School of Pure, Applied and Health Sciences (MT)http://repository.mut.ac.ke:8080/xmlui/handle/123456789/28852024-03-28T21:56:21Z2024-03-28T21:56:21ZSpatial Modelling of Malaria Prevalence in KenyaMorris, Mwenda J.http://repository.mut.ac.ke:8080/xmlui/handle/123456789/63932024-02-17T11:21:05Z2022-10-01T00:00:00ZSpatial Modelling of Malaria Prevalence in Kenya
Morris, Mwenda J.
Malaria is a leading cause of deaths in Kenya. A vector-borne disease caused by parasite of genus plasmodium; the disease is introduced into the human circulatory system from bites caused by infected female anopheles’ mosquitoes. A lot of effort and resources has been put in the fight against malaria, with large amount of national budget being used in the fight against malaria in developing countries which has led to underdevelopment, impoverished livelihoods and low human development index. Malaria burden affects the world’s poorest countries. About 90% of the malaria burden is reported in sub-Saharan Africa. Malaria cases are significantly high in countries of south-East Asia, Western Pacific region, Mediterranean and the Americas. As of 2017, five countries India, Uganda, DR Congo and Mozambique accounted for half of malaria cases reported around the world. In Kenya, the disease has led to impoverished livelihoods with the poorest communities of the country being the most affected. The disease has led to high mortality cases in children under five years and pregnant women. Loss of man hours and work days among adults in the country, leading to low productivity. Studies have shown that there has been a general lack of knowledge on how select demographic and social economic conditions risk factors affect the prevalence of malaria in Kenya. The method of the study involved performing the spatial models for malaria prevalence in Kenya while relaxing the assumptions of stationarity. The assumptions of linearity allowed some covariates like age to have a non-linear effect on prevalence of malaria. Using random walk model of 2nd order and the assumption of stationarity, it allowed covariates to vary spatially. Conditional autoregressive model was used. Data from malaria indicator survey of 2015 (KMIS2015) was used for the study. Both the social-economic and demographic variables were used as predictor variables. These included education level, wealth index, age, access to mosquito nets and place of residence. From the study, demographic and social-economic factors were found to have significant impact on Prevalence of malaria in Kenya. Most cases of malaria were reported in lake, western and coastal regions. The most prone areas were Kisumu, Homabay, Kakamega and Mombasa. There were less cases in central Kenya counties like Nyeri, Tharaka-Nithi with a significant number reported in arid and semi-arid regions of Northern-Kenya counties of Garissa, Mandera, Baringo. Rural population was more susceptible to malaria compared to those in urban areas. The odds of getting (verse not getting malaria) in urban places of residence increases by 0.84, which is estimated to .096, CIs 95% (0.70, 1.01), and a p-value .069. Malaria prevalence varied significantly from one region to another. The study established that Spatial autocorrelation exists among regions mostly due to weather patterns, geography, cultural practices and socio-economic factors.
Master of Science in Statistics, 2022
2022-10-01T00:00:00ZForecasting of Banking Sector Security Prices in Kenya Using Machine Learning TechniquesMarwa, Hassan C.http://repository.mut.ac.ke:8080/xmlui/handle/123456789/63922024-02-17T11:25:12Z2022-10-01T00:00:00ZForecasting of Banking Sector Security Prices in Kenya Using Machine Learning Techniques
Marwa, Hassan C.
