EANSO East African Nature and Science Organization Journals
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Investigating how Art Showcases Cultural Identity in South Western Uganda: A Case Study of Mbarara City
Full text linkIntroduction: Art has always been a powerful tool for expressing cultural identity across the world. From the cave paintings of ancient Europe to the contemporary visual and performance art of the 21st century, art serves as a universal language for expressing shared values, beliefs, and experiences. The study explored the role of art in expressing cultural identity in Mbarara City. Methods: This study adopted a cross-sectional research design with quantitative approaches. Data was collected from local artists, cultural institutions and community members. The total sample size was 70 respondents. A stratified random sampling method was used to select the sample, and questionnaires were used in data collection. Descriptive statistics were used to summarise the survey data. Results: It was established that visual arts such as paintings and carvings depict the traditions of the Ankole people and that music and dance are taught to younger generations as part of cultural education. Respondents also noted that folk dances are frequently used during cultural events and festivals. On the role of Art in preservation of culture, it was found out that Art brings people together and promotes unity within communities and that Artists in Mbarara city play a key role in preserving local traditions. Conclusions: The findings show a strong focus on passing cultural traditions (music and dance) to the younger generation, which indicates a positive outlook for the preservation of these aspects of the culture, even though some other traditions (like storytelling and crafts) may be in decline. While there is strong agreement on the importance of music, dance, and visual arts in cultural representation, some traditional elements are less influential in the modern cultural landscape of Mbarar
Preserving the Collective Memory of the Swahili Culture: An Ethnographic Study of Lamu Island
Full text linkThe paper is part of the Master of Arts (Fine Art project) titled “Depiction of Cultural Aspects of the Swahili of Lamu Island using a Combination of Natural Plant Pigments as Painting Media” from Kenyatta University. It argues that the collective memory of any culture plays a significant role in historic preservation, restoration, conservation, restitution, and documentation. The Swahili have unique cultural elements, including marriage, music, architecture, woodcrafts, textiles, boat construction, body decoration, dance, and poetry. Many scholars have attributed the decline of the Swahili culture to assimilation, extensive migration, and the removal of artefacts from their sites within Lamu Island. The paper establishes a collective memory of the Swahili culture of Lamu Island. The researchers conducted fieldwork by assessing Swahili culture from archived information at the Lamu Fort Museum. The researchers also utilised a non-probability purposive sampling technique by issuing questionnaires to key informants, including Lamu curators, residents, and Swahili experts. The data was analysed quantitatively into thematic areas of the Swahili culture and interpreted by reflexivity. The paper has concluded by appreciating the collective memory of Swahili culture and noting the need for documentation in various initiative
Hygiene and Safety Measures Practised by Roadside Meat Vendors of Namawojjolo and Lukaya Food Markets, Uganda
Full text linkHandling and preparation of roadside roasted meats may often be compromised, considering the general conditions of the makeshift structures and the common minimal education levels of vendors. The study’s objectives were to assess hygiene and safety practices applied in handling, preparing, and vending of roadside roasted meats. Conducted in October 2024 at Namawojjolo and Lukaya, two major food markets along central Uganda's busiest highways, the research used an observational checklist and questionnaires to collect data from 90 meat vendors selling roasted beef, chicken, or goat meat on compliance with best known practices. Descriptive results on hygienic and handling practices were generated, and scores above 70% were used as a hallmark for best practice. Only 6.7% instituted complete sanitation and hygienic practices, while 88.9% did not store leftover meat in refrigerators. Among them, 67.8% kept meat in clean containers, 5.6% stored utensils on clean shelves, and 6.7% had clean roasting areas. Most (93.3%) separate raw meat from ready-to-eat meat, and 37.8% had stalls without rodents. Hygienically, 75.6% wore aprons while working, among whom 85.3% were considered clean aprons, 46.7% had hair covered, 91.1% had short and clean fingernails, 93.3% washed hands with soap, 1.1% covered food while presenting to customers, and 11.1% wore jewellery while working. Training on food safety was undertaken by 63.3% and 78.9% served food in paper bags. Personal hygiene practices of most vendors were fairly good, but most lacked sanitation facilities and demonstrated relatively low knowledge of best and acceptable practices in meat handling. There is a need for more sensitisation and provision of sanitation facilities to vendors to improve both the quality and safety of roadside vendor product
