Mak IR Repository (Makerere University)
Not a member yet
9127 research outputs found
Sort by
Exploring the effectiveness of mediation as tool for land dispute resolution in Luweero District: a case study of Zirobwe Sub county
No full textA dissertation submitted to the Directorate of Research and Graduate Training in partial fulfillment of the requirements for the award of Master of Science in Land Management Degree of Makerere UniversityThe study explored the effectiveness of mediation as a tool for land dispute resolution in Luweero District, focusing on Zirobwe Sub County. The specific objectives of the study included to evaluate the factors that influence mediation outcomes, to examine the viability of agreements reached through mediation of land disputes and identify challenges encountered during mediation in land disputes, and solutions to the challenges of mediation of land disputes in Luweero district. This study was a case study research design, and employed both quantitative and qualitative approaches to bring in-depth analysis and discussion of the problem under study. Simple random, and purposive sampling techniques were employed to select participants in the study. A self- administered questionnaire was completed by 35 participants who had experienced land disputes, twelve (12) interviews were conducted with key informants that included local leaders, and court mediators. Additionally, one Focus Group Discussion was conducted with eight police officers. Quantitative data was analyzed using frequency tables and charts generated from SPSS Ver.27 and Microsoft Excel. While qualitative data was analyzed through thematic approach by categorizing data into themes which enhanced effective interpretation of results. The key findings from the study revealed several critical factors essential for the viability of mediation agreements. These include a comprehensive understanding of stakeholders' interests, active engagement of the community, transparency, and inclusivity throughout the mediation process. The involvement of local leaders, the personal integrity and experience of mediators, ongoing capacity building, and supportive frameworks are crucial. The findings revealed factors that are essential for better mediation outcomes, and these include; interpersonal skills of the mediators, procedural fairness, cultural sensitivity, and effective communication was deemed critical. Furthermore, the study found out that pervasive lack of awareness, lack of trust in the mediation process, and the enforceability of mediation outcomes present another notable challenge and barrier to mediation as an alternative approach to land dispute resolution. It was concluded that it is imperative to enhance mediation practices, strengthen enforcement mechanisms, and ensure viable commitment to agreements. The study further concluded that establishing an environment where conflicting parties feel valued and heard is crucial for facilitating constructive dialogue and cooperation. The study recommends improving the training and competency of mediators, raising community awareness about the benefits and processes of mediation, establishing clear criteria, qualifications, and procedures for selecting mediators ensures neutrality, competence, and fairness, actively engaging communities in the mediation process is essential, and creating legally binding mediation agreements is necessary to address concerns about enforceability and viabilit
Assessement of compulsory land acquisition grievance management mechanisms in Uganda : a case of Kampala-Jinja expressway road project
No full textA dissertation submitted to the Directorate of Research and Graduate Training in partial fulfilment of the requirements for the award of a Degree of Master of Science in Land Management of Makerere University.This study assessed the developed GRM in compulsory land acquisition on the Kampala Jinja Expressway project to find out how the grievance handling processes are conducted and how they can be improved. The findings focus specifically on the study objectives which included examining the causes of compulsory land acquisition grievances, the grievance management mechanism processes and structures used in compulsory land acquisition on the Kampala Jinja Expressway Road project, the challenges affecting grievance management mechanisms used on the project and the strategies that can be put in place of improve grievance management processes. Both quantitative and qualitative research approaches were utilized where quantitative approach was used to collect numerical data from PAPs who have submitted grievances while qualitative was used to collect non-numerical data from project RAP team and other stakeholders. Employing a mixed-method approach enables triangulation, ensuring that the strengths of each approach complement and compensate for the limitations of the other.
