African Pharmaceutical Regulatory Affairs
type: channel
description: African Pharmaceutical Regulatory Affairs 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 Pharmaceutical Regulatory Affairs
items: AI-Powered Tools for Enhancing Drug Identification in Tidjani Village Pharmacists, Tanzania
items: Mobile Innovations in Agricultural Information Transmission
items: ICT Infrastructure Development and Economic Growth Nexus in Ethiopia
items: Machine Learning Models for Climate Prediction and Adaptive Planning in Kenya
items: Open Data Initiatives and Governance Transparency in São Tomé and Príncipe
items: Developing Low-Cost IoT Frameworks for Urban Slum Environmental Monitoring in Tunisia
items: Longitudinal Adoption Analysis of Mobile Money Apps Among Smallholder Farmers in Eastern DR Congo
items: Designing App Interfaces for Risk Communication on Malaria During Pregnancy
items: Machine Learning Models in Climate Prediction and Adaptation Planning for Democratic Republic of Congo
items: Evaluation Methodology for Wearable Device Implementation in Diabetes Monitoring Across Kenyan Urban Slums 2009
items: Social Impact Bonds for Mental Health Care in Lagos
items: Low-Cost IoT Solutions for Environmental Monitoring in Urban Slums of Equatorial Guinea
items: ICT Infrastructure Development and Economic Growth in Ethiopia
items: 5G Technology's Digital Transformation Potential in Togo's African Cities
items: Replication Evaluation of Municipal Water Systems in Ethiopia
items: Methodological Assessment of Quasi-Experimental Designs in Off-Grid Community Systems in Senegal,
items: Natural Language Processing Challenges and Opportunities in Burundian African Languages
items: Community-Based Agriculture Extension Programmes Among Smallholder Women Farmers in Western Kenya
items: AI in Diagnostics
items: Digital Prescription Tracking Systems in Nairobi Outpatient Departments
items: Open Data Initiatives and Governance Transparency in Morocco
items: Technological Adoption Rates and Cost Savings in Rural Health Information Systems
items: Social Media Campaigns and Tobacco Cessation among Youth in Tanzania
items: Integrating Indigenous Knowledge Systems into AI Frameworks in West Africa
items: Designing User Interfaces for Low-Literacy Populations in African Contexts
items: 5G in African Cities

AI-Powered Tools for Enhancing Drug Identification in Tidjani Village Pharmacists, Tanzania
type: item
description: Tidjani Village in Tanzania faces challenges in accurately identifying medications due to limited training and resources. A machine learning approach was employed using historical prescription data from the village pharmacy. A convolutional neural network (CNN) model was trained on this dataset to enhance accuracy in identifying medications. The AI tool achieved an identification accuracy rate of 95% with a confidence interval of [93%, 97%], indicating significant improvement over human error rates. The developed AI system shows promise for reducing medication errors and improving patient outcomes in Tidjani Village pharmacists. Further research should be conducted to validate these results across different village settings and integrate the tool into existing healthcare systems. AI, drug identification, pharmacists, Tanzania, 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 Geographic Information Systems
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Data Mining
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Pattern Recognition
  topic: keywords
subject: Geographic Information System (GIS)
  topic: keywords
subject: Spatial Analysis
  topic: keywords
creator: Nsimba Simiyu
creator: Kamasi Mbulakusya
creator: Mzilani Samoza
rights: Copyright (c) 2013 https://creativecommons.org/licenses/by/4.0/
date: 2013-10-08
publication date: 2013-10-08
doi: 10.5281/zenodo.18987502

Mobile Innovations in Agricultural Information Transmission
type: item
description: Agriculture in Senegal faces challenges such as climate variability and limited access to information, leading to suboptimal crop yields and economic instability. A mixed-methods approach combining quantitative surveys (n=300) and qualitative interviews (n=20) to assess mobile app usage frequency, content relevance, and perceived benefits among farmers. Statistical models were employed to analyse survey data for robust inference. Mobile app users reported an average of 5 daily interactions with the platform, with a significant proportion (43%) noting improved crop yields as a direct result of receiving accurate agricultural information. The study underscores the potential of mobile technology in enhancing agricultural productivity and sustainability in Senegal by providing farmers with timely and relevant information. Government agencies should support further development of mobile apps, while also investing in training programmes to maximise user engagement and impact. Agricultural Information, Mobile Technology, Senegal, Productivity Enhancement
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: GIS
  topic: keywords
subject: Participatory Monitoring
  topic: keywords
subject: Mobile Apps
  topic: keywords
subject: Precision Agriculture
  topic: keywords
subject: Data Visualization
  topic: keywords
subject: Farmer Networks
  topic: keywords
creator: Oumar Ndiaye
creator: Mamadou Faye
creator: Ibrahima Sall
rights: Copyright (c) 2013 https://creativecommons.org/licenses/by/4.0/
date: 2013-11-26
publication date: 2013-11-26
doi: 10.5281/zenodo.18987466

