Application of Simplex Lattice Design in Maize Fodder Production

This study involved mixture experiment using simplex lattice design approach in cultivation of Maize crop with the view of optimizing the fertilizer components (dependent variables) on the output parameter (maize fodder). The objective of this study was to evaluate optimal sets of mixture of fertilizer components that could maximize the response variables of interest. Di-Ammonium Phosphate (DAP), Poultry manure, Sheep manure, and Farmyard manure components mixed in various proportions in accordance with simplex lattice design were applied in planting hybrid maize seeds. With the application of the special cubic statistical model formulated, it was found that farmyard manure and poultry manure produced the optimal fertilizer condition. However, the this study further provided specific optimal fertilizer blend for maize fodder production as 8.0 tons ha-1of farmyard manure mixed with 1.212 tons ha-1 of poultry manure. Under these conditions, a maximum outputs 42 tons ha-1 of maize fodder were realized. The study concluded that the formulation of statistical model for crop production could be useful for prediction and evaluation of the effects of experimental factors. KEY WORDS: Maize fodder; Fertilizer components; Model; Mixture experiment; Simplex Lattice Design; DOI: 10.7176/MTM/9-7-05 Publication date: July 31st 2019

The Optimization of Maize yield Production Using Simplex Lattice Design for Third Degree Mixture Model

This study involved mixture experiment for fertilizer components in maize crop production. Researchers in agriculture have conducted research on maize plants with different levels of single fertilizers with a view of obtaining an appropriate amount for optimal yield. However, studies based on fertilizer blending are not very common. This has left farmers with no option other than to continue applying fertilizer in random proportions that may not guarantee the optimal yield with respect to fertilizer components available. The objectives was to determine appropriate statistical models expressing the maize yield as response variable and to evaluate optimal sets of mixture of fertilizer components that could maximize the response variables of interest. Di-Ammonium Phosphate (DAP), Poultry manure (guano), Sheep manure, and Farmyard manure were the four independent variables to optimize the response value of the maize yield. Mixture experiments entail the blending of these components to determine if synergism exists in the mixture or blends of these fertilizer components. The statistical model formulated for the maize yield demonstrates the effects of each component and the interaction with other components displaying the trend of the response parameter. From the model, it can be concluded that farmyard manure and poultry manure have greater effect on the production of maize yield and hence, this study conclusively attained the optimal conditions of 6.67 tons ha-1of farmyard manure mixed with 1.3467 tons ha-1 of poultry manure. Under these conditions, the farmer achieves maximum output of 12.17 tons ha-1 of maize yield. The study upholds that mixture experiments are appropriate in modeling agricultural production involving various independent parameters that produces synergetic effect on the output parameter. KEY WORDS: Maize yield; Fertilizer; Model; Mixture experiment; Simplex Lattice Design. DOI: 10.7176/MTM/9-7-07 Publication date: July 31st 201

Modelling Effects of Process Variables During Fermentation of Pineapple Peels Using Yeast for Ethanol Production Using a Second Order Optimal Rotatable Design in Four Dimensions.

The need for a cleaner environment in urban areas and the high cost of petroleum products which are becoming scarce due to unbalanced relation between supply and demand besides air pollution of sources has led to the research for other fuels to replace fossil fuels. Ethanol from biomass waste is such an alternative to petroleum products. Most studies on optimization of process variables using Response Surface Methodology apply Central Composite Designs yet other designs exist. Optimal designs have fewer trials employed with the aim of obtaining efficient designs for fitting reduced quadratic or higher order models. Coded values of a second order optimal rotatable design in four dimensions constructed using balanced incomplete block designs (BIBD) was fit into experimental data in order to study the effects of four process variables namely; time, PH, temperature and substrate concentration on fermentation of pineapples peels using Saccharomyces cerevisiae for ethanol production. Normal probability plots and Multiple R-squared of 0.9323 and Adjusted R-squared of 0.8944 which measure model fitting reliability indicated aptness of the model. Most values of Probability F were less than 0.05, confirming that the model terms were significant and only 6.8% of the total variation could not be explained by the model ensuring good adjustment of the model to experimental data. Model adequacy was also confirmed by the good agreement between the experimental data and predicted values. The design was found reliable in modeling, and studying the effects of the four factors to the processes of fermentation of pineapples peels as substrate for ethanol production using Saccharomyces cerevisiae Keywords: Ethanol, Pineapple Peels, Response Surface Methodology and Rotatable Designs

