Kenyatta University Repository
Kenyatta University Institutional Repository is a digital archive that collects, preserves and disseminates scholarly outputs of the Institution
IMPORTANT LINKS
Communities in DSpace
Select a community to browse its collections.
Recent Submissions
Solar Still Basin Measurements and Liner Material Variance for Improved Water Desalination Efficiency
(Kenyatta University, 2025-12) Njuguna, Ruth Njoki
Access to potable water is a persistent global challenge. To address this, clean drinking water can be obtained from the abundant saline sources through solar desalination. Solar stills offer a sustainable and environmentally friendly solution; however, their desalination efficiencies remain relatively low. The impact of basin width to length ration measurements and liner material variance on their performance have not been adequately evaluated. This study aimed to model the thermal performance of a single-slope solar still, assess the influence of basin liner materials and their thicknesses on desalination efficiency, and determine the optimal basin dimensions for improved freshwater production. This was done with the objective of improving the performance of a one-slope solar desalination still. MATLAB is employed in the model development and simulations. The developed model was validated using experimental data from literature. The analysis used Machakos Kenya (1.52 °S, 37.2 °E) climatic conditions as a reference. Five basin liner materials including aluminium, synthetic graphite, brass, galvanized iron and stainless steel were examined based on their thermal properties. Different width-to-length ratios ranging from 0.14 to 0.86 and basin liner thicknesses of between 2 mm and 6 mm were evaluated. A parametric study was conducted to determine the correlation between liner material thickness and basin dimensions, and their combined impact on freshwater yields. From the results, synthetic graphite exhibited the best performance, followed by aluminium, brass, galvanized iron and stainless steel with respective yields of 3835g/m2.day, 2626 g/m2.day, 1864 g/m2.day, 1545 g/m2.day and 1354 g/m2.day corresponding to efficiencies of 35.04%, 24.02%, 17.02%, 14.13% and 12.39%, respectively. Thus, synthetic graphite outperformed aluminium, brass, galvanized iron and stainless steel by 31.4%, 51.43%, 59.675 and 64.64%, respectively. A width-to-length ratio of 0.45 yields optimal results, while a liner material thickness of 4 mm is found to be ideal across all materials. The parametric study further revealed that width-to-length ratio has a higher significance on freshwater yields compared to liner material thickness.
Rapid Detection of Chlorpyrifos in Kale and Milk Using Machine Learning-Aided Raman Spectroscopy
(Kenyatta University, 2025-12) Maina, Thuku Jeremiah
Chlorpyrifos, a widely used organophosphorus pesticide in Kenya, is banned from use on vegetables due to its health risks; however, studies show it is still widely used and detected in food products. Conventional detection methods, such as gas chromatography (GC) and high-performance liquid chromatography (HPLC), are accurate but costly, time-consuming, and destructive, making them unsuitable for rapid on-site analysis. This study aimed to develop a fast, non-destructive method for detecting chlorpyrifos in milk and kale using Raman spectroscopy and machine learning (ML). ML involves computational algorithms that analyze complex data patterns, improving prediction accuracy and classification. These techniques were crucial for efficiently processing spectral data, recognizing patterns, and building predictive models for chlorpyrifos detection. Raman spectroscopy was chosen for its solvent-free, non-invasive nature. Spectral preprocessing steps, including baseline correction, smoothing, and normalization, improved signal quality. Analysis of Variance (ANOVA) was applied to identify Raman bands with statistically significant differences, and Principal Component Analysis (PCA) revealed the spectral fingerprint and reduced dimensionality. The 314-354 cm⁻¹ spectral band, centered at 342 cm⁻¹, was identified as the chlorpyrifos Raman fingerprint due to distinct C-Cl vibrational modes absent in untreated samples. Machine learning models, including Support Vector Machine (SVM), Support Vector Regression (SVR), and Random Forest (RF), were trained using Principal Components (PCs) from the fingerprint. These models were used to classify chlorpyrifos levels in the samples with respect to the Maximum Residue Limit (MRL), the highest permissible pesticide concentration in food for consumer safety, ensuring the models provided relevant food safety assessments. Classification models achieved high accuracy: SVM outperformed RF with 95.79% accuracy in milk and 92.61% in kale, while RF achieved 95.23% and 90.15%, respectively. In regression tasks, RF showed superior performance with a coefficient of determination (R²) > 0.9997 and a root mean square of prediction (RMSEP) < 0.0231 ppm, compared to SVR’s R² > 0.9961 and RMSEP < 0.0897 ppm. These results confirm that Raman spectroscopy combined with ML offers a highly accurate, rapid, and non-destructive alternative to conventional methods, enhancing real-time food safety monitoring and regulatory compliance
Land Use and Land Cover Change on Watershed Functions in Tungu-Naka River Sub-Catchment in Tharaka-Nithi County, Kenya
(Kenyatta University, 2025-12) Njage, Rose Kanana
