African Aerial Photography and Remote Sensing (Technology/Methodology)
type: channel
description: African Aerial Photography and Remote Sensing (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 Aerial Photography and Remote Sensing (Technology/Methodology)
items: Remote Sensing-Based Monitoring of Road Degradation Near Oil Fields in Block 3, South Sudan
items: Customisable Ecosystem Restoration Proposals in Degraded Grasslands of South Sudan via Xenobiology Application Development
items: Open Data Initiatives and Governance Transparency in Ethiopia
items: Solar Microgrids in Off-Grid Senegalese Villages
items: Replicating Low-Cost IoT Solutions for Environmental Monitoring in Urban Slums of Togo
items: GPS-Informed Models for Enhancing Crop Growth Monitoring Among Ethiopian Highland Smallholder Women Farmers
items: NLP Frontiers in African Languages
items: Machine Learning Models for Climate Prediction and Adaptive Planning in Ghana
items: Revisiting Remote Sensing Insights on Agricultural Productivity in Eastern Kenya
items: Blockchain Technology for Supply Chain Transparency in Mineral Extraction in DRC
items: Digital Literacy Workshops on Financial Management Applications in Kenyan Secondary Cities
items: Cybersecurity in East African Financial Systems
items: Indigenous Knowledge Systems Integration into AI Development in Ghanaian Context
items: Methodological Assessment of Manufacturing Systems Adoption in Nigerian Plants Using Quasi-Experimental Design,
items: Aerial LiDAR-Based Forest Management Impact Assessment in Dense Nigerian Communities
items: Blockchain Technology in Supply Chain Transparency during Mineral Extraction in Democratic Republic of Congo and Liberia
items: ICT Infrastructural Development and Economic Growth Dynamics in Ethiopia
items: Remote Sensing Data Integration for Crop Yield Prediction in Madagascar Rice Paddies
items: Elderly Mental Health Awareness in Dakar
items: Methodological Assessment of Field Research Stations in South Africa Using Panel Data for Adoption Rate Measurement
items: Cybersecurity Architectures and Mitigation Strategies for Financial Systems in East Africa
items: Bridging Digital Inclusion in Rural South Africa
items: Methodological Evaluation of Off-Grid Communities Systems in South Africa
items: Natural Language Processing Challenges and Opportunities in African Languages of Togo
items: Design and Impact Evaluation of Agricultural Insurance Programmes in Mozambique's Maize Regions

Remote Sensing-Based Monitoring of Road Degradation Near Oil Fields in Block 3, South Sudan
type: item
description: AFRICAN JOURNAL OF AERIAL PHOTOGRAPHY AND REMOTE SENSING Vol. 7, No. 1, March 2026 • Pan-African Research Journals (PARJ) • Open Access • Peer-Reviewed ORIGINAL RESEARCH ARTICLE Remote Sensing-Based Monitoring of Road Degradation Near Oil Fields in Block 3, South Sudan Aduot Madit Anhiem1, Department of Civil Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia Correspondence: aduot.madit2022@gmail.com • rigkher@gmail.com • Received: 01 January 2026 | Accepted: 14 January 2026 | Published: 10 March 2026 DOI: 10.5281/zenodo.19234278 | Article ID: AJAPRS-2025-0089 Abstract—Road infrastructure supporting oil extraction in South Sudan’s Block 3 oilfield (Upper Nile State) deteriorates rapidly under the combined stresses of heavy oil tanker and equipment traffic, expansive Vertisol clay subgrades, and extreme seasonal flooding, yet systematic monitoring is absent due to the region’s remoteness, active security constraints, and absence of roadside instrumentation. This study presents the first multi-temporal, remote sensing-based road degradation monitoring assessment for Block 3, South Sudan, using dense Sentinel-2 multispectral imagery time series (2017–2023, n = 184 scenes) and Sentinel-1 C-band synthetic aperture radar (SAR) backscatter data. A Road Degradation Index (RDI) is derived from a combination of road-corridor NDVI suppression, bare soil index (BSI), SWIR ratio, and SAR backscatter, validated against 60 field-surveyed road condition points (International Roughness Index, IRI, and visual distress assessment). The resulting Random Forest (RF) classification model achieves an overall accuracy of 87.6% and kappa coefficient of 0.87 against independent field reference data. Analysis of the 2017–2023 time series reveals that 24% of the 412 k
subject: Articles
  topic: section
subject: remote sensing
  topic: keywords
subject: road degradation
  topic: keywords
subject: Sentinel-2
  topic: keywords
subject: NDVI
  topic: keywords
subject: bare soil index
  topic: keywords
subject: SAR
  topic: keywords
subject: South Sudan
  topic: keywords
subject: Block 3
  topic: keywords
creator: ORIGINAL RESEARCH ARTICLE
rights: Copyright (c) 2026 African Aerial Photography and Remote Sensing (Technology/Methodology)
date: 2026-03-27
publication date: 2026-03-27
doi: 10.5281/zenodo.19246306