Mapping Health Research Capacity Building Initiatives in Kenya: A Scoping Review
Full text linkHealth research capacity building (HRCB) is fundamental to the development of resilient health systems, the production of locally relevant evidence, and the advancement of evidence-based policy in low- and middle-income countries such as Kenya. Despite notable progress, Kenya’s HRCB landscape remains fragmented, donor-dependent, and heavily concentrated in urban academic institutions, with limited national coordination or systematic evaluation. This scoping review aimed to comprehensively map HRCB initiatives implemented in Kenya between 2010 and 2025, identifying key thematic areas, geographical coverage, and institutional actors. The review followed the Arksey and O’Malley methodological framework, augmented by the Joanna Briggs Institute (JBI) guidelines and the PRISMA 2020 checklist. Eligibility criteria were defined using the Population–Concept–Context (PCC) framework, focusing on individuals, institutions, and programs engaged in research training, mentorship, infrastructure development, policy engagement, and collaboration within the Kenyan context. A total of 110 records were identified through systematic searches of peer-reviewed databases (PubMed, Google Scholar, ResearchGate) and grey literature sources, including reports from government agencies, academic institutions, and development partners. After screening and full-text review, 31 studies were included in the final synthesis. Data were charted using thematic matrices and analysed narratively. Five key themes emerged: training and mentorship, institutional strengthening, research networks and collaborations, research-to-policy linkages, and equity considerations, including regional and gender disparities. While programs such as CARTA, Afya Bora, and KEMRI-led initiatives demonstrated impact, challenges included inadequate rural reach, persistent gender imbalances, limited sustainability, and weak national ownership. Findings reveal a lack of standardised monitoring indicators and minimal integration of HRCB into broader health and education systems. This review underscores the urgent need for a coordinated national HRCB framework that promotes inclusivity, local leadership, and long-term sustainability. Such efforts are essential to optimise Kenya’s research ecosystem, bridge capacity gaps, and align research development with national health priorities and global goals
Lymphatic Filariasis in Benue State: Progress towards Elimination Based on Mass Drug Administration of Ivermectine and Albendazole
Full text linkThe Federal Government of Nigeria, through the Ministry of Health, commenced mass drug administration of ivermectine (IVM) and albendazole (ALB) in all the 36 States of the Federation in 2011. The objective of this research was to assess the progress towards the elimination of LF in some endemic Local Government Areas of Benue State. While MDA is continuing in six Local Government Areas, in the year 2024, we assessed the impact of MDA on LF prevalence in six Local Government Areas that had achieved five effective annual rounds of MDA. In 2023, a baseline mapping exercise was conducted in the 20 accessible LGAs in Benue State. The results revealed that fifteen out of the twenty LGAs were endemic for LF, with prevalence ranging from 1.0%-7.0%. The number of persons treated with ivermectine and albendazole in the fifteen LGAs was documented during annual MDA, and population-based cluster surveys were conducted at least once in each LGA during the five years of treatment, to verify the reported geographic and programme MDA coverage. This is the number treated divided by the total population eligible to receive treatment (usually <5years). The survey results confirmed that in six LGAs (Ukum, Logo, Konshisha, Katsina-Ala, Gboko, and Otukpa), the coverage exceeded the target of 65% while the other nine LGAs did not reach the recommended coverage. A pre-transmission assessment survey (pre-TAS) was conducted in one sentinel site and at least one spot check site in Ukum, Logo, Konshisha, Katsina-Ala, Gboko, and Otukpa in 2024 and was found to have LF antigenemia (LF Ag) < 2% (range 0.0% to 1.99%). In 2024, transmission assessment surveys (TAS) were conducted in the six LGAs that had previously passed the Pre-transmission assessment survey. The results showed that the six Evaluation units had achieved the LF Ag threshold required to stop MDA. Benue State had made significant progress towards LF elimination with six LGAs qualifying to stop treatment. However, nine other area councils still require a further two years of mass drug administration with effective MDA coverage before these LGAs qualify for impact assessment
Analysing Electronic Gadget Usage and Makerere University Engineering Student Performance