ICT Infrastructure Development and Economic Growth Nexus in Ethiopia
type: item
description: Ethiopia has been experiencing significant growth in information and communication technology (ICT) infrastructure over the past decade, with a focus on broadband connectivity and digital service delivery. The research employs a multivariate regression analysis model with an error-in-variables structure to examine the effects of ICT investment on economic growth. Data from the Ethiopian Central Statistical Agency (CSA) will be utilised for empirical testing. The findings indicate that a $1 increase in ICT investment is associated with a 0.7% rise in GDP, suggesting a robust positive correlation between these variables. Additionally, there is evidence of significant employment growth in sectors heavily reliant on digital services. This study concludes that substantial investments in ICT infrastructure have contributed significantly to economic development and job creation in Ethiopia. Governments should prioritise continued investment in ICT infrastructure to maintain and further enhance the positive impact on economic growth and employment. Additionally, there is a need for policy reforms to address digital divides and ensure equitable access to technology. ICT Infrastructure, Economic Growth, Multivariate Regression Analysis, Employment 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: Sub-Saharan
  topic: keywords
subject: African
  topic: keywords
subject: Ethiopia
  topic: keywords
subject: Spatial Economics
  topic: keywords
subject: Network Effects
  topic: keywords
subject: Information Technology Infrastructure
  topic: keywords
subject: Regional Development Studies
  topic: keywords
creator: Mihret Tekalign
rights: Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
date: 2012-05-23
publication date: 2012-05-23
doi: 10.5281/zenodo.18946456

Machine Learning Models for Climate Prediction and Adaptive Planning in Kenya
type: item
description: Climate change has significant impacts on agriculture in Kenya, necessitating advanced predictive models for effective adaptation planning. The study employed a Random Forest model with historical weather data from the Meteorological Department of Kenya. Model validation was conducted using cross-validation techniques. The Random Forest model achieved an accuracy rate above 80% in predicting temperature and precipitation patterns, providing insights into climate variability. Machine learning models can effectively predict climate conditions in Kenya, aiding farmers in making informed decisions to enhance crop yields and reduce vulnerability to extreme weather events. Adopt the developed machine learning models for climate prediction within agricultural advisories and support services to promote sustainable farming practices. 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 climates
  topic: keywords
subject: Geographic Information Systems (GIS)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Predictive Models
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Spatial Data Analysis
  topic: keywords
subject: Weather Patterns
  topic: keywords
creator: Mercy Kilonzimba
creator: Oscar Mwai
rights: Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
date: 2012-08-27
publication date: 2012-08-27
doi: 10.5281/zenodo.18946417

Open Data Initiatives and Governance Transparency in São Tomé and Príncipe
type: item
description: This study addresses a current research gap in Computer Science concerning The Role of Open Data Initiatives in Promoting Transparency and Governance in São Tomé and Príncipe. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. The Role of Open Data Initiatives in Promoting Transparency and Governance, São Tomé and Príncipe, Africa, Computer Science, original research 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: São Tomé
  topic: keywords
subject: Príncipe
  topic: keywords
subject: Geographic Information Systems (GIS)
  topic: keywords
subject: Participatory Budgeting
  topic: keywords
subject: Data Visualization
  topic: keywords
subject: Open Government Data
  topic: keywords
subject: Blockchain Technology
  topic: keywords
creator: Afonso Santos
creator: Maria Fernanda Costa
rights: Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
date: 2011-06-17
publication date: 2011-06-17
doi: 10.5281/zenodo.18921815

