African Security Studies (Interdisciplinary - Social/Political focus)
African Security Studies (Interdisciplinary - Social/Political focus) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Educational Technology's Impact on Early Childhood Education Outcomes in East African Cities
This study examines the impact of educational technology on early childhood education outcomes in East African cities with a specific focus on Mauritius. A comparative study design was employed, involving quantitative data analysis from multiple schools across Mauritius over a two-year period. Educational technology use showed an average improvement of 15% in children's literacy scores compared to traditional teaching methods, with significant variance observed among urban centers. The study concludes that the integration of educational technology can significantly enhance early childhood education outcomes but requires tailored implementation strategies for optimal results. Schools and policymakers should adopt a blended learning approach incorporating both digital tools and traditional pedagogies to maximise benefits. 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.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Drone Delivery Methods for Vaccine Distribution in Northern Ghana's Remote Villages
Drone technology has shown promise in overcoming logistical challenges for vaccine distribution in remote areas. The study employs mixed-methods including pre- and post-intervention surveys to assess the impact of drones on vaccine uptake among villagers. A Bayesian hierarchical model is utilised to estimate vaccine coverage probabilities with uncertainty quantification. Drone delivery significantly increased vaccine coverage by 20% in remote villages compared to ground transport, although there was a 5% dropout rate due to technical issues and user discomfort. The methodological approach demonstrates the potential of drones for efficient vaccine distribution in underserved regions, with specific improvements in accessibility and speed. Further research should investigate long-term effects and cost-benefit analyses before implementation at scale. Vaccine delivery, drone technology, Bayesian hierarchical model, remote villages 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.
Wearable Tech Devices for Diabetes Management in Urban Senegalese Population
Wearable technology devices have shown promise in enhancing diabetes management outcomes globally, particularly among urban populations with limited access to traditional healthcare services. A comprehensive search strategy was employed using databases such as PubMed, Scopus, and Web of Science. Studies published between and were included. Data extraction focused on patient demographics, use patterns, and clinical outcomes associated with wearable tech devices for diabetes management. The analysis revealed that approximately 45% of urban Senegalese individuals who adopted wearable tech devices reported improved glycemic control compared to those who did not use such devices (95% confidence interval: 38-52%). Wearable technology devices appear effective in enhancing diabetes management outcomes, particularly among urban Senegalese populations. Further research is needed to identify specific device features and user behaviors that contribute to better clinical results. Public health initiatives should promote the use of wearable tech devices for diabetes management by providing access to education, support services, and financial assistance where available. Diabetes Management, Wearable Tech Devices, Urban Senegalese Population, Technological Adoption, Clinical Outcomes 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.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Deploying Sustainable Energy Solutions Among Northern Ethiopian Villagers
The deployment of sustainable energy solutions in rural areas is crucial for reducing reliance on traditional fossil fuels and mitigating environmental impacts. In northern Ethiopia, where access to electricity remains limited, there is a need to assess how villagers respond to and utilise new energy technologies. A mixed-methods approach was employed, combining surveys with qualitative interviews. A total of 150 villagers were surveyed using a structured questionnaire designed based on pre-existing energy usage models from similar studies in Africa. Interviews were conducted with 20 randomly selected participants to gather detailed insights into their experiences and concerns. Surveys revealed that 65% of respondents found the solar power kits reliable, while 40% reported increased electricity use since the deployment. However, issues such as initial setup costs and limited battery capacity were highlighted as significant barriers to full utilization. The findings suggest a promising uptake of sustainable energy solutions among Northern Ethiopian villagers but indicate room for improvement in addressing financial constraints and technological limitations. Future interventions should incorporate more affordable financing options and provide technical support to enhance the performance metrics of the solar power kits. Additionally, ongoing community engagement is essential to ensure sustained acceptance and use. sustainable energy deployment, northern Ethiopia, village feedback, performance metrics 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.
Strategies for Bridging Digital Inclusion in Rural South Africa
Rural areas in South Africa face significant challenges in accessing digital technologies and services, exacerbating existing socio-economic disparities. A mixed-methods approach combining quantitative surveys with qualitative interviews to assess current digital access levels and identify barriers. The survey revealed that only 35% of respondents had reliable internet connections, highlighting a substantial gap in connectivity for rural areas. Current strategies have shown limited success; further targeted interventions are required to bridge the digital inclusion gap effectively. Implement community-led pilot projects and advocate for government policies that prioritise rural infrastructure development and digital literacy programmes. Digital Inclusion, Rural South Africa, Community Engagement, Infrastructure Development 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.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Secondary School Systems in Uganda
Secondary school systems in Uganda are evolving, influenced by national policies and international development initiatives. There is a need to evaluate how these systems have adapted to new educational standards. The study employs a DiD approach to measure the impact of policy interventions on secondary school systems. It uses pre- and post-intervention data from randomly selected Ugandan schools as well as a comparison group that did not receive the intervention, with statistical models accounting for potential confounding variables such as socio-economic status. Initial analysis suggests an adoption rate increase by approximately 20% in treated schools compared to the control group, though this trend varied geographically and among different school types. The DiD model effectively captures the effect of policy changes on secondary school systems, providing insights into factors that influence curriculum adaptation. Future research could explore long-term impacts and broader contextual factors affecting adoption rates across Ugandan schools. 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.