Customisable Ecosystem Restoration Proposals in Degraded Grasslands of South Sudan via Xenobiology Application Development
type: item
description: Degraded grasslands in South Sudan present significant challenges for ecosystem restoration due to environmental stressors such as drought and overgrazing. A comprehensive search strategy was employed to identify relevant studies from various databases. Studies were assessed based on predefined inclusion and exclusion criteria to ensure robust analysis. The review identified a directional theme indicating that xenobiological approaches, such as the creation of genetically modified organisms (GMOs) for soil remediation, show promise in enhancing grassland recovery with proportions ranging from 20% to 45% improvement over traditional methods. Xenobiology applications offer a promising avenue for developing customized ecosystem restoration strategies that can be adapted to local conditions and resource availability. Further research should focus on validating these approaches through controlled experimental studies in South Sudan’s degraded grasslands, with an emphasis on cost-effectiveness and environmental sustainability. Xenobiology, Ecosystem Restoration, Degraded Grasslands, Computer Science, South Sudan Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Degraded Grasslands
  topic: keywords
subject: South Sudan
  topic: keywords
subject: Xenobiology
  topic: keywords
subject: Ecosystem Restoration
  topic: keywords
subject: Customisation
  topic: keywords
subject: Methodological Framework
  topic: keywords
subject: Theoretical Models
  topic: keywords
creator: James Kajok Lok
creator: David Yambio Akech
creator: Evelyn Nyandeng Deng
rights: Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
date: 2012-04-07
publication date: 2012-04-07
doi: 10.5281/zenodo.18962985

Open Data Initiatives and Governance Transparency in Ethiopia
type: item
description: Open data initiatives have become a critical component of public sector reform in Ethiopia, aiming to enhance transparency and accountability. A mixed-methods approach combining qualitative interviews with quantitative analysis of open data datasets was employed. Open data initiatives have been implemented by the Ethiopian government, particularly in sectors such as agriculture and health, resulting in increased citizen engagement and policy improvements. For instance, a 30% increase in agricultural productivity reports were observed due to better access to timely data. The findings underscore the potential of open data for fostering public participation in governance processes. Further implementation of standardised open data policies is recommended to ensure consistent and accessible information across government sectors. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: African Geography
  topic: keywords
subject: Open Data
  topic: keywords
subject: Governance Transparency
  topic: keywords
subject: Public Sector Reform
  topic: keywords
subject: Qualitative Research
  topic: keywords
subject: Data Visualization
  topic: keywords
subject: Web Analytics
  topic: keywords
creator: Negusse Woldehayrondilla
rights: Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
date: 2012-03-22
publication date: 2012-03-22
doi: 10.5281/zenodo.18962935

Solar Microgrids in Off-Grid Senegalese Villages
type: item
description: Solar microgrids have been implemented in off-grid Senegalese villages as a sustainable energy solution. The study utilised data collected through surveys and monitoring of energy consumption patterns. Energy consumption patterns showed an average reduction of 40% in electricity bills compared to previous non-solar methods, with a standard error of ±5%. Solar microgrids significantly reduced financial costs and carbon emissions over the two-year period. Communities should be encouraged to adopt solar microgrids for sustainable energy solutions. solar microgrid, off-grid villages, financial savings, emissions reductions Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Geographic
  topic: keywords
subject: Senegalese
  topic: keywords
subject: Off-Grid
  topic: keywords
subject: Microgrids
  topic: keywords
subject: Sustainability
  topic: keywords
subject: EnergyHarvesting
  topic: keywords
subject: Monitoring
  topic: keywords
creator: Kadiata Diallo
creator: Ibrahima Sow
rights: Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
date: 2011-05-20
publication date: 2011-05-20
doi: 10.5281/zenodo.18930429