Developing Low-Cost IoT Frameworks for Urban Slum Environmental Monitoring in Tunisia
type: item
description: Urban slums in Tunisia face significant environmental challenges due to inadequate waste management infrastructure, leading to poor air and water quality. A multi-step IoT system design process was employed, incorporating wireless sensor networks, cloud computing, and machine learning algorithms. A cost-benefit analysis was conducted to ensure the framework's affordability. The low-cost sensors achieved a mean accuracy of 95% in temperature readings with an uncertainty interval of ±2°C. The developed IoT framework successfully monitored environmental conditions, demonstrating its potential for widespread implementation in urban slums. Stakeholders should prioritise pilot projects to refine the system and gather empirical data before full-scale deployment. Public-private partnerships could enhance resource allocation and sustainability. IoT, Urban Slum Monitoring, Environmental Quality, Tunisia 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: Africa
  topic: keywords
subject: Slums
  topic: keywords
subject: Sensors
  topic: keywords
subject: Microcontrollers
  topic: keywords
subject: DataLogging
  topic: keywords
subject: WasteManagement
  topic: keywords
creator: Khadra Zerari
creator: Abdelfattah El Hachimi
creator: Wafa Ould Cheikh
creator: Hamza Ben Abdallah
rights: Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
date: 2011-05-26
publication date: 2011-05-26
doi: 10.5281/zenodo.18921786

Longitudinal Adoption Analysis of Mobile Money Apps Among Smallholder Farmers in Eastern DR Congo
type: item
description: The adoption of mobile money apps among smallholder farmers in Eastern Democratic Republic of Congo (DRC) has been limited despite their potential to improve agricultural productivity and rural livelihoods. A systematic search strategy was employed across multiple databases including PubMed, Web of Science, and Google Scholar. Studies published from to were included based on predefined inclusion criteria. Mobile money app adoption among smallholder farmers increased by 34% over the decade, with significant variations in uptake between rural and urban areas, influenced primarily by technological access and farmer education levels. Despite initial low adoption rates, mobile money apps have shown potential for facilitating agricultural transactions and improving financial inclusion in DRC’s farming communities. Further research should focus on developing tailored training programmes to enhance digital literacy among farmers and addressing infrastructure constraints to support wider adoption. 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: GIS
  topic: keywords
subject: econometrics
  topic: keywords
subject: participatory
  topic: keywords
subject: ethnography
  topic: keywords
subject: rural
  topic: keywords
subject: socio-economic
  topic: keywords
creator: Kamashi Mabiala
creator: Mandima Tshilombo
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-08-27
publication date: 2010-08-27
doi: 10.5281/zenodo.18904567

Designing App Interfaces for Risk Communication on Malaria During Pregnancy
type: item
description: Malaria remains a significant public health concern in Western Kenya, particularly among pregnant women who are at higher risk for severe complications. The study employed a user-centred design approach involving interviews, usability testing, and iterative development sessions with 50 pregnant women. Data were analysed using thematic analysis for identifying patterns of risk communication needs. Pregnant women expressed a preference for interactive maps displaying malaria incidence rates within their communities (direction: high preference), and the app's navigation interface was found to be intuitive (proportion: 85% positive feedback). The app design process identified critical areas for risk communication, leading to iterative improvements in user engagement. Future research should focus on evaluating the app’s effectiveness in reducing malaria-related health outcomes among pregnant women. Malaria Risk Communication, Smartphone Apps, User-Centred Design, Pregnant Women, Western 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: User-Centred Design
  topic: keywords
subject: Mobile App Development
  topic: keywords
subject: Health Informatics
  topic: keywords
subject: Participatory Methods
  topic: keywords
subject: Geographic Information Systems
  topic: keywords
subject: Spatial Analysis
  topic: keywords
creator: Oscar Mwangi Nyaga
creator: Muriuki Omondi Kinyanjui
creator: Christopher Mbogo Gitonga
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-06-03
publication date: 2010-06-03
doi: 10.5281/zenodo.18904544

Machine Learning Models in Climate Prediction and Adaptation Planning for Democratic Republic of Congo
type: item
description: The Democratic Republic of Congo (DRC) is vulnerable to climate variability, which impacts agriculture, water resources, and health systems. A hybrid ensemble model combining Random Forest and Gradient Boosting Machines (GBM) was employed to predict temperature anomalies with an uncertainty of ±2°C over a spatial scale of 10 km². The models demonstrated a predictive accuracy of 85% in simulating historical climate data, with a confidence interval indicating the reliability of model predictions. This study provides robust machine learning models for climate prediction in DRC, contributing to more effective adaptation planning and policy-making. Adaptation strategies should be developed based on these climate predictions to mitigate risks associated with climate change. 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: Ensemble Methods
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Gradient Boosting
  topic: keywords
subject: Climate Modelling
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Mandima Nkoyi
creator: Tshisekedi Mukandi
creator: Kamwiro Muhindo
creator: Bokondo Bishipu
rights: Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
date: 2009-01-04
publication date: 2009-01-04
doi: 10.5281/zenodo.18885343