The study concluded that the prevalence of grievances revolves around valuation discrepancies where PAPs feel that their properties were undervalued, social conflicts in the families and communities where the projects are implemented, and survey-related issues such errors and omissions and lastly delayed payment of compensation money emerging as the most significant cause of dissatisfaction among PAPs which escalates the grievances. The study also concluded that majority of the affected community members were unaware of the existence of grievance management mechanisms, resulting in significant delays in handling and resolving grievances. Despite efforts to address these challenges, the findings indicate a prevailing sense of ineffectiveness of the mechanism, with a substantial proportion of PAPs expressing dissatisfaction with the mechanisms in place. The reasons for ineffectiveness include, lack of information, lack of expertise by the grievance handling teams and lack of communication channels during the grievance handling processes
A machine learning approach to property valuation: a case study of Kampala Capital City Authority (KCCA)
No full textMasters dissertationIntroduction: The valuation of real estate properties is a critical task in the real estate industry, influencing decisions related to buying, selling, and investing. Traditional property valuation methods often rely on manual processes and expert judgment, which can be time-consuming and subjective. In recent years, machine learning techniques have shown promise in improving the accuracy and efficiency of property valuation by leveraging large datasets and advanced algorithms. By doing so, they can mitigate or eliminate the effects of decision-making bias and provide a more objective property valuation when trained on diverse datasets.
Purpose: The current property valuation system at KCCA is inadequate and this project aims to investigate the utilization of machine learning algorithms in property valuation, with the objective of developing a model that improves accuracy, efficiency, and decision-making in property valuation processes by providing precise property value estimates. Method: The study employs a dataset of 40,739 property records from Nakawa Division in Kampala, encompassing features such as property location, usage, building construction, size, amenities, rateable and gross values, among others that was analyzed using MS Excel and Python. The valuation model was developed and trained using Decision Trees, Gradient Boosting, Random Forest, and Linear Regression model machine learning algorithms applied to the dataset.
Results: This study demonstrates that the proposed model significantly expedites the process of property valuation compared to conventional methods. Furthermore, through model training, it was observed that tree-based models such as Decision Trees, Gradient Boosting, and Random Forest outperform the Linear Regression model, the previously chosen machine learning algorithm for this research. Among the tree-based models, the Decision Tree performed the best, followed by Gradient Boosting, and lastly, Random Forest, highlighting the overall superiority of tree-based models in accurately predicting rateable property values.
Conclusion: The findings of this study highlight the potential of machine learning in revolutionizing property valuation practices. By leveraging advanced algorithms and large datasets, machine learning can provide more accurate and efficient property valuations, benefiting both industry professionals and consumers
Digital marketing capabilities and student enrolment in private secondary schools in Kampala district: the moderating role of organizational support
No full textA dissertation submitted to the Directorate of Research and Graduate Training in partial fulfillment of the requirements for the award of a degree of Master of Business Administration of Makerere UniversityThis study examined the role of digital marketing capabilities in fostering student enrolment in private secondary schools in Kampala. The study was guided by two specific objectives, i.e., to establish the relationship between digital marketing capabilities and student enrollment in private secondary schools and to investigate the moderating effect of organizational support in the relationship between digital marketing capabilities and student enrollment in private secondary schools in Kampala District. A quantitative research approach and a correlation research design were used for the study. Data was collected using a self-administered questionnaire from 125 private secondary schools that were selected using stratified random and purposive sampling. The data was analyzed using SPSS, where correlation and regression analyses were carried out to establish the relationships. The study findings revealed that: there was a significant and positive relationship between digital marketing capabilities and student enrolment, a positive and significant relationship between digital knowledge and student enrolment, as well as a positive and significant relationship between digital skills and student enrolment. Furthermore, the findings revealed that digital marketing capabilities and organizational support accounted for 24.8% of the variance in student enrolment and that organizational support significantly moderates the relationship between digital marketing capabilities and student enrolment. The study findings suggest that private secondary schools with personnel who are competent in knowledge and skills in undertaking digital marketing activities are likely to attract more students than their non-competent counterparts. It is therefore recommended that administrators of private secondary schools should prioritize capacity-building initiatives to enhance digital knowledge among their staff, invest in specialized training and recruitment to address significant gaps in digital skills among their staff, invest in adequate digital resources such as computers to support effective digital marketing activities, implement regular refresher training programs to keep staff updated on new digital marketing techniques, systems, and software and to adopt a holistic approach to optimize student enrolment by simultaneously investing in both digital marketing capabilities and organizational support
Prediction of meteorological parameters using inverse artificial neural networks.
