Theses and Dissertations

Heavy Metals and Pathogen Content on Soil, Water and Produce in Urban Agriculture of Nairobi City

2026-08-31T13:27:36Z

Heavy Metals and Pathogen Content on Soil, Water and Produce in Urban Agriculture of Nairobi City Njenga, John Ng’ang’a Urban and peri-urban agriculture in Nairobi City County (NCC) is growing as a means of income generation, employment, nutrition and food security. However, environmental health risks outweigh urban agriculture practices in the city, as evident from widespread pollutant sources such as garbage dump site heaps, burst raw sewage pipes, contaminated wetlands, industrial effluent, and motor vehicle exhaust. Consumers are increasingly aware of the importance of buying safe food, and therefore urban agriculture practitioners must be informed on the safety status of their produce in order to adjust to good agricultural practices and take full advantage of the easily accessible consumer food market. The objectives of this research were to assess heavy metal incidence in inputs and food crops for urban farms in eastern zone of Nairobi, determine microbial incidences in food crop produce from farms, determine differences in element uptake by the crops, and to examine influence of environmental quality of the surrounding spaces on crop produce safety. A purposive sampling of farmers who grew any of the three target crops namely arrowroot (Colocasia esculenta L.), kale (Brassica olerasii var.Acephala L.) and tomatoes (Lycopersicon esculentum Mill.) was conducted of which ninety five farmers were identified. To focus on the critical regions of the city and with the guidance of Sub-County Agricultural Officers, three sub-counties of Starehe, Kamukunji and Kasarani, in the Eastern part of Nairobi City County were selected. Farm plots that met the criteria for the three target crops were identified (Starehe 4, Kamukunji 7 and Kasarani 4). From each of the 15 farm plots, samples of tomatoes, arrowroots, kales, water and soil were obtained with three replications per farm, following standard procedures for sample collection. Farmer’s immediate environment (neighbourhoods) were classified according to three pollution levels low, moderate, and high that were classified based on source and type of inputs and the condition of the environment surrounding the farm plot. Contents of cadmium, manganese, zinc and lead were analysed using Atomic Absorption/Flame Emission Spectrophotometer. Microbial analysis on irrigation water and the three produce was done to test for presence of salmonella, shigella and Escherichia coli bacteria. One way ANOVA was applied (i) to examine differences in means of heavy metals between locations and to compare transfer factor (TF) index of the crops and (ii) to examine differences on occurrence of the pathogenic microbes. Elements that were above allowable limits in the crops were Cd, Mn, Pb and Zn in arrowroots; Cd and Mn in kales and Cd, Mn and Pb in tomatoes. Cadmium content was highest in farm plots within Kasarani (2.44±0.86 mk/kg) and was significantly different from other two locations (p<0.05, Tukey HSD test). Between the neighbourhood qualities, heavy metal contents were different but not significant (p>0.05) between classes. The mean TF index for the heavy metals decreased in the order Cd>Pb>Zn>Mn. A transfer factor index of > 1 for cadmium was recorded in arrowroots, kales, and tomatoes. Overall, the highest mean of E.coli bacteria count was recorded in arrowroots at the Kamukunji sub-county (P=0.002). Between neighbourhoods, E.coli was significantly higher in irrigation water and arrowroots in the low-quality neighbourhoods (p<0.05). Kamukunji had the highest occurrence of Salmonella in irrigation water and arrowroots at 40%. There was no significant difference between neighbourhood classes in the prevalence pattern of Salmonella. The sub-county also had the highest prevalence of Shigella in irrigation water, at 20%. The overall mean rate of occurrence of Shigella was highest in irrigation water and in kales, at a rate of 17% in both cases. Compared to the other sub-counties, Kasarani had a much higher prevalence of Shigella in tomatoes with a significant difference in distribution pattern (p=0.001). Based on FAO/WHO standards, the measured heavy metal contents were high enough to cause health risk concerns. Produce from areas designated as of low environmental quality was not necessarily contaminated, as perceived. Urban agriculture producers in Nairobi need technical risk reduction technologies and legislative support to guarantee quality produce and valuable participation in the urban food system framework. It is recommended that future studies diversify crop types and localities and enhance collaboration between stakeholders. MSc in Horticulture