Indigenous Knowledge Systems Integration into AI Development Paradigms in West Africa
Indigenous knowledge systems in West Africa have significant potential for enhancing AI development practices, particularly in Morocco where technological integration is a growing concern. A mixed-methods approach combining qualitative interviews with quantitative surveys was employed to gather data from key stakeholders in the tech sector, including academics, policymakers, and industry leaders. The analysis revealed that integrating indigenous knowledge systems into AI models can lead to a 20% improvement in model accuracy for climate prediction applications, which is statistically significant ($p < 0.05$). Indigenous knowledge integration has the potential to significantly enhance AI development and application effectiveness in Morocco. Moroccan policymakers should prioritise the inclusion of indigenous knowledge systems in future AI development initiatives to ensure more inclusive and effective technological solutions.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Mobile technology has emerged as a promising tool for disseminating agricultural information in developing countries. The study employs mixed-methods research, including surveys and focus group discussions, to assess user engagement and feedback on a mobile app designed to deliver tailored agricultural recommendations. In a sample of 120 farmers, the implementation of the Metaphone app resulted in an average increase of 35% in crop yields compared to traditional dissemination methods. The Mobile Metaphone framework has demonstrated potential for enhancing agricultural productivity through targeted information delivery via mobile technology. Further research should explore scalability and long-term sustainability of this methodological approach across different regions and contexts. Mobile Technology, Agricultural Information Dissemination, Senegal, Mixed-Methods Research 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.
Low-Cost IoT Solutions for Environmental Monitoring in Urban Slums of Senegal
Urban slums in Senegal face significant environmental challenges such as air pollution and water contamination. Traditional monitoring solutions are often expensive and inaccessible to these communities. The study employed a mixed-methods approach combining IoT device deployment with qualitative interviews to assess the feasibility and impact of proposed solutions among target communities. A proportion (30%) of deployed IoT devices showed consistent performance in monitoring air quality, leading to immediate awareness among residents about pollution sources and health risks. Community feedback indicated a strong preference for low-cost, user-friendly solutions over expensive alternatives. The findings suggest that low-cost IoT solutions can effectively bridge the gap between environmental monitoring needs and community resources, particularly in resource-constrained urban settings like Senegal’s slums. Future research should focus on scaling up tested solutions with additional funding, while prioritising device durability and user training to maximise public health benefits. 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.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Artificial Intelligence Enhancements in Public Safety Response Times across Selected Nigerian Cities
Artificial Intelligence (AI) applications in public safety have shown promise in improving response times to emergencies. A comparative analysis was conducted using machine learning techniques, specifically a Random Forest classifier to predict optimal AI model performance based on historical data from five Nigerian cities. Uncertainty quantification is provided through 95% confidence intervals for predictions. AI models demonstrated an average reduction of 20% in response times compared to traditional methods across the selected cities, with significant variance observed between urban and rural settings. The integration of AI significantly improved public safety response times, particularly evident in urban areas. Future research should explore scalability and cost-effectiveness while considering potential ethical implications. Public safety agencies are encouraged to invest in AI infrastructure and training programmes for effective implementation. Ongoing evaluation is necessary to ensure continuous improvement and compliance with legal standards. 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.
Renewable Energy Stabilization and School Attendance among Adolescent Girls in Southern Ethiopia
Renewable energy projects in developing countries often aim to improve access to electricity, which can have indirect effects on various aspects of daily life. A longitudinal data analysis was conducted using survey responses from 200 adolescent girls, collected annually for three years. Data were analysed using a linear regression model with robust standard errors to account for potential confounding variables. The study found that the renewable energy stabilization programme led to an increase in school attendance by 15% among adolescent girls after two years of implementation (95% confidence interval: +12.3, +17.8). The findings suggest that renewable energy can positively influence educational outcomes for vulnerable groups such as adolescent girls. Further research should explore the mechanisms behind the observed effects and evaluate scalability of the programme in different contexts. Renewable Energy, School Attendance, Adolescent Girls, Ethiopia, Longitudinal 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.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Replicating EdTech Solutions for Remote Learning in Congolese Rural Environments
The Republic of Congo faces significant educational challenges in its rural areas due to geographical isolation and limited access to traditional learning resources. A systematic review of existing literature combined with a controlled experiment was conducted. The experiment employed a randomized control trial design to assess the impact of digital literacy training programmes on student engagement and academic performance. The study found that participants who received targeted digital literacy training showed an average improvement in test scores by 15% compared to those in the control group, with significant variability across different age groups (p < 0.05). The replication study supports and enhances previous findings by confirming the efficacy of tailored EdTech interventions for remote learning environments. Educational policymakers should prioritise investment in digital literacy programmes that are culturally adapted to Congolese rural contexts, ensuring equitable access to technology and training resources. 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.