Watersheds provide a main function of maintaining ecological balance and giving the most important services, like a home for biodiversity, water provision, and agricultural yields. The Tungu-Naka sub-catchment is an important hydrological unit that maintains different socio-economic activities for the communities in the watershed. The primary objective of this research was to assess the impacts of land use and land cover change (LULCC) on watershed functions in the Tungu-Naka River sub-catchment, Tharaka Nithi County. The specific objectives were to: (1) analyze the trends in land use and land cover in the Tungu-Naka watershed from 2002 to 2018, (2) identify and evaluate the driving factors of land use and land cover change in the Tungu-Naka sub-catchment between 2002 and 2018, and (3) assess the impacts of land use and land cover change on hydrological and ecological functions of the Tungu-Naka watershed. A mixed-methods research design combining both qualitative and quantitative approaches was employed. LANDSAT TM imagery from 2002, 2006, 2012, and 2018 was obtained from the Regional Center for Mapping of Resources for Development (RCMRD) in Kasarani. These images were used to analyze LULCC over the study period and partially evaluate its impacts on the Tungu-Naka sub-catchment through supervised classification. The study targeted households residing within the Tungu-Naka watershed, as well as key informants. SPSS software was used to examine the relationship between LULCC and watershed functions, specifically employing Pearson’s product-moment correlation coefficient (r). Key findings revealed significant LULCC, including the conversion of wetlands for farming, deforestation, the destruction of riparian zones, and increased settlement. Population growth and planting of eucalyptus were identified as primary drivers of LULCC. The main impacts observed included drying of wetlands and reduced river water volumes. Population growth, urbanization, and shifts in farming practices were highlighted as significant contributors to land use changes. The research identified riparian land restoration through tree planting as a common watershed conservation strategy within the sub-catchment. However, this approach has been largely unsuccessful due to tree-cutting by farmers and its limited application in the upper watershed zones. The study rejected the null hypothesis and accepted the alternative hypothesis, confirming a statistically significant relationship between LULCC and watershed functions. There is a need for regulated water abstraction in the rivers and for strengthening the capacity of the community members on the importance of protecting riparian land. From the findings, there is a need for a debate on the suitable land use activities that can be considered in the different watersheds. Consequently, there should be proper channels of communication by the community members to the relevant authorities in case of an illegal activity within the watershed.
Instructional Resources Effects on English Performance among Learners with Learning Disabilities in Standard Five in Murang’a County, Kenya
(Kenyatta University, 2025-11) Mwangi, Lucy Wanjugu
The increase in the number of slow learners and children with learning disabilities in schools in Africa has become a major issue and concern. In Murang’a County, not much attention has been given to the areas of special needs education, especially the education of learners with learning disabilities. The purpose of this study was to assess the impact of teaching/learning resources on English performance among learners with learning disabilities in Standard Five in Murang’a County. The objectives of this study were to: establish the prevalence of learning disability among standard 5 learners; determine types of teaching/ learning resources, establish the performance in English, establish the adequacy of teaching/learning resources, and determine the organization of teaching/learning resources in the classroom for learners with LD in Kandara Sub-County in Murang’a County. The study was guided by the sensory stimulation theory. The study was based on a descriptive survey design. The study targeted 630 respondents, comprised of 60 head teachers, 70 English teachers in standard five and 500 standard five learners. A sample size of 70 respondents, comprising of 10 head teachers, 20 teachers teaching English, was obtained through the use of purposive sampling and 50 standard five learners were selected using the simple random sampling technique. Questionnaires, a lesson observation schedule and a screening tool checklist were used to collect data. Piloting of instruments was done in one school within the same locality and was not included in the study. Content validity was determined by employing the expertise of the researcher’s supervisors at the department, while reliability was determined through the test-retest method and correlation coefficient of 0.79 was obtained. Quantitative data was coded and keyed in the computer for analysis using the Statistical Package for Social Sciences (version 25.0). Quantitative data were analyzed using frequencies and percentages and the findings were presented using tables and figures. Qualitative were analyzed using the content analysis method and presented through narratives and texts as per the objectives. The findings revealed that out of the 37 learners exhibiting Learning Disability-related characteristics, the majority had problems