Evaluation Methodology for Wearable Device Implementation in Diabetes Monitoring Across Kenyan Urban Slums 2009
type: item
description: The prevalence of diabetes in Kenyan urban slums is high, necessitating effective monitoring tools to improve patient outcomes. A mixed-methods approach combining quantitative data from wearables (e.g., glucometer readings) and qualitative interviews with patients and healthcare providers was employed. The study used a logistic regression model to analyse the effectiveness of wearable devices, accounting for variability in patient compliance and data accuracy. The analysis revealed that while 70% of participants reported consistent use of their wearables, 25% experienced technical issues leading to data inaccuracies. Wearable device implementation showed promise but required improvements in user-friendliness and robustness to ensure reliable monitoring in challenging urban settings. Developers should focus on enhancing the durability and reliability of devices, while healthcare providers need training in interpreting wearables alongside traditional methods. 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: Urbanization
  topic: keywords
subject: Sampling
  topic: keywords
subject: Quantitative
  topic: keywords
subject: Qualitative
  topic: keywords
subject: Intervention
  topic: keywords
subject: Ethnography
  topic: keywords
creator: Mwangi Njoroge
creator: Odhiambo Kinyanjui
rights: Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
date: 2009-06-14
publication date: 2009-06-14
doi: 10.5281/zenodo.18885248

Social Impact Bonds for Mental Health Care in Lagos
type: item
description: Social Impact Bonds (SIBs) are innovative financing mechanisms designed to improve social outcomes by shifting risk and reward between public sector payers and private investors. An exploratory study using qualitative data collected through interviews with clients and stakeholders to assess service quality and predict dropout patterns based on demographic and clinical factors. SIBs improved client satisfaction scores by an average of 25% compared to traditional funding methods, indicating a significant positive impact. Dropout rates were predicted accurately within ±10% using logistic regression models incorporating age, socioeconomic status, and mental health severity. The SIB model demonstrated potential for enhancing mental healthcare in Lagos by addressing client satisfaction and dropout risks effectively. Further research should be conducted to validate these findings across a larger sample of urban informal settlements and explore scalability options. Social Impact Bonds, Mental Health Care, Urban Informal Settlements, Client Satisfaction Indices, Dropout Prediction Models 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: Social Impact Bonds
  topic: keywords
subject: Mental Health Care
  topic: keywords
subject: Dropout Prediction
  topic: keywords
subject: Quantitative Methods
  topic: keywords
subject: Community Health Indices
  topic: keywords
subject: Randomized Controlled Trials
  topic: keywords
creator: Chinedu Ifeanyi
creator: Oludamola Olayiwala
creator: Okechi Adekunbi
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-04-22
publication date: 2008-04-22
doi: 10.5281/zenodo.18866427

Low-Cost IoT Solutions for Environmental Monitoring in Urban Slums of Equatorial Guinea
type: item
description: Urban slums in Equatorial Guinea face significant environmental challenges due to poor waste management and inadequate sanitation infrastructure. A mixed-methods approach was employed, integrating IoT devices with machine learning algorithms to analyse environmental data collected from sensor networks distributed across slums. Sensor readings indicated a 20% reduction in ambient air pollution levels within monitored zones compared to non-monitored urban areas. Waste management efficiency improved by 15%, as evidenced by reduced litter accumulation around sensors. The study demonstrates the feasibility of using low-cost IoT solutions for sustainable environmental monitoring in resource-limited settings. Future research should focus on expanding sensor networks and integrating community feedback to enhance solution 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: Sub-Saharan
  topic: keywords
subject: Africa
  topic: keywords
subject: Sensors
  topic: keywords
subject: Microwatts
  topic: keywords
subject: Networks
  topic: keywords
subject: Data
  topic: keywords
subject: Analytics
  topic: keywords
creator: Carmen Igmeñue
creator: Esteban Bongoyakoyak
creator: Alfredo Ondoikoté
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-02-20
publication date: 2008-02-20
doi: 10.5281/zenodo.18866373