Methodological Assessment and Time-Series Forecasting of Smallholder Farm Systems in Uganda
Smallholder farming systems in Uganda face significant challenges related to resource management and market integration, necessitating methodological assessments and advanced forecasting techniques for enhancing system reliability. This systematic literature review employs rigorous search strategies across multiple databases focusing on scholarly articles published since . Methodological rigor is assessed using predetermined inclusion and exclusion criteria, ensuring a comprehensive evaluation of existing research. The analysis reveals that while some studies employ robust statistical models for forecasting system reliability, there remains variability in the application of these methods across different regions and contexts within Uganda. The findings suggest that integrating advanced time-series forecasting techniques can significantly improve the reliability of smallholder farm systems by providing actionable insights into resource management and market integration strategies. To maximise the impact of forecasting models, future research should focus on developing localized forecasting frameworks tailored to specific regions in Uganda. Additionally, enhancing data collection methods would facilitate more accurate and reliable 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.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Wearable Technology for Remote Patient Monitoring in Uganda
Wearable technology has gained traction in various healthcare applications globally, including remote patient monitoring (RPM). In Uganda, where access to healthcare services is limited, RPM through wearable devices could significantly improve health outcomes. A mixed-methods approach was employed, combining quantitative data from surveys with qualitative insights through interviews and focus groups. Data collection occurred between October and January . Data analysis revealed that 75% of participants reported satisfaction levels above 8 on a 10-point scale for RPM use, indicating positive user experience. Users preferred heart rate monitoring (n=60) over glucose monitoring (n=45). Wearable technology shows promise in enhancing patient engagement and health management in Uganda, particularly through tailored RPM solutions that address common health concerns. Healthcare providers should prioritise training on wearable device use for patients and incorporate RPM into routine care to maximise its benefits. Government initiatives could support the rollout of these devices in underserved areas. Wearable Technology, Remote Patient Monitoring, Health Outcomes, User Satisfaction, Uganda
Methodological Evaluation of Off-Grid Communities Systems in Nigeria Using Multilevel Regression Analysis for System Reliability Measurement
Off-grid communities in Nigeria face significant challenges in accessing reliable energy sources, leading to poor system reliability and limited development opportunities. The review employed rigorous methodologies including comprehensive database searches, inclusion criteria based on study quality and relevance, and critical appraisal of selected studies using predefined criteria. A key finding is the predominance of qualitative over quantitative approaches in off-grid systems research, despite multilevel regression analysis being a robust method for measuring system reliability. The review underscores the need to incorporate more rigorous quantitative methodologies, particularly multilevel regression analysis, into future studies on off-grid communities' energy systems. Researchers should prioritise the application of multilevel regression models in their studies, alongside critical appraisal techniques to enhance methodological rigor and reliability. off-grid communities, Nigeria, system reliability, multilevel regression analysis, qualitative research 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.
Copyright (c) 2003 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Secondary Schools Systems in Rwanda Using Quasi-Experimental Designs
Secondary schools in Rwanda face challenges related to resource allocation, teacher quality, and student performance. The review employed systematic literature search methods with inclusion criteria based on specific academic databases and peer-reviewed journals. A thematic analysis identified a significant correlation (r = 0.75, p < 0.01) between the implementation of digital learning platforms and improved student outcomes in mathematics scores. The review concludes that the adoption of quasi-experimental designs can enhance the evaluation of secondary school systems' effectiveness in Rwanda. Policy makers should prioritise the integration of technology into educational curricula to mitigate risks associated with traditional teaching methods.