Replicating Low-Cost IoT Solutions for Environmental Monitoring in Urban Slums of Togo
type: item
description: { "background": "The urban slums of Togo face significant environmental challenges such as poor waste management leading to pollution and health risks.", "purposeandobjectives": "To replicate a previous study's low-cost Internet of Things (IoT) solutions for monitoring air quality, water contamination, and waste levels in these communities.", "methodology": "A replication of the original IoT setup was conducted with sensors calibrated using a linear regression model to predict pollutant concentrations based on historical data ($y = \beta0 + \beta1x + \epsilon$, where $R^2=0.85$ and robust standard errors are reported).", "findings": "The replicated system detected an average of 7% higher levels of particulate matter in urban slums compared to the original study, indicating potential calibration discrepancies.", "conclusion": "While replication confirms the viability of IoT solutions for monitoring environmental conditions in Togolese urban slums, it also highlights the importance of local calibration.", "recommendations": "Further studies should focus on calibrating sensors locally and incorporating feedback mechanisms to improve accuracy.", "keywords": "IoT, Urban Slums, Environmental Monitoring, Calibration, Replication Study", "contributionstatement": "This study demonstrates that with slight modifications in sensor calibration, IoT solutions can be successfully replicated for monitoring environmental conditions in urban slums." } --- Structured Abstract Background The urban slums of Togo face significant environmental challenges such as poor waste management leading to pollution and health risks. Purpose and Objectives To replicate a previous study's low-cost Internet of Things (IoT) solutions for monitoring air quality, water contamination, and waste levels in these communities. Methodology A replication of the original IoT setup was conducted with sensors calibrated using a linear regression model to predict pollutant concentrations based on historical data ($…
subject: Articles
  topic: section
subject: African
  topic: keywords
subject: IoT
  topic: keywords
subject: Sensor Networks
  topic: keywords
subject: Data Fusion
  topic: keywords
subject: Energy Harvesting
  topic: keywords
subject: Wireless Communication
  topic: keywords
subject: Smart Cities
  topic: keywords
creator: Lognonne Eyoné
creator: Adouvi Désiré
creator: Aladji Amara
rights: Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
date: 2011-08-25
publication date: 2011-08-25
doi: 10.5281/zenodo.18930407

GPS-Informed Models for Enhancing Crop Growth Monitoring Among Ethiopian Highland Smallholder Women Farmers
type: item
description: Recent advancements in GPS technology have enabled more precise monitoring of crop growth among smallholder farmers in Ethiopia’s highlands. A mixed-methods approach was employed, integrating GPS data with field observations to model crop growth trajectories over time. The analysis revealed a significant positive correlation (r = 0.85) between daily GPS-sampled soil moisture levels and subsequent plant yield increases in tested plots. GPS-informed models offer a robust tool for improving smallholder farmers' crop management practices, particularly among women farmers in the Ethiopian Highlands. Further research should focus on scaling up these models to larger geographic areas and incorporating additional climate data for enhanced predictive accuracy. Crop Growth Monitoring, GPS Technology, Smallholder Women Farmers, Ethiopian Highlands
subject: Articles
  topic: section
subject: Geographic
  topic: keywords
subject: Highlands
  topic: keywords
subject: GPS
  topic: keywords
subject: GIS
  topic: keywords
subject: Precision Agriculture
  topic: keywords
subject: Remote Sensing
  topic: keywords
subject: Women Farmers
  topic: keywords
creator: Mihret Kebede
creator: Fasil Woldemichael
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-03-03
publication date: 2010-03-03
doi: 10.5281/zenodo.18908970

NLP Frontiers in African Languages
type: item
description: { "background": "Natural Language Processing (NLP) is a critical component of computational linguistics that enables machines to understand and process human language. In Africa, where many languages have unique grammatical structures and vocabularies, NLP presents significant challenges due to the paucity of resources and data.", "purposeandobjectives": "The focus of this report is on the application of NLP for African languages in Tanzania, specifically examining the current state of research and identifying areas where further development is needed. The objectives are to identify gaps in existing models and propose methodologies that can enhance NLP capabilities for Tanzanian languages.", "methodology": "This study employed a comparative analysis of existing NLP frameworks used in other African languages alongside machine learning techniques, particularly focusing on the use of recurrent neural networks (RNNs) with attention mechanisms to improve accuracy. The methodology aimed at developing robust models that can be applied across multiple Tanzanian languages.", "findings": "The findings indicate a significant variation in performance metrics between different Tanzanian languages due to their distinct grammatical structures, which poses challenges for universal NLP models. For instance, the accuracy of language-specific RNN models varied from 75% to 89%, with Swahili generally performing better than other languages.", "conclusion": "While existing NLP models have shown promise in Tanzanian languages, they are not universally applicable and require tailored approaches for each language. The study highlights the need for larger datasets and more diverse linguistic resources to improve model performance and reliability.", "recommendations": "Recommendations include the creation of a central repository for linguistic data to support collaborative research efforts, investment in computational linguistics by both public and private sectors, and the development of educ…
subject: Articles
  topic: section
subject: African languages
  topic: keywords
subject: Computational linguistics
  topic: keywords
subject: Dissemination studies
  topic: keywords
subject: Geospatial analysis
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Morphology
  topic: keywords
subject: Transliteration systems
  topic: keywords
creator: Nzuri Simo
creator: Chinganya Masanja
creator: Kamili Mwakalunga
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-12-05
publication date: 2010-12-05
doi: 10.5281/zenodo.18908929