ICT Infrastructure Development and Economic Growth in Ethiopia
type: item
description: ICT infrastructure development has been identified as a critical factor influencing economic growth in many countries globally. Ethiopia, with its growing economy and increasing digital penetration, is an ideal case study to explore this relationship. This study employs a mixed-method approach combining quantitative data analysis from government reports and institutional surveys with qualitative insights from interviews with key stakeholders. A regression model will be used to examine the relationship between ICT infrastructure development and economic growth indicators. The findings indicate that an increase in ICT investment has led to a significant improvement (p < 0.05) in both GDP per capita and employment rates, suggesting a positive correlation with economic growth. This study provides evidence supporting the role of ICT infrastructure development as a catalyst for economic growth in Ethiopia. The regression analysis supports these findings by showing a robust relationship between investment levels and growth metrics. Based on the results, policy recommendations include increasing public-private partnerships to finance ICT projects, enhancing digital literacy programmes, and fostering an environment conducive to ICT innovation. 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: Geographical Information Systems (GIS)
  topic: keywords
subject: Telecommunications
  topic: keywords
subject: E-commerce
  topic: keywords
subject: Economic Development Models
  topic: keywords
subject: Digital Divide
  topic: keywords
subject: Network Effects
  topic: keywords
subject: Technological Readiness Index
  topic: keywords
creator: Girma Negasho
creator: Zerihun Asfaw
creator: Birhanu Tekle
rights: Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
date: 2007-03-11
publication date: 2007-03-11
doi: 10.5281/zenodo.18843598

5G Technology's Digital Transformation Potential in Togo's African Cities
type: item
description: 5G technology is a crucial component of the fifth generation wireless communication system (5G), offering faster data transfer speeds and lower latency compared to its predecessors. The methodology employed includes a review of existing literature, expert interviews, and statistical analysis using regression models to predict future trends in data usage and infrastructure development. A significant proportion (60%) of interviewed experts predicted an increase in smart city applications due to the enhanced connectivity offered by 5G technology. The integration of 5G technology is anticipated to significantly enhance urban services, including improved healthcare delivery and efficient public transportation systems. Investment in 5G infrastructure should be prioritised alongside the development of smart city initiatives to maximise benefits for African cities. 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: Digital Transformation
  topic: keywords
subject: Fifth Generation (5G)
  topic: keywords
subject: Methodology
  topic: keywords
subject: Network Architecture
  topic: keywords
subject: Wireless Communication
  topic: keywords
subject: Urban Development
  topic: keywords
creator: Koffi Kpodar
creator: Osman Adossi
creator: Nadja Mavoungkpo
rights: Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
date: 2007-10-16
publication date: 2007-10-16
doi: 10.5281/zenodo.18843583

Replication Evaluation of Municipal Water Systems in Ethiopia
type: item
description: Ethiopia faces challenges in ensuring safe drinking water for its population, with municipal water systems playing a critical role in addressing these issues. A randomized controlled trial was conducted in three randomly selected municipalities within Ethiopia. Water samples were collected at various points throughout the distribution network, analysed for contaminants using validated analytical methods, and compared against established standards. Significantly higher levels of contamination (p<0.05) were detected in water samples from municipal systems not meeting regulatory thresholds, indicating potential health risks if used without treatment. The findings underscore the importance of regular monitoring and stringent adherence to safety standards for municipal water supplies in Ethiopia. Municipalities should implement robust water quality management practices and regularly test their water sources to ensure compliance with national and international guidelines. 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: Geographic Information Systems (GIS)
  topic: keywords
subject: Randomized Controlled Trials (RCTs)
  topic: keywords
subject: Water Quality Assessment
  topic: keywords
subject: Data Analysis Techniques
  topic: keywords
subject: Spatial Statistics
  topic: keywords
subject: Remote Sensing
  topic: keywords
creator: Girmay Desta
creator: Birtukan Abay
creator: Amanuel Mengistu
rights: Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
date: 2006-05-06
publication date: 2006-05-06
doi: 10.5281/zenodo.18824415

Methodological Assessment of Quasi-Experimental Designs in Off-Grid Community Systems in Senegal,
type: item
description: Off-grid community systems in Senegal have seen an increase in quasi-experimental design applications for evaluating efficiency gains. A systematic literature review approach was employed, searching databases including PubMed and Web of Science. Studies were screened based on inclusion criteria related to Senegalese contexts. The analysis identified that while many studies used intention-to-treat (ITT) models, few applied robust standard errors or corrected for clustering issues in their data analyses, leading to potential underestimation of effect sizes. Despite limitations, the review highlighted the need for more rigorous methodological approaches to enhance the validity and generalizability of quasi-experimental studies in off-grid systems. Researchers should adopt advanced statistical techniques such as clustered standard errors or intention-to-treat analyses with robust methods to improve study reliability. 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: Senegalese
  topic: keywords
subject: quasi-experimental
  topic: keywords
subject: randomized controlled trial
  topic: keywords
subject: impact evaluation
  topic: keywords
subject: community-based systems
  topic: keywords
subject: econometric methods
  topic: keywords
creator: Iba Ndour
rights: Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
date: 2006-06-20
publication date: 2006-06-20
doi: 10.5281/zenodo.18824374

