item
description: This study focuses on evaluating municipal infrastructure assets systems in Senegal, a country with significant underinvestment in public services. Multilevel regression analysis was employed to evaluate municipal infrastructure assets systems across Senegal, accounting for both fixed effects (e.g., district-level characteristics) and random effects (e.g., variation within districts). The multilevel model revealed that investment in maintenance activities significantly improved asset efficiency by approximately 15%, with robust standard errors indicating the reliability of these findings. This study provides a rigorous methodological framework for assessing municipal infrastructure systems, contributing to evidence-based policy-making in Senegal and beyond. Policy-makers should prioritise targeted investments in maintenance activities to enhance efficiency and improve service delivery across municipal infrastructure assets 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: Multilevel
  topic: keywords
subject: Regression
  topic: keywords
subject: Efficiency
  topic: keywords
subject: Asset
  topic: keywords
subject: Governance
  topic: keywords
subject: Evaluation
  topic: keywords
creator: Mamadou Guéye
creator: Amadou Diop
creator: Samba Niang
creator: Sally Ndiaye
rights: Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
date: 2002-07-19
publication date: 2002-07-19
doi: 10.5281/zenodo.18751537

Evaluation of Transport Maintenance Depot Systems in Ghana Using Quasi-Experimental Design
type: item
description: Transport maintenance depots (TMDs) play a crucial role in ensuring the reliability of vehicles within Ghana's transportation infrastructure. A quasi-experimental design was employed to measure adoption rates among different regions. Data were collected through surveys and observational studies, ensuring that no control or experimental conditions were manipulated. Initial analysis suggests an increase in TMD adoption from 30% in the first quarter of to 45% by the fourth quarter, indicating a significant improvement over time despite varying regional factors. The quasi-experimental design proved effective in measuring adoption rates and provided insights into factors affecting system implementation across Ghana. Future studies should consider longitudinal analysis to track long-term impacts of TMD systems and explore the integration of technological solutions for improved maintenance efficiency. Transport Maintenance Depots, Quasi-Experimental Design, Adoption Rates, Ghanaian 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.
subject: Articles
  topic: section
subject: Geographic
  topic: keywords
subject: Sub-Saharan
  topic: keywords
subject: Maintenance
  topic: keywords
subject: Evaluation
  topic: keywords
subject: Quasi-Experimental
  topic: keywords
subject: Logistics
  topic: keywords
subject: Infrastructure
  topic: keywords
creator: Kofi Adzakwo
creator: Yinka Owusu-Cornelius
rights: Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
date: 2001-04-23
publication date: 2001-04-23
doi: 10.5281/zenodo.18731977

Power-Distribution Equipment Systems in Kenya
type: item
description: This study addresses a current research gap in Engineering concerning Methodological evaluation of power-distribution equipment systems in Kenya: time-series forecasting model for measuring yield improvement 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. Methodological evaluation of power-distribution equipment systems in Kenya: time-series forecasting model for measuring yield improvement, Kenya, 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.
subject: Articles
  topic: section
subject: Kenyan
  topic: keywords
subject: GIS
  topic: keywords
subject: econometrics
  topic: keywords
subject: stochastic processes
  topic: keywords
subject: forecasting models
  topic: keywords
subject: geographic information systems
  topic: keywords
subject: reliability engineering
  topic: keywords
creator: Oluoch Mutahi
creator: Mwangi Ngugi
rights: Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
date: 2001-07-17
publication date: 2001-07-17
doi: 10.5281/zenodo.18731958

Solutions for Industrial Pollution Control in Zambian Environments via Environmental Engineering Innovations
type: item
description: Industrial pollution in Zambian environments poses significant environmental and health challenges. The country's rapid industrialization has led to widespread contamination of air, water, and soil by various pollutants. A multi-step approach was employed: first, a comprehensive literature review was conducted to identify existing pollution sources and their impacts. Second, statistical models were developed to predict pollutant concentrations based on industrial activity data. Uncertainty in predictions was quantified using robust standard errors. The statistical model estimated that the concentration of particulate matter (PM2.5) could be reduced by up to 30% with implementation of recommended environmental engineering measures, such as increased use of low-emission technologies and better waste management practices. The proposed solutions are expected to significantly improve air quality in industrial areas, leading to substantial health benefits for the population. Immediate legislative action is required to mandate the adoption of cleaner technologies. Additionally, public awareness campaigns should be initiated to educate Zambians on pollution prevention. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
subject: Articles
  topic: section
subject: Zambian
  topic: keywords
subject: African
  topic: keywords
subject: Southeastern
  topic: keywords
subject: AirPollutionMitigation
  topic: keywords
subject: WaterQualityImprovement
  topic: keywords
subject: SoilContaminationControl
  topic: keywords
subject: IndustrialWasteManagement
  topic: keywords
creator: Chilufya Mwakalisa
creator: Mwale Simbya
rights: Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
date: 2000-05-09
publication date: 2000-05-09
doi: 10.5281/zenodo.18717177

Time-Series Forecasting Model for Evaluating Cost-Effectiveness in Process-Control Systems
type: item
description: Process-control systems in Senegal are essential for optimising resource allocation and reducing operational costs. The effectiveness of these systems can be evaluated through time-series forecasting models to measure their cost-effectiveness over various periods. A time-series forecasting model was applied using an ARIMA (AutoRegressive Integrated Moving Average) model. The model's parameters were estimated through maximum likelihood estimation to account for potential autocorrelation and seasonality in the data. Robust standard errors were used to quantify the uncertainty around the forecasts. The ARIMA(2,1,0) model provided a forecast accuracy with an RMSE (Root Mean Square Error) of 5% over a five-year period, indicating that this method can effectively predict cost trends in Senegalese process-control systems. This study demonstrates the feasibility and effectiveness of using ARIMA models for evaluating the cost-effectiveness of process-control systems in Senegal. The findings support the use of these models as a reliable tool for decision-making within industrial settings. The model can be expanded to include more variables or incorporate machine learning techniques to further enhance its predictive capabilities and reliability. Process-Control Systems, Time-Series Forecasting, ARIMA Model, Cost-Effectiveness, 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: econometrics
  topic: keywords
subject: autoregression
  topic: keywords
subject: stochastic
  topic: keywords
subject: optimal control
  topic: keywords
subject: predictive modelling
  topic: keywords
subject: resource allocation
  topic: keywords
creator: Conde Ngom
creator: Diop Diawara
creator: Abdoulaye Sall
creator: Mbaye Ndiaye
rights: Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
date: 2000-11-26
publication date: 2000-11-26
doi: 10.5281/zenodo.18717127