Machine Learning Models for Climate Prediction and Adaptive Planning in Ghana
type: item
description: Climate change poses significant challenges to agriculture, water resources management, and urban planning in Ghana. Accurate climate predictions are essential for adaptive planning and mitigation strategies. A hybrid ensemble model combining Random Forest (RF) and Extreme Gradient Boosting (XGBoost) was employed. Model performance was evaluated using Mean Absolute Error (MAE) with a 95% confidence interval as uncertainty quantification. RF-XGBoost outperformed baseline models, achieving an MAE of 2.3°C compared to the RF model's 2.8°C and XGBoost’s 2.6°C, indicating improved predictive accuracy in climate forecasting for Ghana. The hybrid ensemble approach demonstrated enhanced robustness and precision in climate predictions, facilitating more informed adaptive planning efforts in Ghana. Future research should focus on integrating additional datasets to further refine the models' performance and explore their application across different regions of Ghana. Machine Learning, Climate Prediction, Ensemble Models, Extreme Gradient Boosting, Random Forest Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: Geographic Information Systems
  topic: keywords
subject: Ensemble Forecasting
  topic: keywords
subject: Climate Indices
  topic: keywords
subject: Machine Learning Algorithms
  topic: keywords
subject: Data Fusion
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Abena Aggrey
creator: Kofi Adomako
creator: Yaw Asamoah
creator: Adwoa Agyei
rights: Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
date: 2009-06-04
publication date: 2009-06-04
doi: 10.5281/zenodo.18894921

Revisiting Remote Sensing Insights on Agricultural Productivity in Eastern Kenya
type: item
description: This study revisits previous research on remote sensing data to assess agricultural productivity growth in rural Eastern Kenya. Remote sensing data from the year was utilised. A linear regression model with robust standard errors was applied to estimate productivity growth. A significant proportion (p < 0.05) of the variance in agricultural yield was explained by technological advancements, indicating their substantial impact on crop yields. The replication confirms the original findings and emphasizes the importance of integrating technology into rural agricultural practices to enhance productivity. Further research should consider expanding the dataset to include more recent years to assess long-term trends in agricultural productivity. Agricultural Productivity, Remote Sensing, Technology Impact, Eastern Kenya Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Kenya
  topic: keywords
subject: Remote Sensing
  topic: keywords
subject: Agricultural Productivity
  topic: keywords
subject: Linear Regression
  topic: keywords
subject: Geostatistics
  topic: keywords
subject: GIS
  topic: keywords
subject: Spatial Analysis
  topic: keywords
creator: Kerubo Macharia
creator: Chewe Ndirangu
creator: Carmel Gitonga
creator: Wanjira Wambugu
rights: Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
date: 2009-08-17
publication date: 2009-08-17
doi: 10.5281/zenodo.18894870

Blockchain Technology for Supply Chain Transparency in Mineral Extraction in DRC
type: item
description: This study addresses a current research gap in Computer Science concerning Blockchain Technology for Supply Chain Transparency in Mineral Extraction in DRC in Central African Republic. 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. Blockchain Technology for Supply Chain Transparency in Mineral Extraction in DRC, Central African Republic, Africa, Computer Science, methodology paper This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: African geography
  topic: keywords
subject: blockchain technology
  topic: keywords
subject: supply chain management
  topic: keywords
subject: transparency models
  topic: keywords
subject: data integrity
  topic: keywords
subject: geographic information systems
  topic: keywords
subject: smart contracts
  topic: keywords
creator: Mboumba Kalala
creator: Madingoulou Mbassa
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-01-03
publication date: 2008-01-03
doi: 10.5281/zenodo.18872133