Full text linkPurpose: This study aims to investigate the relationship between electronic gadget usage and academic performance among Makerere University engineering students in Uganda. Methods: A structured five-item Likert-scale 10-item questionnaire was used to collect data from 618 students at Makerere University School of Engineering. Smart PLS-SEM was employed to assess the measurement and structural models, including reliability, validity, and path analysis. Results: The findings indicate that electronic gadget usage has a statistically significant positive effect on engineering student performance (β = 0.673, p < 0.001), explaining 45.3% of the variance in academic performance at a 95% confidence interval. Convergent and discriminant validity were confirmed, and the model met acceptable thresholds for composite reliability. Conclusion: Electronic gadgets are essential tools that can enhance academic performance if used strategically. Their widespread use among engineering students offers both opportunities and challenges for academic success. Institutions should promote responsible use through structured digital literacy initiatives. This study contributes to the body of knowledge on the electronic gadget usage and student performance, offering practical recommendations for educators and policymakers to enhance academic outcome
Adoption of the Fourth Industrial Revolution in the Library and Information Services Sector: Implications and Prospects for Uganda
Full text linkThe Fourth Industrial Revolution (4IR) is characterised by advancements in the business models, transforming library and information services. To systematically explore the impact of the 4IR on library and information services, a comprehensive literature review was conducted employing predictive analysis. This methodology involved a multi-step process beginning with the identification of relevant literature through My LOFT, Research4Life, and Google Scholar. Keywords related to the Fourth Industrial Revolution, library services, big data, artificial intelligence, and digital transformation were used to filter sources. This approach enabled the identification of key patterns, potential impacts, and future directions in the integration of 4IR technologies within libraries. Through this rigorous analysis, the review aimed to provide a detailed and predictive understanding of how libraries can adapt to and leverage these emerging technologies to enhance their service delivery and operational efficiency. Findings suggest that the 4IR technological disruption is distinct in its speed, scope, and impact on systems. Libraries from low-resource settings face unique challenges in adopting these technologies due to the digital divide perpetrated by economic constraints and infrastructural limitations. However, they have the opportunity to narrow the divide and enhance service delivery through innovative use of 4IR technologies. Despite the magnificent possibilities, there are some sceptics raising privacy concerns, job displacement, and the need for significant investment in human capital and technology. However, this study recommends that libraries must adapt by embracing flexible work models, leveraging mobile apps, employing AI and robotics where affordable, and investing in high-speed internet. The Fourth Industrial Revolution compels libraries to reimagine their roles, ensuring they remain vital in the trending digital dispensation. As such, library professionals in Uganda must stay abreast of technological trends, continuously update their skills, and foster an inclusive approach to technology adoption to navigate and thrive in this new era
Online Fraud and Cryptocurrency in Tanzania: Legal Issues Surrounding Cyber Scams
Full text linkOver the past decade, Tanzania has experienced significant digital transformation driven by increased internet access, mobile technology, and digital financial services. This growth has created fertile ground for innovations like blockchain and cryptocurrencies. Popular digital currencies such as Bitcoin and Ethereum are now used locally for investment, remittances, and transactions, promoting financial inclusion and modernisation. However, these benefits are accompanied by rising risks, particularly online fraud. Criminal activities such as Ponzi schemes, phishing scams, fraudulent Initial Coin Offerings (ICOs), and ransomware attacks have increased, exploiting limited public awareness and weak institutional oversight. Tanzanian law enforcement and regulatory bodies often lack the technical expertise and jurisdictional reach to respond effectively, leaving victims vulnerable and eroding trust in digital finance. This paper examines the legal and regulatory challenges posed by cryptocurrency-related fraud in Tanzania. It analyses key domestic laws, including the Cybercrimes Act 2015, Electronic Transactions Act 2015, and Anti-Money Laundering Act 2006. While these laws address cybercrime broadly, they lack specific provisions related to cryptocurrencies, such as clear definitions, asset tracing mechanisms, and cross-border cooperation tools. Drawing on Tanzanian case studies, legal literature, and international examples from countries like the UK and Japan, the study identifies legal gaps and highlights successful regulatory strategies abroad, including crypto exchange licensing, Know Your Customer (KYC) rules, and public education campaigns. The paper concludes by recommending comprehensive reforms to strengthen Tanzania’s legal framework, enhance regulatory oversight, promote public awareness, and develop cross-border enforcement strategies to protect the integrity of the country’s digital financial ecosystem