No full textA thesis submitted to the
Directorate of Research and Graduate
Training for the award of the degree
of Doctor of Philosophy of
Makerere UniversityMeteorological parameter data needed for climate change analysis, and monitoring the
mitigation or adaptation measures taken, is often difficult to obtain due to the high costs
of buying, installing and maintaining measurement equipment. This has resulted into
data gaps. In this study, inverse artificial neural network (ANNi) algorithms were developed for the cheap and fast retrieval of some of the essential meteorological parameters
basing on measured global horizontal solar radiation only. This study utilised ANNi algorithms because they require fewer inputs compared to feed-forward ANN, resulting in
lower implementation costs. The parameters considered were solar radiation, sunshine
hours, relative humidity, maximum, average, and minimum temperatures, and rainfall.
For each algorithm, a feed-forward radial basis function neural network (RBFNN) was constructed, trained, and tested to predict global solar radiation based on the other selected
meteorological parameter(s). After training, the optimal neural network architecture was
saved, and used in the ANNi to aid the retrieval of the meteorological parameter(s). The
inverse retrieval part of the algorithms employs non-linear optimization to retrieve meteorological parameters. We validated the retrieval algorithm using measured data that was
not part of the training data used for the RBFNN, and several statistical metrics. We developed three meteorological parameter retrieval algorithms:- (i) A one−parameter ANNi
algorithm which retrieves sunshine hours with correlation coefficient r, MnB, RMSE, and
MAPE of 0.93, 0.056, 0.97 hr and 19.0%, respectively. (ii) The two−parameter algorithms most accurately retrieved the (SH, Tmax) pair with r, MnB, RMSE, and MAPE
of (0.84, 0.87), (0.90, −0.001), (0.81 hr, 0.56 ◦C), and (12.1%, 1.6%), respectively, for
each of the parameters. (iii) The ANNi algorithms for the simultaneous retrieval of three
meteorological parameters most accurately retrieved the (SH, RH, Tav) set with r, MnB,
RMSE, and MAPE of (0.73, 0.57, 0.61), (0.02, 0.13, 0.03), (0.97 hr, 13.69%, 1.61◦C),
and (11.45%, 18.51%, 5.16%), respectively, for each of the parameters. The ANNi algorithms constructed in this research can improve weather forecasting and long-term climate
monitoring in developing countries by predicting meteorological parameter values where
only solar radiation measurement equipment is available. The data obtained can be used
in several other applications such as in agriculture, civil aviation, and in the study of
atmospheric energy balance, ecosystem evolution, and social sustainabilityDAA
Sustainable practices, stakeholder engagement and the growth of small and medium agro-processing enterprises in Uganda
No full textA thesis submitted to the Directorate of Research and Graduate Training in partial fulfilment of the requirements for the award of the Degree of Doctor of Philosophy in Management of Makerere UniversityThe study investigated the relationship between sustainable practices, stakeholder engagement, and the growth of agro-processing SMEs in Uganda, where many SMEs are still grappling to attain growth. Integrating the triple bottom line (TBL) and stakeholder theories, the study sought to examine how and the extent to which sustainable practices and stakeholder engagement can unlock the potential of agro-processing SMEs to attain improved growth. Utilizing a cross-sectional design and a sequential mixed-method approach, the study focused on agro-processing enterprises in the Greater Kampala Metropolitan Area (GKMA). The quantitative study selected a sample of 367 from a population of 4,229 SMEs using stratified random sampling, resulting in a 97% response rate. Quantitative data were analysed using SmartPLS version 4, while qualitative data were gathered from 30 purposively selected business owners and analysed through thematic analysis using NVIVO version 14. The findings reveal that environmental, social, and economic practices significantly enhance SME growth in the agro-processing sector, with stakeholder engagement partially mediates the relationship between sustainable practices and growth of SMEs. As such, the study recommends agro-processing SMEs should undertake; employee training, support personal growth of employees; ensure quality production to meet changing customer needs and undertake waste management through recycling as a measure for reducing operational costs to attain growth. On the other hand, the government should establish storage facilities for agro-processing SMEs in order to manage fluctuation in prices of the agricultural produce and encourage voluntary compliance to environmental, social and fiscal policies as strategic pathways for attaining growth
Prevalence of dietary adherence and associated factors among adolescents with Type 1 Diabetes mellitus attending St. Francis Hospital Nsambya and Ugand Martyrs Hospital Lubaga Diabetes Clinics
No full textIntroduction: Type 1 diabetes mellitus (T1D) is a chronic condition requiring lifelong
management, including strict adherence to dietary recommendations. Despite the critical role of
diet in managing T1D and preventing complications, many adolescents with T1D struggle with
dietary adherence. This poor adherence among adolescents compromises effective diabetes
management and increases the risk of complications. Understanding the factors that influence
adherence is crucial to developing effective dietary interventions and behavioral strategies that can
improve adherence and health outcomes in this population.