Digital Literacy Workshops on Financial Management Applications in Kenyan Secondary Cities
type: item
description: Digital literacy workshops have been identified as a critical tool for enhancing financial management skills among microfinance clients in secondary cities of Kenya. A comprehensive search strategy was implemented using electronic databases such as PubMed, Web of Science, Google Scholar, and Scopus. Studies published between and were included if they focused on digital literacy workshops for financial management applications in secondary cities of Kenya. The review followed PRISMA guidelines. The analysis revealed a significant proportion (78%) of participants reported improved understanding and application of financial management concepts post-workshop, with themes including access to technology, facilitator expertise, and participant engagement. However, there was variability in the effectiveness across different workshops based on these factors. This review highlights the need for standardised digital literacy training programmes tailored to microfinance clients in Kenyan secondary cities, emphasising the importance of consistent technological infrastructure and skilled trainers. Future research should focus on evaluating long-term impacts and scalability of digital literacy workshops. Additionally, further investigation into participant feedback and technology adoption rates is recommended. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Digital Literacy
  topic: keywords
subject: Financial Inclusion
  topic: keywords
subject: Microfinance
  topic: keywords
subject: Secondary Cities
  topic: keywords
subject: Kenyan Context
  topic: keywords
subject: Empirical Research
  topic: keywords
subject: Quantitative Methods
  topic: keywords
creator: Oscar Mungai
creator: Waweru Kiprop
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-09-19
publication date: 2008-09-19
doi: 10.5281/zenodo.18872110

Cybersecurity in East African Financial Systems
type: item
description: East African financial systems are increasingly vulnerable to cyber threats due to rapid technological adoption and a lack of standardised cybersecurity practices. A mixed-methods approach combining quantitative surveys (n=500) and qualitative interviews (n=20), with statistical analysis using logistic regression models to assess threat frequency and severity. Botswana's financial sector reported a higher incidence of cyber attacks compared to other countries, with an estimated 42% experiencing at least one breach in the past year, while Tanzania showed lower attack rates but significant financial losses from fraud. Mitigation strategies vary by country, emphasising the importance of regional collaboration and tailored cybersecurity policies for effective protection against evolving cyber threats. Develop a regional cybersecurity framework based on Botswana's successful model to strengthen East African financial resilience.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: East Africa
  topic: keywords
subject: Threats
  topic: keywords
subject: Mitigation Strategies
  topic: keywords
subject: Financial Systems
  topic: keywords
subject: Geographical Focus
  topic: keywords
subject: Mixed-Methods Approach
  topic: keywords
creator: Modiba Mothoagapeng
creator: Seoka Sekhoboane
creator: Makwena Mokgophelo
creator: Motsete Mogotsi
rights: Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
date: 2007-03-24
publication date: 2007-03-24
doi: 10.5281/zenodo.18851974

Indigenous Knowledge Systems Integration into AI Development in Ghanaian Context
type: item
description: The integration of Indigenous Knowledge Systems (IKS) into Artificial Intelligence (AI) development has gained traction globally as a means to bridge technological gaps and enhance local applications. In Ghana, where IKS are deeply embedded in socio-cultural contexts, this research aims to explore how such integration can be harnessed for AI advancements. This research employs a mixed-methods approach combining qualitative interviews with semi-structured questionnaires to gather data on local perceptions of AI applications. Quantitative analysis will be conducted using machine learning algorithms to evaluate the accuracy and efficiency of these models in real-world scenarios. The preliminary findings suggest that there is significant interest among communities for AI solutions that incorporate traditional environmental knowledge, indicating a strong potential for successful integration. Preliminary model performance shows an error rate reduction by 15% when IKS are integrated into the AI algorithm. This study provides evidence supporting the feasibility of integrating IKS into AI development in Ghanaian contexts. The findings highlight the importance of community engagement and cultural sensitivity in developing AI solutions that resonate with local needs. Future research should focus on scaling up these models to larger geographical areas, conducting further empirical studies to validate findings, and establishing guidelines for ethical AI development that respects IKS. Indigenous Knowledge Systems, Artificial Intelligence, Ghana, Environmental Monitoring, Machine Learning Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: African Geography
  topic: keywords
subject: Indigenous Knowledge Systems
  topic: keywords
subject: AI Development
  topic: keywords
subject: Methodology
  topic: keywords
subject: Cultural Heritage
  topic: keywords
subject: Ethnographic Research
  topic: keywords
subject: Cognitive Computing
  topic: keywords
creator: Nana Afoakwah
creator: Kofi Asare
rights: Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
date: 2007-04-02
publication date: 2007-04-02
doi: 10.5281/zenodo.18851944

Methodological Assessment of Manufacturing Systems Adoption in Nigerian Plants Using Quasi-Experimental Design,
type: item
description: Manufacturing systems adoption in Nigerian plants has been a topic of interest for assessing productivity improvements. The review employs rigorous methodology including systematic searching and selection processes from academic databases. Studies are critically analysed for their use of quasi-experimental design in measuring adoption rates across various sectors. A notable finding is the predominance of within-industry comparisons over cross-sectional studies, indicating a preference for local context-specific evaluations. The review underscores the importance of rigorous methodological approaches to ensure valid conclusions regarding manufacturing system adoptions in Nigerian contexts. Future research should expand on longitudinal quasi-experimental designs to provide more comprehensive insights into long-term adoption impacts. manufacturing systems, adoption rates, quasi-experimental design, Nigeria Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: African
  topic: keywords
subject: econometric
  topic: keywords
subject: intervention
  topic: keywords
subject: diffusion
  topic: keywords
subject: replication
  topic: keywords
subject: qualitative
  topic: keywords
creator: Chukwuka Amaechi
creator: Uzoma Ifeaku
rights: Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
date: 2006-06-14
publication date: 2006-06-14
doi: 10.5281/zenodo.18829996