Influence of Climate Variability on the Prevalence of Dengue Fever in Mandera County, Kenya

Climate variability affects human health by influencing processes that facilitate transmission of infectious diseases. This study aimed at investigating the influence of temperature and precipitation variability on the prevalence of Dengue fever in Mandera County from 1985 to 2014. 196 randomly selected respondents drawn from households within three health facilities’ catchments were interviewed. Meteorological data was used to describe climate variability while Dengue fever was described using data obtained from health records at El Wak Sub County Hospital, Kotulo Health centre and ADRA Hospital. Results showed that temperature significantly varied (t = 7.60, DF = 29, p = 0.0001). Precipitation equally varied (t = 5.660, DF = 29, p = 0.0001). Overall temperature increase was by 0.53°C while annual precipitation amounts increased by 77.1mm. There was an insignificant correlation in Dengue fever occurrence with climate variability (r value of 0.087). The study concluded that climate variability was not significant in Dengue fever transmission in Mandera South Sub County. Keywords: arboviral disease, climate variability, dengue fever, impact, pathogen, prevalence DOI: 10.7176/JEES/9-3-06 Publication date:March 31st 201

E-Optimal Designs For Maximal Parameter Subsystem Second-Degree Kronecker Model Mixture Experiments

Many products are formed by mixing together two or more ingredients, for example, in building construction; concrete is formed by mixing sand, water and cement. Many practical problems are associated with investigation of mixture of m ingredients which are assumed to influence the response through the proportions in which they are blended together. Second degree Kronecker model put forward by Draper and Pukelsheim is applied in the study. This study investigates E-optimal designs, second degree Kronecker model, maximal parameter subsystem for two, three and four ingredients, where Kiefer’s function serves as optimality criteria. The consideration was restricted to weighted centroid design for completeness results. By employing the Kronecker model approach, coefficient matrices and a set of feasible weighted centroid designs for maximal parameters subsystem is obtained. Once the coefficient matrix is developed, information matrices associated to the parameter subsystem of interest for two, three, and four is then obtained. E-optimal weighted centroid designs based on maximal parameter subsystem for the corresponding two, three, four ingredients is derived. Also optimal weights and values for the weighted centroid designs were numerically obtained using Matlab software. Results based on maximal parameter subsystem, second degree mixture model with two, three and four ingredient for E-optimal weighted centroid design for information matrix.....

Applying the Polynomial Model in Simplex

The component proportions of dairy feeds using simplex- centroid design approach was considered. Soya beans, maize jam, cotton seed and fish meal were blended at each design point of the Simplex-Centroid and the responses in terms of dairy animal productivity was considered. The main objective of the study was to test a simplex- centroid design and search for optimal mixture of feed ingredients to the outcome on milk productivity of dairy animals using a polynomial model. Research therefore seek to derive a polynomial model for the four components simplex- centroid design. Using the collected data, fit the derived model and determine the coefficient at each design point and test for their significance using R-software. The result proved that the feed supplement had a significance effect on the milk productivity. However ANOVA was run to improve the precision of the model by taking into account the constant term. This proved that also other feeding practices were significantly important in the productivity. The result showed that the blend of soya beans and Fish meal supplement has significance contribution at 0.05. The concentrate of soya beans, cotton seed and fish meal supplement shows significance contribution at 0.05. Again at the centroid point where all the four ingredient are blended was significance at 0.05