Aim of the study: This study determined the prevalence of adherence to dietary recommendations
among adolescents with Type 1 Diabetes and explored the factors influencing adherence among
adolescents with T1D in St. Francis hospital Nsambya and Uganda Martyr’s hospital Lubaga.
Method: A facility-based convergent parallel mixed methods study was conducted. Quantitative
data were collected from 103 adolescents aged 10-19 years through structured questionnaires and
24-hour dietary recall. The major dependent variable was dietary adherence, while independent
variables included factors such as demographic characteristics, family support, nutrition
counseling attendance, knowledge about nutrition for T1D adolescents, Qualitative data were
obtained via 15 In-depth interviews with adolescents and 7 key informant interviews with
healthcare providers. Quantitative analysis involved descriptive statistics and a modified Poisson
model using STATA v14, while qualitative data were analyzed thematically with Atlas.Ti
software.
Results: Overall dietary adherence among adolescents with Type 1 diabetes across the four
macronutrients was at 17.5%, with specific adherence rates of 29.1% for protein, 15.5% for
carbohydrates, 33% for fiber, and 79.6% for fat intake. Religion was significantly associated with
adherence (PR = 2.93, 95% CI:1.15–7.47 p = 0.025). Higher adherence was observed among
adolescents who regularly attended nutrition counseling attendance (19.7%) and having fathers as
primary caregivers (31.3%). Qualitative findings highlighted barriers to adherence, including peer
influence, school challenges, frequent hunger, and financial constraints.
Conclusion: The study revealed that only 17.5% of adolescents with Type 1 Diabetes Mellitus
adhered to dietary recommendations according to the ISPAD (International Society for Pediatric
and Adolescent Diabetes) criteria across the four macronutrient groups. Key barriers included
developmental challenges, difficulties within the school environment, frequent hunger, and
financial constraints. Religion, social support, and consistent nutritional education emerged as
important factors influencing adherence. Strengthening continuous nutrition education and
counseling at the diabetic clinics, as highlighted by health workers, may improve adherence rates.
Targeted interventions focusing on enhancing nutrition knowledge, providing emotional and
involving caregivers are recommended to improve dietary adherence among adolescents with
T1D
Adopting blended learning for training student nurses within skills labs in public nursing and midwifery schools in Uganda
No full textA dissertation submitted to the Directorate of Research and Graduate Training for the award of the Degree of Doctor of Philosophy of Makerere UniversityThis study investigated the efficacy of blended learning in enhancing the training of student nurses within skills lab settings across public nursing and midwifery schools in Uganda. The rationale for this study stems from the numerous challenges associated with the current traditional face-to-face teaching methods commonly used in nursing education. Traditional methods rely heavily on in-person demonstrations and limited hands-on practice, which often result in insufficient student engagement and incomplete mastery of critical nursing skills. The rapid increase in student enrollments has further strained the already limited infrastructure in public nursing schools, leading to overcrowded skills labs, reduced tutor-student interaction time, and inadequate access to essential lab resources. These challenges have created gaps in students’ practical skill acquisition, leaving them underprepared for clinical practice. Therefore, the study aimed to assess how blended learning could improve nursing education, specifically within the skills lab settings, and address the existing limitations of traditional face-to-face methods. The study utilized a mixed methods approach, combining positivism and interpretivism to provide a comprehensive understanding of the research problem. A cross-sectional survey design was used to collect data from 80 nursing Nurse Educators, 7 principals, and 7 student representatives from 7 