Aerial LiDAR-Based Forest Management Impact Assessment in Dense Nigerian Communities
type: item
description: Aerial LiDAR technology has emerged as a powerful tool for mapping forest cover in dense urban environments, offering high-resolution data that can be used to assess biodiversity and carbon sequestration potential. The methodology involves collecting high-resolution LiDAR data over multiple seasons, segmenting the imagery into individual tree canopies for accurate quantification. A statistical model predicting carbon storage is applied with robust standard errors to account for variability in forest structure and composition. Initial analysis indicates a significant positive correlation between community management practices and increased tree cover density by 30% over five years, suggesting enhanced carbon sequestration potential. The findings underscore the effectiveness of LiDAR-based assessments in monitoring and managing forest ecosystems within densely populated areas, providing actionable insights for biodiversity conservation efforts. Communities should be encouraged to adopt sustainable management practices supported by continuous LiDAR surveillance to maximise carbon sequestration and preserve local biodiversity. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: African Geography
  topic: keywords
subject: LiDAR
  topic: keywords
subject: Remote Sensing
  topic: keywords
subject: GIS
  topic: keywords
subject: Ecological Impact Assessment
  topic: keywords
subject: Precision Mapping
  topic: keywords
subject: Forest Dynamics
  topic: keywords
creator: Olumide Onifade
rights: Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
date: 2006-08-18
publication date: 2006-08-18
doi: 10.5281/zenodo.18829968

Blockchain Technology in Supply Chain Transparency during Mineral Extraction in Democratic Republic of Congo and Liberia
type: item
description: Blockchain technology has emerged as a promising solution for enhancing transparency in complex supply chains, particularly within mineral extraction sectors characterized by high levels of corruption and illicit activities. The review will be guided by a systematic search strategy encompassing academic databases such as Scopus, Web of Science, and Google Scholar using keywords related to blockchain technology, mineral extraction, supply chain transparency, and DRC/Liberia. Studies published between and will be included. Blockchain-based solutions have shown promise in reducing intermediaries' influence on the supply chain by increasing traceability and accountability, though their adoption is still nascent with varying degrees of success across different contexts. While blockchain technology holds significant potential for enhancing transparency and integrity in mineral extraction operations, its implementation faces challenges related to technological maturity, regulatory frameworks, and stakeholder engagement. Further research should focus on developing robust blockchain solutions tailored to local conditions. Policy makers should consider incentivizing the use of blockchain technology through grants or tax benefits, while fostering collaboration between industry stakeholders to address technical and operational hurdles. Academic communities can contribute by conducting more detailed case studies and comparative analyses. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: African Geography
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Supply Chain Management
  topic: keywords
subject: Transparency
  topic: keywords
subject: Mineral Extraction
  topic: keywords
subject: Africa
  topic: keywords
subject: Logistics Systems
  topic: keywords
creator: Moses Gbakpan
creator: Oliver Kpormaror
rights: Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
date: 2005-07-23
publication date: 2005-07-23
doi: 10.5281/zenodo.18815701

ICT Infrastructural Development and Economic Growth Dynamics in Ethiopia
type: item
description: This study addresses a current research gap in Computer Science concerning ICT Infrastructure Development and its Impact on Economic Growth in Ethiopia 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. ICT Infrastructure Development and its Impact on Economic Growth in Ethiopia, Ethiopia, Africa, Computer Science, working paper This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: econometric
  topic: keywords
subject: accessibility
  topic: keywords
subject: digital divide
  topic: keywords
subject: service diffusion
  topic: keywords
creator: Aregawi Abera Haileuyad
creator: Mulu Woldetsadik
rights: Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
date: 2005-10-15
publication date: 2005-10-15
doi: 10.5281/zenodo.18815660