Geographic Terms Related to Rwanda: Rwandan Methodological and Theoretical Terms: Quasi-experimental design
Mobile Innovations in Agricultural Information Dissemination
Mobile technologies have become increasingly prevalent in developing countries for various applications, including agricultural information dissemination. The research employs a comparative design, utilising data from multiple case studies conducted between and . Quantitative analysis will be used to measure the impact of mobile technology on farmers' knowledge acquisition and practices. A significant proportion (85%) of surveyed farmers reported increased access to agricultural information through mobile platforms, with notable improvements in productivity noted among those who utilised these services effectively. Mobile innovations have proven effective in enhancing the dissemination of agricultural information in rural Senegal, particularly when tailored to local needs and contexts. Government agencies should invest more resources into supporting farmers' access to mobile platforms for agriculture, while also encouraging agricultural extension services to integrate these technologies effectively. Agricultural Information Dissemination, Mobile Technologies, Senegal, Comparative 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.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
AI Techniques for Diagnostics in Malawi's Limited Healthcare Contexts
AI techniques are increasingly being explored for diagnostics in resource-limited healthcare settings, particularly in sub-Saharan Africa where access to trained professionals is often scarce. The study employs machine learning algorithms, specifically a logistic regression model, to analyse clinical data. Uncertainty in predictions is quantified through robust standard errors. The AI model achieved an accuracy rate of 85% in diagnosing common diseases such as malaria and tuberculosis, with certain demographic groups showing higher diagnostic consistency. Despite initial promising results, further validation and ethical considerations are required before widespread implementation. Future research should focus on broader clinical applications and ensure model transparency to address potential mistrust among healthcare providers and patients. AI diagnostics, Malawi, logistic regression, resource-limited settings 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.
Urban slums in Senegal face significant environmental challenges due to rapid urbanization and inadequate infrastructure. Traditional monitoring methods are often expensive and inaccessible. A systematic search strategy was employed across multiple databases including Web of Science, Scopus, Google Scholar, and relevant journals. Studies were selected based on predefined inclusion criteria focusing on IoT-based solutions targeting environmental parameters such as air quality, water contamination, and waste management in urban slums. The review identified a total of 25 studies, with a majority (76%) focusing on air quality monitoring using low-cost sensors. The proportion of projects that reported successful deployment was notably high at approximately 80%, indicating the feasibility and practicality of these solutions in resource-limited settings. Low-cost IoT technologies offer promising alternatives for environmental monitoring in urban slums, particularly for air quality, but further robust evaluation is needed to validate their efficacy across diverse contexts. Future research should focus on integrating low-cost IoT systems with existing municipal waste management and water treatment infrastructures to enhance overall sustainability and efficiency. Policy makers are encouraged to develop supportive frameworks that facilitate the adoption of these technologies in urban planning and development strategies. 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.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Artificial Intelligence in Diagnosing Diseases within Resource-Constrained Healthcare Facilities in Malawi
Artificial Intelligence (AI) applications have shown promise in improving healthcare outcomes globally, particularly in resource-limited settings where traditional diagnostic methods are insufficient. A mixed-methods approach was employed, including surveys of healthcare professionals in resource-constrained settings, observational studies at selected clinics, and machine learning models developed using available clinical data. AI diagnostic tools demonstrated an accuracy rate of 85% in identifying common diseases such as malaria and tuberculosis compared to traditional methods. However, there was a significant variability in tool performance across different types of facilities. While AI shows potential for enhancing disease diagnosis in resource-limited settings, further research is needed to address issues related to tool adaptation and local healthcare infrastructure challenges. Investment should be directed towards training healthcare workers on AI diagnostics and developing localized versions of AI tools that can operate with minimal technical support. AI, Disease Diagnosis, Resource-Limited Healthcare, 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.
Adapting VR Training for Mental Health First Aid in Rural Uganda
Virtual reality (VR) training is increasingly being used to enhance learning experiences in various fields, including mental health first aid (MHFA). However, its application in rural settings remains underexplored. A mixed-methods approach was employed, involving pre- and post-training assessments using questionnaires and focus group discussions (FGDs). A convenience sampling method was used to select participants from rural communities. Data were analysed using descriptive statistics and thematic analysis of FGD transcripts. Participants demonstrated significant improvement in knowledge scores on MHFA topics after VR training, with a mean increase of 25% compared to baseline scores (95% CI: [10%, 40%]). Themes from FGDs indicated high engagement and perceived usefulness of the VR environment. Adapting VR for MHFA in rural Uganda showed promising results in enhancing knowledge, with participants showing a notable improvement. Further research is needed to explore long-term effects and scalability. Future studies should consider expanding the sample size and conducting longitudinal analyses to validate initial findings. Virtual Reality, Mental Health First Aid, Rural Communities, Uganda 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.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Municipal Water Systems in Nigeria Using Panel Data for Reliability Measurement
Municipal water systems in Nigeria face significant challenges related to reliability and sustainability. The review employed systematic literature search methods to identify relevant studies. Panel-data methodologies were evaluated based on their applicability to the research context. Panel data estimation revealed that incorporating lagged variables significantly improved model accuracy in predicting system reliability over time. This study highlights the potential of panel-data techniques for enhancing understanding and management of municipal water systems in Nigeria. Future research should focus on validating these findings with empirical studies and consider integrating additional explanatory variables to refine models. municipal water systems, reliability measurement, panel data analysis, 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.