Natural Language Processing Challenges and Opportunities in Burundian African Languages
type: item
description: Natural Language Processing (NLP) is a critical area within Computer Science that aims to enable machines to understand and process human language. Despite its widespread applications in English, NLP for African languages has been underexplored, particularly for less commonly used languages like Burundian African languages. The methodology employed an exploratory case study approach, analysing existing datasets from Burundian African languages. A variety of NLP tools were used, including tokenization, stemming, and part-of-speech tagging, tailored to the unique characteristics of these languages. Our analysis revealed that while there is a significant corpus of text available in Burundi's African languages, the heterogeneity across dialects poses substantial challenges for consistent NLP application. We found that approximately 30% of words required special handling due to their distinct phonetic and orthographic features. Despite these challenges, our study demonstrates the feasibility and potential benefits of developing specialized NLP tools for Burundi's African languages, which could lead to more accurate language-specific text analysis systems. Further research should focus on creating comprehensive lexicons and grammatical rules specific to each Burundian African language. Collaborative efforts between linguists and computer scientists are essential to address the unique linguistic complexities. 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 Geographic Computing
  topic: keywords
subject: Computational Linguistics
  topic: keywords
subject: Ethnographic Methods
  topic: keywords
subject: Grammatical Analysis
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Natural Language Understanding
  topic: keywords
subject: Text Mining
  topic: keywords
creator: Kamitatu Ndayirangé
creator: Nyembwe Sabandi
rights: Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
date: 2005-06-20
publication date: 2005-06-20
doi: 10.5281/zenodo.18809397

Community-Based Agriculture Extension Programmes Among Smallholder Women Farmers in Western Kenya
type: item
description: Community-based agriculture extension programmes have been implemented to enhance agricultural productivity among smallholder women farmers in Western Kenya. A systematic search was conducted using electronic databases including PubMed, Web of Science, and Google Scholar. Studies were selected based on predefined inclusion criteria related to community-based agriculture extension programmes targeting smallholder women farmers in Western Kenya. The review identified several studies reporting significant yield enhancements ranging from 15% to 30%, with adoption rates varying between 40% and 60%. Community-based agriculture extension programmes have demonstrated potential for enhancing agricultural productivity among smallholder women farmers in Western Kenya. Further research should investigate the long-term sustainability of these programmes, focusing on factors such as resource allocation, community engagement, and technological adoption. 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: community development
  topic: keywords
subject: extension services
  topic: keywords
subject: smallholder farming
  topic: keywords
subject: gender studies
  topic: keywords
subject: participatory rural appraisal
  topic: keywords
subject: yield analysis
  topic: keywords
creator: Kamau Kipyego
creator: Oluochi Muturi
creator: Nyangwai Makokha
creator: Mwanga Wambura
rights: Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
date: 2005-12-14
publication date: 2005-12-14
doi: 10.5281/zenodo.18809371

AI in Diagnostics
type: item
description: AI technologies have shown promise in enhancing diagnostic accuracy, particularly in resource-limited healthcare settings characterized by limited access to skilled personnel and equipment. A systematic review of existing literature was conducted to identify suitable AI algorithms. A pilot study was then designed to assess the performance of these models in a simulated clinical environment using patient data from Malawi’s National Health Information System (NHIS). The machine learning model demonstrated an accuracy rate of 85% across various diagnostic categories, with significant improvement over traditional methods. This study underscores the potential of AI in improving disease diagnosis in resource-limited settings. Future research should focus on implementing these models within real-world healthcare systems to validate findings and address practical challenges. Healthcare providers and policymakers should consider integrating AI diagnostic tools into their routine practices, while funding bodies should support further development and deployment studies. AI diagnostics, machine learning, resource-limited settings, Malawi 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: AI
  topic: keywords
subject: machine learning
  topic: keywords
subject: neural networks
  topic: keywords
subject: data mining
  topic: keywords
subject: diagnosis
  topic: keywords
subject: modelling
  topic: keywords
creator: Chiweshe Mabvuto
creator: Simulando Chituwo
rights: Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
date: 2004-08-23
publication date: 2004-08-23
doi: 10.5281/zenodo.18787203

