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Ritual Healing Innovations in African Pentecostalism
No full textRitual healing and deliverance creativities and innovations in African Pentecostal (AP) churches have become a phenomenon if not a hallmark of African Pentecostalism in sub-Saharan Africa, yet there is little contextual knowledge known that informs such innovations. Using fire healing ritual in the Pentecostal Church Universal (PCU) in Nairobi Kenya as a case study, the paper attempts to offer explanatory frameworks for ritual deliverance innovations and creativities. This chapter shows that healing rituals have cultural and biblical premises. Church pastors often blend the African traditional cultural symbolism with the bible narratives to innovate ritual materials—which make meaning to an African Christian. The usage of the Bible in grounding this ritual is part of the continued processes of Bible/Christianity Africanizing, especially in the socio-economic and technological spaces which call for creative approaches in creating meaning to life and religion
Flood inundation and damage assessment of the degraded Semliki River plains using SAR data, Google Earth Engine, and GIS techniques
No full text8379-8390 p. : maps.The Semliki River valley in Ntoroko district has experienced devastating annual floods since 2019. Recurrent floods in Ntoroko District have displaced thousands and devastated pasturelands, disrupting livelihoods. Therefore, rapid assessment of flooded areas is crucial for developing effective mitigation strategies, disaster preparedness plans, and proactive policies to enhance resilience and mitigate the impact of future flood events. This study introduced a combined approach using Synthetic Aperture Radar (SAR) imagery and a digital elevation model (DEM) to map flood extent, depth, and building exposure in the Semliki Valley. Using Sentinel-1 SAR images taken both before and during the flood, combined with the ALOS PALSAR DEM, inundated areas and flood depths were determined, based on thresholding the SAR backscatter of the VH polarisation images. The flood extent maps were generated using Google Earth Engine and GIS techniques to create depth maps by subtracting the surface elevation from the height/surface of the flood waters. Building exposure and impact analysis for two flood events was ascertained through spatial join and overlay. The results showed that the 2023 flood event inundated approximately 1,968 hectares, including 1,553 hectares of pastureland and 74 buildings, while the 2024 event covered 1,139 hectares, equally inundating 1,050 hectares of pastureland and 54 buildings. Further analysis revealed that despite the smaller extent, the 2024 flood event caused a severe impact on the buildings compared to the 2023 flood disaster
Nutraceutical benefits of seaweeds and their phytocompounds: a functional approach to disease prevention and management
No full textSeaweeds (SWD), macroalgae or sea vegetables are a diverse group of over 9000 macroscopic and multicellular marine algae taxonomically classified (based on morphology and pigmentation) as green, brown and red algae. With microalgae, SWD represents one of the most researched oceanic resources turned to as treasure troves of bioactive compounds with ethnomedicinal, pharmaceutical, cosmeceutical and dietetic end-uses for millennia. This review compiles the nutraceutical uses of SWD and their bioactive compounds in nutrition and traditional management of diseases, offering future perspectives on using this group of organisms to improve human life. The review reveals that the nutraceutical application of SWD as nutrient-dense marine foods for treating diseases may be correlated with their inherent biosynthesis and possession of minerals, vitamins, dietary fibres and bioactive compounds. Compounds of algal origin have been validated and found to elicit antimicrobial, anti-inflammatory, free radical scavenging (antioxidant), antiproliferative and antidiabetic activities, among others. © 2025 Society of Chemical Industry
A multi-hydrological model ensemble prediction uncertainty estimation (e-PRUNE) framework
No full textAmong the several hydrological model uncertainty estimation methods, the generalized uncertainty estimation (GLUE) method is popular due to its simplicity. The application of GLUE tends to be limited to the cases when hydrological models are applied individually. Notably, little attention is given to model differences when applying GLUE. This study introduced a framework for multi-hydrological model ensemble prediction uncertainty estimation (e-PRUNE). For demonstration, the framework was applied to real hydrometeorological data while considering three sub-sources of calibration-related uncertainty including the influence of the choice of a sampling scheme, hydrological model (HM), and objective function (OF). Ten SCs, six HMs, and eight OFs were considered. Influences from SCs, OFs, and HMs were combined and assumed to substantially comprise the overall predictive uncertainty (OPU). The sub-uncertainty bound based on HM's choice was larger than that of either SC's or OF's selection. Contributions of sub-uncertainties from HM, SC, and OF to the OPU were additive. Thus, the effect of removing one source of uncertainty (for instance, OF) could easily be realized from the width of OPU's bounds. This study showed the importance of the e-PRUNE framework for insight into the contributions of various sub-uncertainty sources to the OPU
Advancing gender-responsive AI in higher education: a participatory rural appraisal of traditional and modern food processing innovations in Uganda
No full textThis study explores how gender-responsive artificial intelligence (AI) can
transform higher education participatory rural appraisal (PRA) research to
advance Sustainable Development Goal (SDG) 2 - Zero Hunger - by
harmonising traditional and modern food processing practices in Uganda.
