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ItemDevelopment of a bioactive gauze dressing functionalized with herb-loaded nanoparticles to facilitate wound healing(Makerere University, 2025)Wound healing is crucial in healthcare due to its significant physiological and economic impacts on patients. Among wound dressings, gauze is the most widely used; however, it is associated with a high risk of bacterial wound infections that delay healing. Hence, enhancing its effectiveness remains critical. In Uganda, herbal medicines are still utilised in the treatment of wounds and other illnesses; however, they require high dosages and prolonged treatment, leading to poor patient compliance. Nanoencapsulation offers a promising solution by improving the drug's therapeutic effect through sustained release while minimising toxicity and dosage. The main objective of this study was to develop and evaluate a gauze dressing functionalized with herb-loaded chitosan nanoparticles for enhanced wound healing. This study explored the extraction of selected medicinal herbs (Bidens pilosa L., Ageratum conyzoides L., and Hoslundia opposita Vahl) using different methods and solvents with varying polarity. The extraction yield, in vitro antibacterial (on Staphylococcus aureus/Methicillin-Resistant Staphylococcus Aureus (MRSA), Enterococcus faecalis, Escherichia coli, Klebsiella Pneumoniae, and Pseudomonas aeruginosa), antibiofilm, and antioxidant activities, as well as phytochemicals present, were determined. The most efficacious extract was subjected to GC-MS and LC-MS analysis. It was nanoencapsulated in chitosan nanoparticles via the ionic gelation method, and optimisation was performed using Response Surface Methodology (RSM). The herb-loaded nanoparticles were evaluated for in vitro antibacterial activity and incorporated into the gauze dressing. Their in vitro antibacterial activity, in vivo wound healing in Wistar rats, water absorption, and retention capacity, as well as in vivo skin irritation in rabbits, were tested. High extraction yields were obtained for samples extracted with highly polar solvents. The methanol (100%) extract of H. opposita extracted by maceration, displayed better bioactivity (antibacterial, antibiofilm and antioxidant). The biological activity of the plants was attributed to the presence of various phytochemicals. The herb nanoparticles obtained were spherical with size 212 nm, zeta potential 40 mV, Polydispersity Index (PDI) 0.22, encapsulation efficiency 79.1% and loading capacity 9.82 %. Their in vitro drug-release profile showed sustained release of 50% over 24 hours, compared with 100% for the free extract. They exhibited enhanced antibacterial activity with minimum inhibitory concentrations (1.875 to 3.275 mg/mL) against S. aureus, MRSA, E. faecalis, E. coli, K. pneumoniae, and P. aeruginosa. Gauze incorporated with nanoparticles exhibited no antibacterial activity but significantly accelerated wound healing, achieving 93% wound closure by day 18 compared with 41% in untreated gauze. The dressing also exhibited improved water-holding capacity; no skin irritation was observed in rabbits, and its water absorption capacity remained unaltered. Extract fractionation identified rutin and 4111-acetylvitexin-211-O-rhamnoside as the possible major bioactive compounds in the methanol extract of H. opposita. In conclusion, integrating the H. opposita extract-loaded chitosan nanoparticles into gauze significantly enhanced wound healing, offering a promising advancement in wound care technology.