Financial analysts should be able to make accurate predictions about the direction of the stock market. First and foremost, investors should understand how the stock market works before making any form of investment in a company. An investment in a stock that is trading at a cheap price at the right moment can result in profits, but an investment in a stock that is trading at a high price at the wrong time might result in low results. When deciding whether to buy, sell, or do nothing at all, experienced traders typically use a combination of indicators to make their assessment. Less experienced traders, on the other hand, may not be able to recognize the appropriate patterns of market elements when using the available indicator baskets. Throughout the course of history, there have been a wide variety of strategies and methods that may effectively predict the behaviour of stocks. On the other hand, more resources have been devoted to the study of the stock market since Machine Learning (ML) was developed, and it has been shown that accurate stock market prediction is feasible. Although research in this area has been done, there hasn't been any that compares the various machine learning algorithms for predicting the securities of the Kenyan banking sector. As a result, the purpose of this study was to make projections on the pricing of banking sector securities in Kenya by utilizing Machine Learning techniques and fit Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models for forecasting banking sector security prices. The study made its predictions based on the random walk theory of predicting stock market movements. The research aimed to look at all of the banks that are traded on the Nairobi Securities Exchange, and a representative sample was picked from three of those institutions, using purposive sampling technique: the Kenya Commercial bank, the Equity bank, and the Co-operative bank. The correctness of each model was evaluated by applying the Root Mean Squared Error (RMSE) criterion, the Mean Squared Error (MSE) criterion, the Mean Absolute Percentage Error (MAPE) criterion, and the Mean Absolute Error (MAE) criterion. The confusion matrix criterion was used to choose the most effective model. With an RMSE of 0.1217, SVM beat the other models when using the accuracy metrics criteria and the Confusion Matrix. In comparison, ANN and ARIMA had RMSEs of 0.1477 and 0.1743 respectively. It was also clear, based on the confusion matrix, that SVM performed better than ANN since it had an accuracy of 0.6171, which is equivalent to 61.71 percent, whereas ANN only had 0.5959, (59.59%). The study recommended that SVM should be used by financial experts for stock price predictions. For further research, historical data can be used in conjunction with studies of financial and political events when forecasting.
Master of Science in Statistics, 2022
2022-10-01T00:00:00ZRemoval of Phenol, Hydroquinone and 2-Naphthol from Aqueous Solutions Using Tertiary Amine Modulated CornstarchBosuben, Haronhttp://repository.mut.ac.ke:8080/xmlui/handle/123456789/63782024-02-17T11:24:48Z2022-11-01T00:00:00ZRemoval of Phenol, Hydroquinone and 2-Naphthol from Aqueous Solutions Using Tertiary Amine Modulated Cornstarch
Bosuben, Haron
Phenolics are a family of chemical compounds with at least one hydroxyl group attached directly to aromatic group. The sources of phenolics pollutants in water are mainly chemical industries and pharmaceutical. They are harmful and toxic even at modest concentrations. Some of their health effects are irreversible and it is prudent to remove phenolics from water to overcome them. The method that has attracted more attention is adsorption. Major strides have been made over years to search for highly selective and efficient bio-adsorbent for water remediation. Despite substantial achievements, there is need to search for bio-adsorbent which is ecofriendly and efficient. The objectives of the study were to modify cornstarch using triethanolamine and to characterize unmodified and modified cornstarch using Fourier Transform Infrared (FT-IR), to optimize selected adsorption parameters for the removal of phenol, hydroquinone and 2-naphthol in water using aminated cornstarch (ACS) and to determine the adsorption capacity of dried raw cornstarch (RCS) and ACS in removal of phenolic compounds (PCs). Pseudo first and second order kinetic models were used to determine mechanisms involved in both chemisorption and physisorption processes. The Langmuir and Freundlich isotherms were used to determine adsorption capacity of the ACS and whether it was monolayer or multilayer. The modulation of cornstarch was in two steps; chlorination of cornstarch using acetyl chloride reflux in aniline at 70 ℃ and 120 rpm for 5 hours, and amination using triethanolamine refluxed at 40 ℃ and 120 rpm for 3.5 hours. The FT-IR spectrum of the ACS showed strong broad band with increased intensity at 3295.44cm-1 which confirmed C-N stretch of amine group and N-H stretch of amine salt were anchored. The efficiency of modified cornstarch in phenolics removal at different pH levels, contact time, initial phenolics concentration and dosage of ACS in aqueous media at constant temperature (25±1 ℃) were investigated. Batch studies revealed that maximum removal of PCs was realized at a contact time of 10 mins, pH of 5.0-6.0 and constant temperature of 25±1 ℃ for the phenol, hydroquinone and 2-naphthol compounds. The uptake increased with increase in the dosage of ACS and initial concentration of phenolics. The rate of adsorption process was best described by the pseudo – second order kinetic model, indicating that the rate mechanism was chemisorption. The maximum uptake of PCs occurred at initial concentration of 10ppm and then plateaued. The batch experimental data obtained best fitted into the Langmuir isotherm with regression coefficient, R2=0.9998, 0.9999, 1.000 and monolayer adsorption capacities of 4.297, 4.585 and 5.048 mg/g for phenol, hydroquinone and 2-naphthol respectively. The adsorption process was monolayer and homogenous in nature. These adsorption capacities were relatively higher than many reported processes, thus indicating that the ACS an effective adsorbent for removal of PCs from aqueous media. The findings from this study provides an alternative biopolymer that may be used for the removal of phenolic compounds from water.