Remote Sensing Data Integration for Crop Yield Prediction in Madagascar Rice Paddies
type: item
description: Remote sensing data integration is increasingly used for crop yield prediction in agricultural settings, particularly in developing countries like Madagascar where traditional methods are limited. A systematic search was conducted across multiple databases focusing on studies that utilised remote sensing for crop yield prediction, with an emphasis on Madagascar’s environment and climate conditions. Remote sensing data integration significantly improved yield predictions by up to 20% in comparison to traditional methods. Application timelines varied based on sensor type and environmental factors. The review underscores the potential of remote sensing for enhancing agricultural productivity, especially in Madagascar's diverse rice paddies. Further research should explore integrating machine learning models with remote sensing data for more accurate yield predictions. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Remote sensing
  topic: keywords
subject: GIS
  topic: keywords
subject: GPS
  topic: keywords
subject: precision agriculture
  topic: keywords
subject: crop modelling
  topic: keywords
subject: geographic information systems
  topic: keywords
subject: agricultural technology
  topic: keywords
creator: Rolande Randriamasingo
creator: Antoine Raveloniranahary
rights: Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
date: 2004-11-10
publication date: 2004-11-10
doi: 10.5281/zenodo.18795282

Elderly Mental Health Awareness in Dakar
type: item
description: Elderly mental health in Dakar, Senegal faces significant challenges due to cultural stigma and limited access to healthcare. The study employed a comparative design with two groups: an experimental group receiving the educational intervention and a control group not exposed to it. Qualitative data were collected through focus group discussions and interviews. A thematic analysis revealed that participants in the experimental group showed a significant increase (p<0.05) in understanding of mental health issues, with themes including stigma reduction and improved access to services. The educational intervention successfully enhanced mental health awareness among elderly residents in Dakar. Future research should explore long-term impacts and scalability of the intervention across different demographics in Senegal. elderly, mental health, Dakar, educational intervention, qualitative study Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: African
  topic: keywords
subject: Qualitative
  topic: keywords
subject: Intervention
  topic: keywords
subject: Stigma
  topic: keywords
subject: Cultural anthropology
  topic: keywords
subject: Ethnography
  topic: keywords
subject: Community engagement
  topic: keywords
creator: Oumar Gouze
rights: Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
date: 2004-04-17
publication date: 2004-04-17
doi: 10.5281/zenodo.18795242

Methodological Assessment of Field Research Stations in South Africa Using Panel Data for Adoption Rate Measurement
type: item
description: Field research stations in South Africa play a crucial role in various scientific disciplines, including Computer Science. A comprehensive systematic literature review was conducted, focusing on methodologies employed in South African field research stations. The analysis included a wide range of studies that utilised panel-data estimation techniques. Panel-data methods were found to be predominantly used (85%) for measuring adoption rates across the reviewed studies, with significant variations observed among different disciplines and institutions. The review identified several gaps in methodological consistency and highlighted a need for standardised approaches to ensure robust data interpretation. Future research should prioritise developing a unified framework based on panel-data methods to enhance comparability and reliability of adoption rate measurements. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: African geography
  topic: keywords
subject: panel data
  topic: keywords
subject: qualitative methods
  topic: keywords
subject: statistical analysis
  topic: keywords
subject: system evaluation
  topic: keywords
subject: geographic information systems (GIS)
  topic: keywords
subject: spatial statistics
  topic: keywords
creator: Sipho Nkosi
rights: Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
date: 2002-05-21
publication date: 2002-05-21
doi: 10.5281/zenodo.18751556

Cybersecurity Architectures and Mitigation Strategies for Financial Systems in East Africa
type: item
description: Cybersecurity threats to financial systems in East Africa are on the rise, with significant implications for economic stability and public trust. A mixed-methods approach combining quantitative analysis of network traffic data and qualitative interviews with cybersecurity experts was employed. The analysis revealed a 30% increase in malware infections targeting East African banks' networks over the past year, necessitating robust defence mechanisms. Enhanced encryption protocols and real-time monitoring systems are recommended to mitigate these risks effectively. Financial institutions should integrate advanced security solutions into their existing infrastructure to safeguard against future cyber threats. cybersecurity, financial systems, East Africa, Ethiopia Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Ethiopia
  topic: keywords
subject: Sub-Saharan
  topic: keywords
subject: Network Security
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Cryptography
  topic: keywords
subject: Blockchain
  topic: keywords
subject: IoT Security
  topic: keywords
creator: Fikadu Abera
creator: Mekuria Belay
creator: Yilma Gebreab
creator: Alemayehu Desta
rights: Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
date: 2002-10-04
publication date: 2002-10-04
doi: 10.5281/zenodo.18751475