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ItemInvestigation into the mechanism and performance characteristics of ECOroads enzyme-modified lateric soils to predict pavement performance(Makerere University, 2025)Recently, suitable virgin materials possessing the desired engineering properties are depleting and therefore increasing the cost of investments. Soil stabilization techniques using either traditional or non-traditional methods have been adopted to combat this challenge. However, traditional stabilization techniques have some shortcomings and environmental concerns. Innovations in pavement materials and engineering have emerged using non-traditional stabilization techniques to enhance soil engineering properties, address environmental concerns, and promote sustainable development. This study examines the influence of ECOroads enzyme, one of the non-traditional additives on the fundamental mechanism of enzyme-based lateritic soil stabilization, engineering properties, and response modeling of variables. Response Surface Methodology (RSM) which combines both experimental designs and statistical techniques for empirical model building and optimization was adopted. A novel method using STATEASE 360 software and a popular central composite design in RSM was used to build the model to optimize processes and predict performance. Soil samples were prepared using three dosages of ECOroads enzyme R2, R6 & R7 in replicates for tests at each curing time of 0, 7, 14 and 28 days as per the design of the experiment. The test results showed an increase in CBR values from 34% to 101%; UCS from 289kPa to 1349kPa after 28 days of curing. A reduction in Liquid Limits and an increase in Plastic Limits were registered, resulting in a reduction in Plasticity Indices of the soil materials. The developed RSM predictive models of independent variables (enzyme dosage, curing time & compaction levels) and two responses (UCS & CBR) were significant (p ≤ 0.05) and the diagnostic has a strong relationship between the measured and predicted values. The optimal response values of CBR of 76% and UCS of 994kPa were achieved at an enzyme dosage of 0.047mL/kg of soils, cured for 16days when samples were compacted at 98% of the MDD at optimal moisture content with desirability of 0.727. Microstructural tests were conducted on ECOroads enzyme-modified soil samples and results revealed an increase and reduction in some soil minerals highlighting variations in atomic counts and images compared to the control specimens. These results of the microstructural tests for the mechanism might explain the increase in strength, stability, and stiffness registered during macro-structural tests. From the results of this study, ECOroads enzyme can be used to stabilize marginal soil materials to meet requirements of new pavement layers and addresses environmental concerns.
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ItemDevelopment of a safety monitoring device for the quarrying sector in Uganda(Makerere University, 2025)Mining has one of the highest average fatality rates among Uganda’s major industries since the days of the shovel. Although miners’ health and safety have improved throughout the years, mining is still one of the most dangerous occupations. This has triggered the need for the development of a safety monitoring device for the quarrying sector in Uganda. An assessment conducted identified and ranked risks in quarries based on their severity (Rs) and degree of control (Rc), which were used to acquire a total risk score (R), with R=1 being the lowest and R=5 the highest. The identified risks with the highest R were considered for the monitoring. These included: particulate matter (PM2.5), excessive heat (temperature), and air quality (CO, CO2, H2, CH4 levels). Using Ashby’s systematic design process, a safety monitoring device was developed and tested for performance and statistical evaluation. Performance evaluation showed PM2.5 as the only parameter that exceeded its set threshold (50 μg/m3) specifically during peak hours (11:30AM to 14:30PM). The statistical evaluation conducted using Pearson Correlation Coefficient (r) with P-Value approach revealed PM2.5 as having the most positive correlation and statistical significance with CO (r = 0.937, P = 0.00019) and CO2 (r = 0.903, P = 0.00084). Humidity showed the most negative correlation, specifically with PM2.5 (r = -0.875, CH₄ r = -0.869), and H2 showed the weakest, statistically non-significant correlations with other parameters. Mitigation measures like dust suppression and the use of masks to prevent PM2.5 infections were suggested. Key words: Quarrying, Mining, Risks and Hazards, Safety monitoring device, Temperature, Particulate Matter, and Air Quality.