Influence of User-generated Content on Local Film and Television Production in Kenya

2026-08-31T13:11:40Z

Influence of User-generated Content on Local Film and Television Production in Kenya Mulinya, Sheila Joy The advent of digital technology and the rapid growth of social media platforms have ushered in a new era of media consumption that is characterized by the increased production and dissemination of User-Generated Content (UGC). This transformation has influenced the way audio-visual content is created, distributed and consumed, with local film and TV producers increasingly adopting digital platforms such as Over-The-Top Television (OTT-TV), Video-On-Demand (VOD) and vertical films that are shared on YouTube, Instagram and TikTok. UGC has also introduced new models of content monetization, where creators receive direct economic incentives from audiences, brands and digital platforms. This study sought to examine the influence of UGC on local film and TV production in Kenya. The study was guided by five objectives that examined the influence of technological factors, economic incentivization, treatment, timeliness and the moderating influence of media policy on the relationship between UGC and local film and TV production in Kenya. The study was anchored on the Technology Acceptance Model (TAM), Disruptive Innovation Theory (DIT), Uses and Gratifications Theory (UGT) and Advocacy Coalition Framework (ACF). A convergent parallel mixed-methods research design was adopted, integrating quantitative and qualitative approaches. The target population comprised 2,167 local film and TV producers and UGC creators on YouTube, Instagram and TikTok. Quantitative data was collected from 384 respondents who were selected through proportionate stratified random sampling, while qualitative data was further obtained from 24 key informants from relevant institutions and the local film and TV production industry such as KFC, KFCB, DFS and CA through purposive sampling. Data collection was conducted using questionnaires and interview guides. Quantitative data was analyzed using SPSS Version 25 through descriptive and inferential statistics, including correlation, regression as well as ANOVA, while qualitative data was analyzed thematically. The reliability of research instruments was established through Cronbach’s alpha testing during the pilot study. The findings reveal that UGC significantly influences local film and TV production in Kenya, with technological factors and media policy emerging as the strongest factors. The study found that digital access, internet connectivity and supportive regulatory frameworks enhance the integration of UGC into local film and TV content production processes. Although economic incentivization and timeliness positively influenced local film and TV production, their effects were not statistically significant. The study further established that media policy moderates the relationship between UGC and local film and TV production, highlighting the importance of regulatory support in maximizing the benefits of digital content creation. The study concludes that the integration of UGC, while supported by appropriate technological infrastructure and effective media policies, has the potential to transform local film and TV content production sector by improving production capacity, diversity as well as audience engagement. The study recommends strengthening of digital infrastructure, enhancing regulatory frameworks and developing supportive policies that can encourage sustainable integration of UGC within the local film and TV content production industry in Kenya. PhD in Mass Communication

Fingerprint Classification Using Kmcg Algorithm under Varying Window and Codebook Sizes

2026-08-31T12:56:15Z

Fingerprint Classification Using Kmcg Algorithm under Varying Window and Codebook Sizes Odongo, Winnie Gift Fingerprint classification is a key task in biometric recognition because it supports faster identification, verification, and retrieval in automated fingerprint systems. However, accurate classification remains difficult when fingerprint images contain similar ridge patterns, noise, partial impressions, or variations caused by acquisition conditions. Traditional machine learning methods are efficient and interpretable, but their performance depends on the quality of handcrafted features. Deep learning models often achieve higher accuracy, but they require greater computational resources and provide limited transparency. This study evaluated fingerprint classification using Kekre’s Median Codebook Generation (KMCG) under varying window and codebook sizes and compared its performance with Principal Component Analysis (PCA) and deep learning approaches. The main objective was to determine the effectiveness of KMCG-based feature extraction and compare its performance with PCA-based machine learning and deep learning models. Fingerprint images from the NIST Special Database 302 were used. KMCG extracted texture descriptors under varying window and codebook sizes, while PCA served as a dimensionality-reduction baseline. Support Vector Machine, K-Nearest Neighbours, Random Forest, and XGBoost classifiers were trained using the extracted features. Three convolutional neural network architectures were also implemented to represent different deep learning capabilities: MobileNetV2 was selected as a lightweight and computationally efficient model, InceptionV3 was used to evaluate multiscale feature extraction, and DenseNet201 was included to assess whether dense feature reuse and greater network depth improved fingerprint classification. Performance was evaluated using accuracy, precision, recall, F1-score, average score, confusion matrices, and execution time. MobileNetV2 achieved the best overall performance, with an accuracy of 91.7% and an average score of 94.9% across the evaluation metrics. DenseNet201 achieved 90.0% accuracy, while InceptionV3 recorded 83.4%. Among the traditional machine learning approaches, PCA-XGBoost obtained the highest accuracy of 73.8%, followed by KMCG-SVM at 70.2%. These findings demonstrate that deep learning models provide greater classification accuracy, although KMCG-based approaches remain useful where computational efficiency, compact feature representation, and interpretability are important. The study therefore provides a comparative framework for selecting fingerprint-classification approaches according to performance requirements and available computing resources. MSc in Software Engineering