Rule-Based Cyber Incident Detection and Response for Higher Education in Low-Resource Contexts: A Case Study of Universities in Eastern Uganda
Full text linkHigher education institutions (HEIs) in developing countries are increasingly dependent on digital systems for teaching, administration, and research. However, resource-constrained universities remain highly vulnerable to cyber threats such as phishing, ransomware, and web application attacks. To address these challenges, this study adopted the Design Science Methodology (DSM) to design, implement, and evaluate a lightweight, rule-based cyber incident analysis and response algorithm specifically tailored for universities in Eastern Uganda. The approach combined contextual data collection from six universities with simulation-based evaluations to ensure both practical relevance and technical validity. The algorithm, developed on a Laravel-PHP-MySQL stack, integrates rule-based detection, correlation of multi-stage attacks, and an administrative dashboard for IT staff. Simulation results showed strong performance with a recall of 92.8%, precision of 91.3%, and an F1-score of 92.1%. Response latency remained below 100 milliseconds, and the system maintained stability up to 450 requests per second. Benchmarking against Snort demonstrated higher precision and lower resource consumption, though Snort achieved slightly higher recall. This research contributes a context-appropriate and cost-effective cybersecurity framework for HEIs in low-resource contexts. It extends the Defense-in-Depth (DiD) and Resource-Based View (RBV) theories to constrained environments and provides practical recommendations for implementing modular, rule-based detection systems to enhance cybersecurity resilience in African universitie
Wind Speed Prediction Using BiLSTM Deep Learning Model and Comparable Batch Sizes of Training Data: A Case of Singida Wind Farm Site, Tanzania
Full text linkSingida region, located in central Tanzania, has long been identified as a potential location for installing wind farms to generate electric power due to the steady annual wind speed. Apart from the huge potential of contributing to the national grid, wind power also helps to address carbon emissions and environmental problems associated with generating electric power using fossil fuels. Failure to accurately predict wind speed can lead to poor harvest of wind power and low contribution to the national grid, and in the end, affect consumers. Bidirectional Long Short-Term Memory (BiLSTM) is one of the Deep Learning models which can be used to predict time series parameters such as wind speed. In a BiLSTM model, a batch size is an important hyperparameter as it is used to set the number of training data samples to be processed together before the weights of a Deep Learning model are updated. Despite its importance, there is still a research gap on the impact of batch size on the prediction performance of BiLSTM models, especially in the context of predicting wind speed at the Singida Wind Farm Site, located in the Singida region, Tanzania. The goal of the study was to fill this gap by developing a BiLSTM model and comparing the performance of three batch sizes (16, 32 and 64) in predicting wind speed at the Singida Wind Farm Site. The 14-year Singida Wind Farm Site daily wind speed dataset was first pre-processed by scaling (normalizing) it using Standard scaler and then split into training, validation and test sets before used to train and test the developed BiLSTM model which used previous 5 days wind speed values as input to predict the output (next day (6th day) wind speed). The trained BiLSTM model with the optimal (best performing) batch size was then saved in .h5 format and integrated with a Gradio-based web App to provide a user interface for officials in the Singida region to predict daily wind speed at the Singida Wind Farm Site. The evaluation findings revealed that batch size has an impact on the prediction performance of the developed BiLSTM model, showing that the lower the batch size, the better the prediction performance of the BiLSTM model. The findings also revealed that, 16 is the optimal (best performing) batch size with Mean Absolute Error (MAE) score of 0.58, Root Mean Squared Error (RMSE) score of 0.76 and R2 score of 0.79, followed with a batch size of 32 (MAE score of 0.62, RMSE score of 0.79 and R2 score of 0.75) and followed by a batch size of 64 (MAE score of 0.66, RMSE score of 0.81 and R2 score of 0.72). This study recommends that Artificial Intelligence (AI) software developers and researchers use a batch size of 16 in BiLSTM models when forecasting wind speed at the Singida Wind Farm Site, as well as in environments and climates which resemble that of the Singida Wind Farm Site in Tanzania