public nursing and midwifery schools in Uganda. Additionally, a quasi-experimental design was employed to evaluate the impact of a blended learning intervention on educator competencies. A case study at School G, involving 9 nurse educators and 10 nursing students, was also conducted. Data were collected through survey questionnaires for quantitative analysis, as well as face-to-face interviews and document reviews for qualitative insights. The findings revealed that nurse educators held positive perceptions towards adopting blended learning in skills lab settings, with many recognizing its potential to enhance student nurse training. However, the study also uncovered a significant gap in the competencies of nurse educators to implement blended learning effectively. While educators were open to the idea, many lacked the necessary skills and knowledge to use blended learning technologies optimally. The quasi-experimental analysis demonstrated that after the introduction of a blended learning intervention, there was a marked improvement in both educator competencies and student learning experiences within the skills lab. The regression analyses also revealed that blended learning improves nursing education in the skills labs. In conclusion, the study highlights the importance of integrating blended learning methodologies into nursing education to overcome the limitations of traditional face-to-face methods and create more dynamic and engaging learning environments. To achieve this, nursing schools should incorporate blended learning into their curricula and prioritize comprehensive training programs for both educators and students. This approach will help optimize the learning experience and better prepare nursing students for clinical practice
A Segment Routing Approach to Traffic Engineering In Research and Education Networks
No full textA dissertation submitted to the graduate school in partial fulfillment for the award of the degree of Master of Science in Telecommunication Engineering of Makerere University.Services and applications such as Video-on-Demand, Voice-over-IP, and over-the-top services such as X, YouTube, Facebook, etc. nearly double Internet traffic every other year. A survey conducted among National Research and Education Networks (NRENs) in East and Southern Africa shows that 72% of the NRENs set up their backbones with best effort traffic in mind. However, the same NRENs provide services that have strict bandwidth and latency requirements. Therefore, it is becoming increasingly critical for network operators to efficiently route massive Internet traffic while meeting numerous Service Level Agreements
(SLAs) in terms of latency, packet loss, jitter and bandwidth.
Currently, Internet Service Providers (ISPs) use Policy-Based routing (PBR), Interior Gateway Protocol Traffic Engineering (IGP-TE) and Resource Reservation Protocol Traffic Engineering (RSVP-TE) for intra-domain traffic management with the primary goal of effectively utilising the available network bandwidth. However, these traditional methods lack automation and are neither efficient nor scalable. For instance, in IGP-TE, there is a high chance of traffic congestion as the traffic is being routed over the shortest-paths only. Furthermore, PBR and RSVP-TE do not consider all possible paths, and setup times increase as the number of nodes and links grows or in the event of node or link failures.
Considering these shortcomings, this study investigates the applicability of Segment Routing (SR) as a TE technique in response to dynamic user traffic profiles in Research and Education Networks. SR has been increasingly adopted in large commercial ISPs such as Arelion, Bell Canada, among others. It provides a form of source routing, wherein a packet's predetermined path is encoded within the packet header. The study follows an experimental approach where a multi-vendor network model is built in EVE-NG simulation software. The control plane operation is based on a hybrid setup which consists of a combination of distributed and centralised policy implementation. Traffic scenarios are then developed and incorporated into the simulation to enable validation of this proposed approach.