Bridging Digital Inclusion in Rural South Africa
type: item
description: Digital inclusion in rural South Africa faces significant challenges due to inadequate access to digital infrastructure and services. A comprehensive search of academic databases, including Google Scholar and Scopus, was conducted using keywords such as 'digital inclusion', 'rural South Africa', and 'remote sensing'. The findings revealed a substantial proportion (65%) of surveyed individuals lacked internet access in rural areas, with an average use time per day of 1.5 hours. Digital infrastructure improvements coupled with targeted digital literacy programmes are essential for enhancing access and usage among rural populations. Public-private partnerships should be encouraged to support the development and deployment of affordable internet solutions in underserved regions. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Geographic Terms: African Rural Methodological Terms: Literature Review Qualitative Analysis Theoretical Concepts: Digital Divide Equity Inclusion Infrastructure Socioeconomic Factors
  topic: keywords
creator: Nombuyisile Mngqibiso
creator: Sibusiso Khumalo
creator: Nomonde Ngwenyama
creator: Kgoliso Dlamini
rights: Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
date: 2001-07-15
publication date: 2001-07-15
doi: 10.5281/zenodo.18731969

Methodological Evaluation of Off-Grid Communities Systems in South Africa
type: item
description: This study addresses a current research gap in Computer Science concerning Methodological evaluation of off-grid communities systems in South Africa: randomized field trial 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 off-grid communities systems in South Africa: randomized field trial for measuring system reliability, South Africa, Africa, Computer Science, methodology paper This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: randomized controlled trial
  topic: keywords
subject: data collection methods
  topic: keywords
subject: geographic information systems
  topic: keywords
subject: statistical analysis
  topic: keywords
subject: sampling theory
  topic: keywords
subject: community infrastructure assessment
  topic: keywords
creator: Sifiso Thembisile Mogolelo
creator: Themba Sikhosana Ngqwane
creator: Nokuthula Masego Nkosi
creator: Mawuli Khumalo Dlamini
rights: Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
date: 2001-12-03
publication date: 2001-12-03
doi: 10.5281/zenodo.18731956

Natural Language Processing Challenges and Opportunities in African Languages of Togo
type: item
description: Natural Language Processing (NLP) is a critical component of modern computational systems that process human language. Despite its widespread use in widely spoken languages, NLP techniques for African languages remain underexplored and often face significant challenges. A comparative approach was adopted to evaluate different NLP methodologies. A Maximum Likelihood Estimation (MLE) model was selected as the primary methodological tool due to its robustness in handling sparse data typical of minority languages. The effectiveness of this choice was assessed using a confidence interval around the estimated parameters. The empirical results indicated that the MLE model significantly improved the accuracy of language classification tasks, achieving an accuracy rate of over 90% on a test dataset with a 2-sigma uncertainty level. This study provides valuable insights into the development of NLP models for African languages and highlights the potential benefits of using robust statistical methods in under-resourced language domains. Future research should focus on expanding the MLE model to include additional linguistic features that may enhance its performance, particularly when dealing with more complex Togolese dialects. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: African
  topic: keywords
subject: Grammar
  topic: keywords
subject: Dependency
  topic: keywords
subject: Tagging
  topic: keywords
subject: Corpus
  topic: keywords
subject: Cohesion
  topic: keywords
creator: Koffi Akpakpa
creator: Ehouds Yohannesso
creator: Akouin Tsogbah
creator: Ahanonu Agossou
rights: Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
date: 2000-09-11
publication date: 2000-09-11
doi: 10.5281/zenodo.18717175

Design and Impact Evaluation of Agricultural Insurance Programmes in Mozambique's Maize Regions
type: item
description: The study examines the impact of agricultural insurance programmes in Mozambique's maize regions, focusing on risk mitigation and policy effectiveness. A mixed-methods approach combining survey data from farmers and statistical analysis of programme performance metrics. Insights revealed that a specific model had an R² value of 0.75, indicating strong explanatory power regarding risk mitigation outcomes. The study concludes with recommendations for policy makers to enhance insurance effectiveness by addressing identified gaps in coverage and transparency. Policy recommendations include expanding insurance coverage to cover more maize-producing areas and improving communication channels between farmers and insurers. Agricultural Insurance, Maize Regions, Risk Mitigation, Farmer Satisfaction, Policy Effectiveness Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
subject: Articles
  topic: section
subject: African Geography
  topic: keywords
subject: Risk Mitigation Strategies
  topic: keywords
subject: Policy Analysis
  topic: keywords
subject: Methodological Evaluation
  topic: keywords
subject: Insurance Model Design
  topic: keywords
subject: Farmer Satisfaction Indices
  topic: keywords
subject: Quantitative Research Methods
  topic: keywords
creator: Rita Muvira
creator: Neville Machangwa
creator: Isaiah Chikomba
creator: Carmen Simango
rights: Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
date: 2000-11-08
publication date: 2000-11-08
doi: 10.5281/zenodo.18717125