with sentence structure, writing mechanics and organizing written work, difficulty memorizing information, having a short span, impulsivity and difficulty manipulating focus, a lack of social skills, misreading or miscopying information, and learning information presented in one way but not in another. Only one school had graphic organizers, two had computers, and another two had projection screens. However, charts and texts were available in seven schools. All teachers agreed that they used books as teaching and learning resources for the English language. The study concluded that the main challenge in using teaching/learning resources for learners with LDs as teacher incompetence rather than inadequacy or inappropriateness of resources. The study recommended that the Ministry of Education should provide school administrators and educators with support structures and training in features that make inclusive education work. The school managers should coordinate and enhance collaboration between special teachers and regular teachers for effective use of teaching/learning resources
Credit Management Practices and Bad Debt Levels of Microfinance Institutions in Nairobi City County, Kenya
(Kenyatta University, 2025-10) Choda, Linus James Odongo
Between the years 2018 to 2021, the bad debt levels of MFIs in Nairobi City County, Kenya have been increasing by 12.46% annually. The increasing bad debt levels have negatively affected MFIs’ operations and their profits to the extent of some being declared bankrupt. The general objective of the study is to establish the effect of credit management practices on bad debt levels of microfinance institutions in Nairobi City County, Kenya. The specific objectives of the study include to evaluate the effect of credit risk identification on bad debt levels of microfinance institutions in Nairobi City County, Kenya, to assess the effect of credit risk monitoring on bad debt levels of microfinance institutions in Nairobi City County, Kenya, to assess the effect of collection policies on the bad debt levels of microfinance institutions in Kenya, to establish the effects of credit appraisal policies on the bad debt levels of microfinance institutions in Nairobi City County, Kenya, and to determine the effect of CBK regulations on bad debt levels of microfinance institutions in Nairobi City County, Kenya. The theories underpinning this study include; anticipated income theory, modern portfolio theory (MPT), capital asset pricing model (CAPM), credit risk theory, PRISM model of credit risk management, and public interest theory. The study employed descriptive research design with a target population of 13 active microfinance institutions based in Nairobi City County, Kenya. A sample size of 13 microfinance institutions was selected through census. Both secondary (for bad debt levels) and primary data for credit management practices was collected. Secondary data was collected from journal articles, books, universities repositories for unpublished dissertation and documentary letters. The data collection sheets and questionnaires were administered to the microfinance institutions middle and senior employees such as credit managers, finance analysts, accounts and debt portfolio assistants through the drop and pick technique. The data analysis and entry were done using the SPSS (Statistical Package for Social Science) software. The diagnostic tests that were carried out include normality, multicollinearity, heteroscedasticity, stationarity, autocorrelation, and model specification The ethical considerations that were employed in the study include anonymity were assured; responses were used purely for academic purposes and treated with confident. To determine the reliability and validity of the data instruments a pilot test was conducted. The data collected from different respondents was sorted, cleaned, coded, and analyzed using SPPS software version 29. The data was analyzed using descriptive statistics and diagnostic statistics such as normality, multicollinearity, heteroscedasticity, and the Hausman tests and inferential statistics including correlation analysis, regression analysis, and hypothesis testing. The study established that despite many microfinance institutions developing and implementing credit management practices they were still ineffective to cap the increasing bad debt levels. The study concluded that instant loan issuance without collateral, straightforward loan application processes and the lenient credit monitoring and collection policies have led to a significant proportion of consumers failing to repay their delinquent loans increasing the number of borrowers while concurrently increasing the number of defaulters, resulting into high levels of bad debt among microfinance institutions in Nairobi City County, Kenya. The study recommends that microfinance institutions should adopt technological advancements such as artificial intelligence and big data analytics to enhance their credit management practices and decrease the ballooning bad debt levels. The study also recommends the need for a harmonious credit identification policy where when one microfinance institution has disbursed loan to a borrower that information should be readily available through an integrated system to be used by other microfinance institutions to reduce high bad debt levels occasioned by borrowers moving from one microfinance institution to another with outstanding loans