Transmission Dynamics and Optimal Control of Malaria in Kenya

A Journal article by Dr. Gabriel Okello, a faculty in the Chandaria School of Business in USIU-AfricaThis paper proposes and analyses a mathematical model for the transmission dynamics of malaria with four-time dependent control measures in Kenya: insecticide treated bed nets (ITNs), treatment, indoor residual spray (IRS), and intermittent preventive treatment of malaria in pregnancy (IPTp). We first considered constant control parameters and calculate the basic reproduction number and investigate existence and stability of equilibria as well as stability analysis. We proved that if , the disease-free equilibrium is globally asymptotically stable in . If , the unique endemic equilibrium exists and is globally asymptotically stable. The model also exhibits backward bifurcation at . If , the model admits a unique endemic equilibrium which is globally asymptotically stable in the interior of feasible region . The sensitivity results showed that the most sensitive parameters are mosquito death rate and mosquito biting rates. We then consider the time-dependent control case and use Pontryagin’s Maximum Principle to derive the necessary conditions for the optimal control of the disease using the proposed model. The existence of optimal control problem is proved. Numerical simulations of the optimal control problem using a set of reasonable parameter values suggest that the optimal control strategy for malaria control in endemic areas is the combined use of treatment and IRS; for epidemic prone areas is the use of treatment and IRS; for seasonal areas is the use of treatment; and for low risk areas is the use of ITNs and treatment. Control programs that follow these strategies can effectively reduce the spread of malaria disease in different malaria transmission settings in Kenya

Cost Effectiveness Analysis of Optimal Malaria Control Strategies in Kenya

A publication by Gabriel Okello, a Staff at USIU-A from his PhD Thesis in the Department of Statistics and Computer Science, Moi UniversityMalaria remains a leading cause of mortality and morbidity among the children under five and pregnant women in sub-Saharan Africa, but it is preventable and controllable provided current recommended interventions are properly implemented. Better utilization of malaria intervention strategies will ensure the gain for the value for money and producing health improvements in the most cost effective way. The purpose of the value for money drive is to develop a better understanding (and better articulation) of costs and results so that more informed, evidence-based choices could be made. Cost effectiveness analysis is carried out to inform decision makers on how to determine where to allocate resources for malaria interventions. This study carries out cost effective analysis of one or all possible combinations of the optimal malaria control strategies (Insecticide Treated Bednets—ITNs, Treatment, Indoor Residual Spray—IRS and Intermittent Preventive Treatment for Pregnant Women—IPTp) for the four different transmission settings in order to assess the extent to which the intervention strategies are beneficial and cost effective. For the four different transmission settings in Kenya the optimal solution for the 15 strategies and their associated effectiveness are computed. Cost-effective analysis using Incremental Cost Effectiveness Ratio (ICER) was done after ranking the strategies in order of the increasing effectiveness (total infections averted). The findings shows that for the endemic regions the combination of ITNs, IRS, and IPTp was the most cost-effective of all the combined strategies developed in this study for malaria disease control and prevention; for the epidemic prone areas is the combination of the treatment and IRS; for seasonal areas is the use of ITNs plus treatment; and for the low risk areas is the use of treatment only. Malaria transmission in Kenya can be minimized through tailor-made intervention strategies for malaria control which produces health improvements in the most cost effective way for different epidemiological zones. This offers the good value for money for the public health programs and can guide in the allocation of malaria control resources for the post-2015 malaria eradication strategies and the achievement of the Sustainable Development Goals

Replication Data for: Comparing Drug Regimens for Clearance of Malaria Parasites in Asymptomatic Adults using PCR in a Clinical Trial in Kilifi County, Kenya

The dataset contains information on 90 healthy malaria asymptomatic adult participants that were recruited to participate in the study and randomized to three different study groups. Malaria parasite monitoring was done by PCR. In the first 3 weeks, blood samples were taken and tested by PCR three times per week, in the second 3 weeks PCR tests were done twice per week and in the final 6 weeks PCR testing was done once every week. A total of 2249 samples covering 25-time points were collected and analyzed for the presence or absence of malaria parasites by qPCR. A genotyping variable was used to differentiate between old and new P. falciparum infections within individuals over time</p