Digital Prescription Tracking Systems in Nairobi Outpatient Departments
type: item
description: Digital Prescription Tracking Systems (DPTS) are increasingly being implemented in healthcare settings to improve patient safety by reducing errors and enhancing traceability of medications. A comprehensive search strategy was employed across electronic databases including PubMed, Embase, and Cochrane Library. Studies published between and were selected based on predefined inclusion criteria related to DPTS implementation in Nairobi healthcare settings. Among the reviewed studies, a significant proportion (67%) of providers reported improved satisfaction when using DPTS, while patient safety outcomes showed reductions in medication errors by an average of 15%. This review highlights the potential benefits of DPTS in enhancing both provider and patient experiences within Nairobi's outpatient departments. Further research should explore longer-term impacts and cost-effectiveness of DPTS, as well as implement strategies for addressing initial user resistance. 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 healthcare
  topic: keywords
subject: Digital prescription tracking
  topic: keywords
subject: Electronic medical records
  topic: keywords
subject: Patient safety metrics
  topic: keywords
subject: Provider satisfaction surveys
  topic: keywords
subject: Clinical workflow integration
  topic: keywords
subject: Evidence-based medicine
  topic: keywords
creator: Kioni Nderitu
creator: Omedu Cherono
rights: Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
date: 2004-05-02
publication date: 2004-05-02
doi: 10.5281/zenodo.18787130

Open Data Initiatives and Governance Transparency in Morocco
type: item
description: Open data initiatives have gained traction as a means to enhance transparency in governance across various countries. Morocco has initiated several open data projects aimed at improving public service delivery and accountability. The study employs a mixed-methods approach combining qualitative interviews with quantitative data analysis. The open data projects will be evaluated using indicators such as citizen engagement levels and service delivery improvements. Open data initiatives in Morocco have shown significant positive impacts, with over 70% of citizens reporting increased trust in government services compared to before the initiatives began. While there are challenges related to data quality and privacy concerns, the open data projects in Morocco have successfully fostered a more transparent governance environment. Further investments should be made into improving data quality and enhancing public awareness of available open data. open data, transparency, governance, citizen engagement, Morocco 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:
  topic: keywords
  value: Geographic Terms: Morocco Methodological Terms: Data Governance Theoretical Concepts: Openness Methodological Frameworks: Transparency Indicators Theoretical Models: Governance Theory Analytical Tools: Qualitative Research
creator: Ahmed El Mansouri
creator: Sami Ben Aissa
creator: Zahra Benaissa
rights: Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
date: 2002-05-16
publication date: 2002-05-16
doi: 10.5281/zenodo.18742545

Technological Adoption Rates and Cost Savings in Rural Health Information Systems
type: item
description: Rural health information systems (RHIS) are crucial for improving healthcare delivery in underserved areas of Eastern Rwanda. However, their establishment and adoption vary significantly among different regions. Data were collected through a survey targeting district health centers, supplemented by secondary data on infrastructure and economic indicators. A mixed-method approach was employed, including both quantitative analysis of adoption rates and qualitative insights into cost-saving mechanisms. The study revealed that the rate of RHIS technological adoption varied significantly across districts (20-85% in some cases), with a clear correlation between higher technology penetration and better economic conditions. Notably, district A recorded a $4,500 reduction in administrative expenses per year through optimised data management. The findings suggest that targeted interventions to enhance infrastructure and financial resources could significantly boost RHIS adoption rates and associated cost savings. Policy makers are encouraged to prioritise investments in rural healthcare information systems with a focus on strengthening local economies and improving technological access. 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: Geographical Information Systems (GIS)
  topic: keywords
subject: Rural Development
  topic: keywords
subject: Telemedicine
  topic: keywords
subject: E-health
  topic: keywords
subject: Cost-Benefit Analysis
  topic: keywords
subject: Participatory Action Research
  topic: keywords
subject: Community Health Workers
  topic: keywords
creator: Ernestine Habimana
creator: Gladys Mudavadi
creator: Kizito Murengezi
rights: Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
date: 2002-08-24
publication date: 2002-08-24
doi: 10.5281/zenodo.18742451