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ItemAssessing the risk of environmental contamination by pathogens released from household “Septic Tanks” in Kampala(Makerere University, 2025-12-19)In Sub-Saharan African (SSA) cities, “septic tanks” that are poorly designed and maintained are widespread. This poses significant risks of pathogen contamination to the surrounding environment. When in the environment, these pathogens put people at risk of contracting sanitation related diseases. The factors influencing pathogen concentrations in “septic tank” effluent, as well as the capacity of the surrounding environment to safely attenuate discharged effluent, remain poorly understood. This study investigated the potential for environmental contamination by pathogens released from household “septic tanks” in Kampala. The study was conducted in Bukoto I and Mulago III parishes, where the existing “septic tanks” were classified and evaluated against 13 key design criteria. Indicator pathogen presence in “septic tank” effluent was assessed by enumerating Escherichia coli (E. coli) in samples collected from 54 containments. A bivariate risk classification framework was then developed to categorize the risk of pathogen contamination to the environment by “septic tanks”. Four main typologies were identified: (1) fully lined tanks with an effluent pipe to a soak away pit; (2) fully lined tanks with an effluent pipe to an open drain; (3) fully lined tanks without an effluent pipe; and (4) lined tanks open at the bottom. The study revealed that none of the “septic tanks” met all the 13 key design criteria, and about 74% satisfied at least seven of the assessed design criteria. This raises concerns over the broad classification of such systems as septic tanks. Mean E. coli concentration in “septic tank” effluent was 6.52 ±1.83 log10 CFU/100mL. There were significant correlations between E. coli in “septic tank” effluent with the “septic tank” volume per capita (ρ = -0.386, p = 0.005), total volatile solids (TVS) (ρ = 0.463, p = 0.000), years of operation (ρ = -0.369, p = 0.023) and detention time (ρ = -0.397, p = 0.0036). “Septic tanks” receiving combined black and grey water exhibited significantly higher effluent E. coli concentrations (1.13 log 10 difference, p = 0.025) compared to those treating black water alone. The sampled “septic tanks” were later categorized into four risk zones based on their potential to contaminate their surrounding environment with pathogens. These were critical (30.2%), latent (28.3%), localized (26.4%), and minimal risk (15.1%). The study shows that many household “septic tanks” do not meet recommended design, sizing, or siting standards. This increases the likelihood of poorly treated effluent entering the environment, especially in areas with shallow groundwater, poor soils, or close to streams. Improving septic tank design and construction practices is therefore necessary to enhance treatment effectiveness. High-risk systems should be prioritized for upgrades, such as secondary treatment units, retrofitting or extension of centralized sewer networks where applicable.
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ItemReliability enhancement of Non-destructive testing methods for IN-SITU concrete compressive strength using Convolutional Neural Networks with destructive testing data.(Makerere University, 2025-12-17)Reliable evaluation of in-situ compressive strength of concrete is vital for ensuring safety and longevity of ageing infrastructure particularly bridges that recently are subjected to increasing traffic loads and deterioration due to climatic conditions. While traditional core extraction methods have reported high accuracy, these are intrusive, time-consuming and costly, thus limiting their practical application in structural assessment of concrete. In response to the need for less intrusive, quicker and cost-effective alternatives, this study investigated the use of the two most common Non-Destructive Testing techniques (NDT)—Schmidt Rebound Hammer (REB) and Ultrasonic Pulse Velocity (UPV)—coupled with Convolutional Neural Networks (CNN) to improve predictive accuracy. The study developed and trained three CNN model configurations using MATLAB® R2025a with data collected from five existing concrete bridge structures. The architecture consisted of a Conv1D with an input layer for two-channel sequential data, a convolutional layer with five filters, BatchNorm, ReLU activations, two fully connected layers with dropout regularization and a final dense output neuron, optimized for regression tasks. Each test method (REB, UPV and Core) generated 30 measurements which were subsequently cleaned and organized into triplicate datasets. REB-only, UPV-only and Combined REB–UPV were evaluated against core test results as ground truth. Model results revealed that the REB-only model performed reasonably well (R² = 0.75, RMSE = 1.78 MPa, MAPE = 10.8%), UPV-only model exhibited lower predictive capacity (R² = 0.31, RMSE = 2.09 MPa, MAPE = 29.6%) whereas the Combined REB–UPV model outperformed both, achieving an R² of 0.90, RMSE of 1.39 MPa and MAPE of 7.8% during training. However, signs of overfitting were observed during testing, primarily attributed to the limited dataset size. It was demonstrated that combining NDT data with CNN-based deep learning networks can significantly enhance compressive strength prediction over single-method approaches. This study offers a novel application of CNNs—typically used in image recognition—for numeric prediction-based concrete assessment using NDT and Destructive Testing (DT) data. It presents a promising, scalable yet non-invasive practical tool for structural health monitoring.