Master of Science in Chemistry, 2022.
2022-11-01T00:00:00ZCharacterization and Modification of Clays from Selected Sites in Murang’a and Nyeri Counties Kenya for Assessing Refractory ApplicationsLomertwala, Mnangat Hassanhttp://repository.mut.ac.ke:8080/xmlui/handle/123456789/45682024-02-17T11:18:42Z2020-03-01T00:00:00ZCharacterization and Modification of Clays from Selected Sites in Murang’a and Nyeri Counties Kenya for Assessing Refractory Applications
Lomertwala, Mnangat Hassan
Clay is a stony or earthy mineral aggregate composed of fine-grained minerals, which are plastic at appropriate water content and hardens up when fired. Uses of clay include manufacture of cement, ceramics, bricks, drilling clays, paints, fillers in paper industry and for refractory production. Refractories are materials that withstand high temperatures and are comprised of oxides with high melting temperature such as Al2O3, SiO2, ZrO, Cr2O3, and MgO. Clay deposits have been reported in Kenya in areas such as Kano plains, Mwea, Chavakali and Ilesi. Murang’a and Nyeri counties are also two of such areas with clay deposits. Despite the abundance of clay, Kenya still imports both clay products and raw clays for industrial use, clay worth 3 billion Kenya shillings was imported in 2013. This project intended to determine the elemental and mineralogical composition of clays from Murang’a and Nyeri counties to determine whether they can be used for refractory applications. Futhermore, the study determined the effect of acid washing and addition of CaO on the refractory properties of clays. Most of Kenyan clays are used for ceramics and cement manufacture.The samples of clays used in this study were obtained from Githima (0° 46´ 40´´ S and 37º 6´ 31´´ E), Kimathi Sampling site (0º 40´ 0´´ S and 37º 10´ 28´´ E) and Ithanje Sampling site (0º 36´ 30´´ S and 37º 6´ 46´´ E). Elemental and mineralogical composition were determined using Atomic Absorption Spectrophotometer and X-Ray Diffraction respectively. The clays were leached using hydrochloric and oxalic acid separately at concentration of 0.0, 0.1, 0.25, 0.5, 1, and 2 M. The clay was blended with 0, 1, 2, 5, 15 and 30 % of CaO. The raw, acid-treated and clays containing CaO were moulded into blocks (8x4x4 cm) followed by air and oven drying then fired in a furnace at 1000 ºC. The fired bricks were tested for apparent porosity, bulk density, linear shrinkage, and refractoriness. The major components of the raw clays when expressed as oxides were 40.80-55.40 % SiO2, 16.27-30.33 % Al2O3, 0.62-7.62 % TiO2, 0.84-2.65 % K2O, 0.02-1.82% MgO. Elemental composition of the acid treated clays, in oxide form were in the ranges of 40.80-65.16 % SiO2, 7.16 -30.33 % Al2O3, 0.33-7.62 %TiO2, 0.24-2.65 % K2O, 0.01-1.82 % MgO. Refractory properties of the raw clays were in the ranges 26.31-31.33 % apparent porosity, 1.56-1.68 g/cm3 bulk density, 1-3 % linear shrinkage, and 1609-1686 °C refractoriness. Refractory properties of the acid treated clays were in the ranges of 21.65-35.1 % apparent porosity, 1.32-1.76 g/cm3 bulk density, 1-3.62 % linear shrinkage, and 1575-1686 °C refractoriness. Refractory properties of CaO added clays were in the ranges of 22.16-31.33 % apparent porosity, 1.56-2.8 g/cm3 bulk density, 0.8-3.3 % linear shrinkage, and 1579-1686 °C refractoriness. These clays meet certain aspects of refractory materials. However, they require enhancement through acid treatment and CaO addition to meet requirements for refractory application. Proper utilization of findings from this study would improve Kenya’s industrialization and economic diversification.
Masters of Science in Chemistry, 2020
2020-03-01T00:00:00Z