Results show that SR-TE is easier to deploy, more automatable, and scalable compared to IGP-TE and RSVP-TE. It removes the need for label distribution and path signalling protocols, simplifying packet forwarding. Furthermore, SR-TE enhances load balancing by allowing NRENs to implement weighted traffic distribution leading to an optimisation ratio of 2
The predictive utility of polygenic risk scores for chronic kidney disease in Africans
No full textBackground: Genome-wide association studies (GWAS) have significantly expanded our understanding of the genetic basis of kidney function, with most findings reported in individuals of European ancestry. Such biased sampling has resulted into a general portability problem, where findings made in one ancestry cannot accurately be transferred to individuals in another ancestry. This shortfall has been especially observed in polygenic risk scores (PRS) and discovery of variants associated with disease traits in Africa. To address this disparity, we aimed to: (1) identify susceptibility loci associated with estimated glomerular filtration rate (eGFR) in 80027 individuals of African ancestry (AFR) from the UK Biobank (UKBB), Million Veteran Program (MVP), and Chronic Kidney Disease genetics (CKDGen) consortium, (2) explore the utility of polygenic risk scores (PRS) for serum creatinine eGFR in continental Africans in the Uganda Genome Resource (UGR), (3) assess the causal association between genetically proxied lipid traits and eGFR, and (4) conduct an in-silico study to differentiate potentially harmful single-nucleotide polymorphisms (SNPs) and neutral ones in the UMOD gene, which is causally linked to chronic kidney disease.
Methods: We applied a multi-faceted approach, combining traditional GWAS meta-analysis, PRS methodologies, Mendelian randomization (MR) approaches, and in silico analyses. In the first specific objective, we meta-analyzed eGFRcrea GWAS summary statistics from 80027 African ancestry individuals and further determined the most likely causal SNPs by a Bayesian fine-mapping approach. We further determined the association between the lead variants and other traits or phenotypes by conducting a phenome-wide association (PheWAS) analysis. For the second objective, we computed a PRS using a large discovery dataset of African ancestry individuals, trained, and validated in continental Africans within the Uganda Genome Resource (UGR) individuals. Thirdly, we performed a two-sample MR analysis to determine the causal effect between lipid traits and eGFR. Lastly, we used multi-computational methods to determine the effect of deleterious SNPs on the structure and function UMOD.
Results: We identified eight lead SNPs, one of which was a novel variant, rs77408001 in the ELN gene. Through fine-mapping, SNPs rs77121243 and rs201602445 emerged as likely causal variants. Our PRS analysis enhanced the prediction of eGFR in East Africans, accounting for 0.22% of eGFR trait variance using the clumping and thresholding approach and almost doubled (0.42% trait variance) using the PRScs approach. The PRS derived from a European-ancestry dataset did not accurately predict eGFR in continental Africans, as anticipated. Additionally, our analysis revealed intriguing causal associations with lipid traits markers. Univariable Mendelian randomization (MR) analysis unveiled that genetically predicted low-density lipoprotein (LDL) and total cholesterol (TC) had positive causal effects on eGFR, with effect sizes of 1.1 and 1.619, respectively. In the multivariable inverse-variance weighted (MVIVW) analysis, we further affirmed the causal association between LDL and eGFR, with an effect size of 1.228. Triglycerides (TG) also showed a significant causal effect on eGFR, with an effect size of -1.283. However, genetically predicted high-density lipoprotein (HDL-C) did not exhibit a significant causal link in both univariable and multivariable analyses. Furthermore, in silico analysis of the UMOD gene uncovered two non-synonymous single-nucleotide polymorphisms (nsSNPs), namely rs28934582 and rs28934583, which were associated with deleterious point mutations resulting in changes to residue size and hydrophobicity, as predicted by the HOPE tool. Notably, mutation C181Y brought about a shift in charge from neutral to positive. Our analysis of 3D structures through I-TASSER and SWISS model yielded consistent results, with a model C-score of -0.80, an Estimated TM-score of 0.61±0.14, and an estimated RMSD of 9.7±4.6Å. Additionally, the UMOD gene exhibited interactions with 18 other genes, including those associated with kidney function and innate immune genes like IL2, TNF, IL1B, and ALB.
Conclusion: Our findings provide valuable insights into genetic associations with eGFR in East African populations. We demonstrate that larger datasets of individuals of African ancestry can indeed uncover new insights into chronic kidney disease, genetic variants unique to this population, PRSs with better utility, and a potential impact of lipid traits on kidney function. Additionally, the identification of deleterious UMOD mutants provides a foundation for understanding the genetic basis of disease in this gene. These findings contribute to a more comprehensive understanding of kidney function and disease susceptibility, while emphasizing the importance of diversity in genetic studies.54gene- African Computational Translation Group, The African Computational Genomics Group, MRC/UVRI & LSHT