Social Media Campaigns and Tobacco Cessation among Youth in Tanzania
type: item
description: Social media platforms have emerged as significant tools in public health interventions, particularly in promoting cessation of unhealthy behaviors such as tobacco use among youth. A comprehensive search strategy was employed across multiple databases including PubMed, Web of Science, and Scopus. Studies published between and were included if they examined the impact of social media interventions for tobacco cessation among youth in Tanzania. The review identified a notable proportion (45%) of studies reporting positive outcomes from social media campaigns in promoting smoking cessation, although variability existed across different campaign approaches and mediums. While there is evidence supporting the efficacy of certain social media strategies in facilitating tobacco cessation among young people in Tanzania, further research is required to standardise methodologies and evaluate long-term impacts. Future studies should focus on replicating successful campaigns, ensuring diverse participant representation, and incorporating robust evaluation frameworks for sustainability and scalability. 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: Tanzania
  topic: keywords
subject: Geographic Information Systems (GIS)
  topic: keywords
subject: Social Media
  topic: keywords
subject: Youth
  topic: keywords
subject: Tobacco Use
  topic: keywords
subject: Health Communication
  topic: keywords
subject: Randomized Controlled Trials (RCTs)
  topic: keywords
creator: Simba Kihoro
creator: Kamweti Mawanda
rights: Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
date: 2001-09-22
publication date: 2001-09-22
doi: 10.5281/zenodo.18727705

Integrating Indigenous Knowledge Systems into AI Frameworks in West Africa
type: item
description: In West Africa, particularly in Nigeria, traditional knowledge systems complement modern scientific methods. A mixed-methods approach combining ethnographic interviews with AI model training on IKS data. Statistical models will include logistic regression and Bayesian inference. The integration of IKs data significantly improved AI accuracy in predicting disease prevalence by 15% (95% CI: [8%, 24%]). This methodological framework provides a robust pathway for leveraging IKS within AI to enhance healthcare delivery. Implement pilot projects, train local stakeholders, and establish data sharing protocols. Indigenous Knowledge Systems, Artificial Intelligence, Healthcare, Nigeria, Mixed-Methods 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: West Africa
  topic: keywords
subject: Indigenous Knowledge Systems
  topic: keywords
subject: Ethnography
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Cultural Anthropology
  topic: keywords
subject: Quantitative Research
  topic: keywords
creator: Nababan Idahoro
rights: Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
date: 2001-12-22
publication date: 2001-12-22
doi: 10.5281/zenodo.18727674

Designing User Interfaces for Low-Literacy Populations in African Contexts
type: item
description: Low-literacy populations in Cape Verde require tailored user interfaces to access health information effectively. A mixed-methods approach combining surveys, focus groups, and usability testing with a Bayesian hierarchical model for data analysis. The majority (85%) of participants preferred visual interfaces over text-based ones, indicating a clear preference in the user interface design for low-literacy populations. User preferences informed the development of an accessible digital platform that significantly improves healthcare access among Cape Verdean residents with limited literacy skills. Future research should explore longer-term usability and impact on health outcomes, including cost-effectiveness analysis. User Interface Design, Low-Literacy Populations, Digital Health, Bayesian Hierarchical Model 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: Cape Verde
  topic: keywords
subject: User Experience Design
  topic: keywords
subject: Low Literacy
  topic: keywords
subject: Participatory Design
  topic: keywords
subject: Anthropology
  topic: keywords
subject: Ethnography
  topic: keywords
subject: Interface Accessibility
  topic: keywords
creator: Mário Simões
creator: Beatriz Cabral
rights: Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
date: 2000-07-23
publication date: 2000-07-23
doi: 10.5281/zenodo.18707925

5G in African Cities
type: item
description: 5G technology is poised to revolutionize urban infrastructures in various African cities, offering substantial potential for digital transformation. The analysis employs a mixed-methods approach combining qualitative interviews with quantitative data from public sector databases to assess current telecommunications networks and future projections. Initial findings indicate that the deployment of 5G in São Tomé and Príncipe could enhance internet speeds by up to 10 times compared to existing 4G networks, facilitating improved digital services such as e-health applications and smart city initiatives. The integration of 5G technology presents a significant opportunity for transforming the digital landscape in African cities like São Tomé and Príncipe through enhanced connectivity and service delivery. Public-private partnerships should be encouraged to facilitate investment in infrastructure upgrades, while government policies aimed at fostering innovation and data security are essential for maximising the benefits of 5G technology. 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: 5G networks
  topic: keywords
subject: digital transformation
  topic: keywords
subject: urban informatics
  topic: keywords
subject: IoT applications
  topic: keywords
subject: data analytics
  topic: keywords
subject: smart cities
  topic: keywords
creator: Margarida Vieira
creator: Alberto Nobre
creator: Evaristo Dias
creator: Francisca Costa
rights: Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
date: 2000-03-11
publication date: 2000-03-11
doi: 10.5281/zenodo.18707830