Amidst rapid technological progress, AI’s potential remains disproportionately
urban-centric, sidelining rural women who dominate Uganda’s traditional food
systems yet face systemic barriers to accessing modern innovations. Through a
mixed-methods approach including a systematic review of global AI
applications in agriculture, analysis of national and international policy
frameworks, and community-driven PRA case studies, this research uncovers
critical gaps in gender-equitable AI adaptation within Higher Education
Institutions (HEIs). Findings reveal that socio-economic disparities, limited
digital literacy, and infrastructural inequities exclude women from AI-driven
solutions, thus undermining Uganda’s progress toward sustainable agriculture.
However, HEIs emerge as pivotal agents of change. This is because, by
embedding gender-responsive AI into participatory research curricula,
universities can co-design inclusive technologies that amplify women’s
expertise in traditional food preservation while integrating modern efficiencies.
This study proposes a tripartite strategy: (1) Gender-sensitive AI training
programs tailored to rural contexts, (2) Low-cost, culturally relevant AI tools
for decentralised food processing, and (3) Cross-sector partnerships linking
academia, policymakers, and grassroots innovators to align AI initiatives with
SDG 2 (Zero Hunger) and SDG 5 (Gender Equality) targets. This work
challenges the global AI paradigm by centring marginalised voices and
demonstrating how participatory, gender-responsive education frameworks can
catalyse equitable technological adoption. Its actionable insights offer a
blueprint for HEIs worldwide to harness AI as a tool for social justice, bridging
the divide between tradition and innovation to build resilient, inclusive food
systems
Design and optimization of a hybrid graphene–gold–silver terahertz metasurface biosensor for high-sensitivity sperm detection with machine learning for behavior prediction
No full textThis study introduces a plasmonic-based sensor for sperm detection, integrating gold, graphene, and black phosphorus within a tailored multilayer structure. The sensor design consists of a silver-coated circular ring resonator (radius: 2–2.5 µm), a black phosphorus-coated square ring (7–8 µm), and four gold-coated circular resonators (each with a 2 µm radius) placed on a graphene-coated square platform. Electromagnetic simulations performed using COMSOL Multiphysics indicate optimal sensing performance within the 0.1–0.6 THz frequency range. The sensor demonstrates remarkable sensitivity of 5000 GHz per refractive index unit (RIU−1), a figure of merit of 90.909 RIU−1, and a detection limit of 0.02 RIU. It is capable of detecting sperm concentrations in a range of 17–197 million/mL, corresponding to refractive index variations from 1.33 to 1.3461. Furthermore, performance optimization through XGBoost machine learning achieved perfect prediction accuracy (R2 = 1.00) across all test cases. This high-efficiency sensor marks a significant step forward in sperm detection technologies, with promising applications in male fertility assessment and reproductive medicin
Online information seeking behaviour of postgraduate engineering students at Kyambogo University in Uganda
No full textThis study investigated the online information seeking behaviour of postgraduate engineering students at Kyambogo University in Uganda to determine the extent to which they use electronic (e-) library resources. A convergent mixed methods research approach was used. Quantitative data were collected from 58 out of 80 postgraduate engineering students using an online self-administered questionnaire while qualitative data were collected from interviews with 11 library staff and a focus group discussion with five members of library management. The findings revealed that the most used (M = 4.71) online information sources were Google Scholar, followed by corporate websites (M = 4.0), internet search engines (M = 3.98) and e-library resources (M = 3.62). The online information seeking behaviour of postgraduate engineering students was mainly influenced by their academic needs as students and their profession as engineers. This study, therefore, has implications for collection development in academic libraries to provide access to industry-relevant collections alongside the e-library resources to bridge the information gap between professional engineering practice and academic research. The study recommends that the library promote e-library resources and provide information literacy training to postgraduate engineering students to enhance the use of e-library resources
Using eDNA to assess freshwater bacterial diversity along a forest–non-forest gradient in the afrotropics
No full text13 p. : col.Healthy ecosystems are critical for maintaining ecosystem services and water security; yet many freshwater ecosystems have been subject to environmental degradation. Impacts are often greatest in water-scarce and developing regions, including across much of Sub-Saharan Africa, where many people lack access to basic drinking water. However, environmental monitoring programmes to track ecosystem health are generally lacking across this region due to limited resources and funding. Recent advances in environmental DNA (eDNA) methods offer an increasingly cost-effective and information-rich
solution. Here, we explore the potential of eDNA as a tool for ecological monitoring of freshwater ecosystems in Uganda, East Africa. We sampled
eDNA to quantify the bacterial diversity of rivers, streams, and swamps across a gradient of human disturbance in and around Kibale National Park, using off-the-shelf sampling methods that require minimal pre-existing infrastructure. We found distinct bacterial communities between intact and degraded habitats, but the bacterial community in rivers converged when flowing through intact forest. We identified several taxa with differential abundances that might serve as potential bioindicators of degraded ecosystems, and showed that a machine learning tool trained on eDNA can accurately differentiate between intact and degraded habitats. Our proof-of- concept study demonstrates the potential of eDNA as a practical and cost-effective biomonitoring tool for freshwater ecosystems in resource-limited regions, including Sub-Saharan Africa. We also highlight the potential benefits of protected forest in modulating bacterial composition in freshwater ecosystems
Prevalence and predictors of stress, anxiety, and depression among healthcare workers during the COVID–19 pandemic at a mental referral hospital in Kampala, Uganda
No full text13 p.Background
Healthcare workers faced immense psychological strain during the Corona Virus Disease 2019 (COVID – 19) pandemic due to increased workloads, infection risk, and limited protective equipment. This study assessed the prevalence of stress, anxiety, and depression among them and identified factors contributing to these mental health challenges during the outbreak.