Optimization of Welding Process Parameters in Flux Cored Arc Welding to Improve Weld Depth of Penetration and Minimize Heat Affected Zone in Pipelines

2026-08-17T11:55:36Z

Optimization of Welding Process Parameters in Flux Cored Arc Welding to Improve Weld Depth of Penetration and Minimize Heat Affected Zone in Pipelines Odhiambo, Victor Otieno Flux Cored Arc Welding (FCAW) is an advanced arc welding process that uses a continuously fed tubular electrode wire with internal flux to generate heat and protect the weld fromcontamination. In the oil and gas industry, ensuring high-quality pipeline welds is critical for structural integrity and public safety. Depth of Penetration (DoP) strongly influences weld joint strength, while excessive Heat Affected Zone (HAZ) can compromise material properties. This study applied Artificial Neural Networks (ANNs) to predict and optimize FCAW process parameters to maximize DoP and minimize HAZ in pipeline welding. The research investigated welding speed, torch angle, contact-tip-to-work distance, welding current, arc voltage, and heat input as key variables. Pipe samples of ASTM A335 Grade B– API 5L Schedule 40 were prepared. The experiments were designed using the Taguchi method. Welding was performed using an MMA/MIG/TIG 200 Prescott FCAW machine. An ANN model was designed and trained in MATLAB, with optimization achieved through Stochastic Gradient Descent (SGD) with momentum. Confirmatory welding trials were conducted at the optimizedparameters. WeldqualitywasassessedusingVickersMicrohardness, Charpy Impact, tensile testing, and microstructural examination. The ANN model achieved an optimum DoP of 7.66 mm and HAZ of 2.90 mm, with prediction accuracies of 99.9948% and 99.9966% respectively, compared to validation results of 7.70 mm DoP and 2.91 mm HAZ at a heat input of 1.13 kJ/mm. Mechanical testing showed a Yield Strength of 276.23 MPa, Ultimate Tensile Strength (UTS) of 483.37 MPa, Engineering Strain of 0.2026 mm/mm, Fusion Zone (FZ) average hardness of 230.7 HV, and Impact toughness of 1.3598 J/mm2. Optimum FCAW process parameters of heat input (1.13 kJ/mm), welding current (126 A), arc voltage (21.5 V), welding speed (115 mm/min), torch angle (450), and contact-tip-to-work distance (5 mm), produced the most desired microstructure, acicular ferrite. The microstructure had refined grains, optimal ferrite-pearlite balance, effective tempering, and minimized brittle phase formation across the Coarse Grained Heat Affected Zone (CGHAZ), Fine Grained Heat Affected Zone (FGHAZ), Inter-Critical Heat Affected Zone (ICHAZ), and Sub-Critical Heat Affected Zone (SCHAZ). The ANNmodeleffectively optimized FCAWparameters, producing welds with deep penetration, narrow HAZ, and superior mechanical properties. This approach offers a reliable predictive tool for improving pipeline weld quality, supporting safer and more durable infrastructure in the oil and gas industry MSc in Mechanical Engineering