Methods
A cross-sectional study was done at Butabika National Mental Referral Hospital between February and March of 2023. We gave self-administered, paper-based surveys to all willing Healthcare workers (HCWs) working day shifts during this period. Sections on socio-demographics characteristics, perceived stress (PSS–10), anxiety (GAD–7), and depression (PHQ–9) were all included in the questionnaire. The SPSS version 26.0 program was used to analyse the data. To determine the variables that predict psychological distress, we employed Modified Poisson regression. When the p-value was less than 0.05, statistical significance was declared.
Results
Among the 209 enrolled participants, 198 (94.7%) eligible subjects were included in the analysis. Majority of the participants (58.6%) were female, (73.7%) were married, (49.5%) were nurses and 58.6% had more working hours. The prevalence of symptoms of stress, anxiety and depression in healthcare workers during the COVID-19 pandemic was 91.9%, 27.3% and 57.6%, respectively. In relation to perceived stress, being female (aRR = 1.219; 95% CI: 1.010–2.922), being younger in age (aRR = 1.672; 95% CI: 1.050–5.733) and having worked for 11–15 years (aRR = 1.274; 95% CI: 1.020–2.503) were significantly associated with higher risk of perceived stress. Participants with a bachelor’s degree had higher risk of generalized anxiety disorder symptoms (aRR = 2.577; 95% CI: 1.123–4.980), whereas being a nurse (aRR = 0.082; 95% CI: 0.040–0.900) showed lower risk of anxiety. Being married (aRR = 1.322; 95% CI: 1.042–2.260) and being younger in age (aRR = 1.037; 95% CI: 1.005–2.834) were significantly associated with higher risk of depressive symptoms, whereas being a technician (aRR = 0.683; 95% CI: 0.480–0.972) and having no change in work volume (aRR = 0.711; 95% CI: 0.532–0.987) were associated to lower risk of depression symptoms.
Conclusion
Psychological distress was high among mental health workers during COVID-19. Supportive measures such as adequate PPE, manageable workloads, mental health check-ins, and challenging societal stigma are needed to improve well-being, within study limitations
Modelling and optimization of the production process of ethylene from Bio-ethanol using Aspen plus
No full text149-159 p. ;Ethylene is the most important chemical produced by the petrochemical industry; it accounts for over 70% of the products made in the sector. Its global market value exceeds 200 billion US dollars and is steadily growing. Despite this importance, the current means of producing ethylene are not environmentally friendly: The production of ethylene by hydro-cracking contributes the largest amount of greenhouse gas emissions from the petrochemical industry. For this reason, several research efforts have been directed to finding alternatives to hydrocarbons as a sustainable source of ethylene. In the recent past, much attention has been given to the dehydration of ethanol as it is the most promising alternative. This route is, however, limited by its high energy requirement in order to match the conventional method. To lower this energy requirement, researchers have focused on developing more effective catalysts. Little to no work, however, has been done to determine the contribution of process configuration to this energy requirement. This study investigates the energy usage of different process models for the catalytic dehydration of bioethanol to ethylene using Aspen Plus V11. Three models were developed, optimized, and compared. The first model was the base case on which other models were compared, the second model included a recycle stream, and the last model involved the compression of liquid-phase ethanol rather than gaseous-phase ethylene. Due to lack of generalized kinetic data, the RGibbs reactor was used for the simulation. Sensitivity analyses were used to optimize the models. The base case operated at 162°C and 1 atm, the model with a recycle stream at 125°C and 1 atm, and the liquid-phase ethanol compression model at 330°C and 55 atm. Results show that the model with the recycle stream achieved the lowest energy consumption (1910 kW) but also yielded the least amount of ethylene (5230 kg/hr). The base case gave the highest ethylene yield (5649 kg/hr). While innovative, the liquid compression model consumed the highest amount of energy (2456.25 kW) due to high operating conditions. The findings in this work shed light on the importance of process modeling and optimization in enhancing the sustainability and economic viability of bioethanol to ethylene production