Healthcare Worker-Related Factors Contributing to Tuberculosis Treatment Non-Adherence among Patients in Kisumu East Sub-County

Background: Treatment non-adherence poses significant risks to health outcomes and impedes the health system’s efficiency, hence curtailing progress towards the end Tuberculosis (TB) strategy under SDG 3.3. Despite interventions to address TB treatment non-adherence, Kenya still reports high TB treatment non-adherence rates of 35% and consequently poor treatment outcome rates. Health Care Workers (HCWs) play a critical role in linking the population to health services, yet little is known of their influence on patients’ TB treatment non-adherence in Kenya. Objective: To analyze HCW-related factors associated with TB treatment non-adherence among patients in Kisumu East Sub-County. Methods: Health facility-based analytical cross-sectional mixed-method study. A Semi-structured questionnaire on treatment adherence and patients’ perceptions of HCWs during the clinic visit was administered to 102 consenting adult (out of a total census of 107 adults) drug-susceptible TB patients. 12 purposively selected HCWs by rank from 6 health facilities participated in Key Informant Interview sessions. Medication adherence was measured using the Morisky Medication Adherence Scale and then expressed as a dichotomous variable. Quantitative analysis utilized STATA version 15.1 while qualitative deductive thematic analysis was done using NVIVO version 14. Results: TB treatment non-adherence rate of 26% (CI: 18% - 36%) was recorded. Overall, patients who felt supported in dealing with the illness were 8 times more likely to adhere to treatment compared to those who were not (aOR = 7.947, 95% CI: 2.214 - 28.527, p = 0.001). Key HCW related factors influencing adherence to treatment included: friendliness (cOR = 4.31, 95% CI: 1.514 - 12.284, p = 0.006), respect (cOR = 6.679, 95% CI: 2.239 - 19.923, p = 0.001) and non-discriminatory service (cOR = 0.1478, 95% CI: 0.047 - 0.464, p = 0.001), communication [adequacy of consultation time (cOR = 6.563, 95% CI: 2.467 - 17.458, p = 0.001) and patients’ involvement in their health decisions (cOR = 3.02 95% CI: 1.061 - 8.592, p = 0.038)] and education and counselling (cOR = 4.371, 95% CI: 1.725 - 11.075, p = 0.002). Conclusion: The study results underline importance of patient-centered consultation for TB patients and targeted education and counselling for improved treatment adherence.

Concentration of Copper, Zinc, Lead, Cadmium, and Nickel in the Incinerator Bottom Ash in Five County Hospitals in Kenya

Healthcare wastes contain potentially harmful microorganisms, inorganic and organic compounds that pose a risk to human health and the environment. Incineration is a common method employed in healthcare waste management to reduce volume, quantity, toxicity as well as elimination of microorganisms. However, some of the substances remain unchanged during incineration and become part of bottom ash, such as heavy metals and persistent organic pollutants. Monitoring of pollution by heavy metals is important since their concentrations in the environment affect public health. The goal of this study was to determine the levels of Copper (Cu), Zinc (Zn) Lead (Pb), Cadmium (Cd) and Nickel (Ni) in the incinerator bottom ash in five selected County hospitals in Kenya. Bottom ash samples were collected over a period of six months. Sample preparation and treatment were done using standard methods. Analysis of the heavy metals were done using atomic absorption spectrophotometer, model AA-6200. One-Way Analysis of Variance (ANOVA) was performed to determine whether there were significant differences on the mean levels of Cu, Zn, Pd, Cd and Ni in incinerator bottom ash from the five sampling locations. A post-hoc Tukey’s Test (HSD) was used to determine if there were significant differences between and within samples. The significant differences were accepted at p ≤ 0.05. To standardize the results, overall mean of each metal from each site was calculated. The metal mean concentration values were compared with existing permissible levels set by the WHO. The concentrations (mg/kg) were in the range of 102.27 - 192.53 for Cu, Zn (131.68 - 2840.85), Pb (41.06 - 303.96), Cd (1.92 - 20.49) whereas Ni was (13.83 - 38.27) with a mean of 150.76 ± 77.88 for Copper, 131.66 ± 1598.95 for Zinc, 234.60 ± 262.76 for Lead, 12.256 ± 10.86 for Cadmium and 29.45 ± 18.24 for Nickel across the five sampling locations. There were significant differences between levels determined by one-way ANOVA of Zn (F (4, 25) = 6.893, p = 0.001, p ≤ 0.05) and Cd (F (4, 25) = 5.641, p = 0.02) and none with Cu (F (4, 25) = 1.405, p = 0.261, p ≤ 0.05), Pb (F (4, 25) = 1.073, p = 0.391, p ≤ 0.05) and Ni (F (4, 25) = 2.492, p = 0.069). Results reveal that metal content in all samples exceed the WHO permissible levels for Cu (100 mg/kg), while those for Ni were below the WHO set standards of 50 mg/kg. Levels of Zn in three hospitals exceeded permissible level of 300 mg/kg while level of Pb exceeded WHO set standards of 100 mg/kg in two hospitals. Samples from four hospitals exceeded permissible level for Cd of 3 mg/kg. This study provides evidence that incinerator bottom ash is contaminated with toxic heavy metals to human health and the environment. This study recommends that hospitals should handle the bottom ash as hazardous wastes and there is need to train and provide appropriate personal protective equipment to healthcare workers, waste handlers, and incinerator operators and enforce compliance to existing regulation and guidelines on healthcare waste management to safeguard the environment and human health.

High Dimension Multivariate Data Analysis for Small Group Samples of Chemical Volatile Profiles of African Nightshade Species

Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.

Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN)

Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.

Advancing Type II Diabetes Predictions with a Hybrid LSTM-XGBoost Approach

In this paper, we explore the ability of a hybrid model integrating Long Short-Term Memory (LSTM) networks and eXtreme Gradient Boosting (XGBoost) to enhance the prediction accuracy of Type II Diabetes Mellitus, which is caused by a combination of genetic, behavioral, and environmental factors. Utilizing comprehensive datasets from the Women in Data Science (WiDS) Datathon for the years 2020 and 2021, which provide a wide range of patient information required for reliable prediction. The research employs a novel approach by combining LSTM’s ability to analyze sequential data with XGBoost’s strength in handling structured datasets. To prepare this data for analysis, the methodology includes preparing it and implementing the hybrid model. The LSTM model, which excels at processing sequential data, detects temporal patterns and trends in patient history, while XGBoost, known for its classification effectiveness, converts these patterns into predictive insights. Our results demonstrate that the LSTM-XGBoost model can operate effectively with a prediction accuracy achieving 0.99. This study not only shows the usefulness of the hybrid LSTM-XGBoost model in predicting diabetes but it also provides the path for future research. This progress in machine learning applications represents a significant step forward in healthcare, with the potential to alter the treatment of chronic diseases such as diabetes and lead to better patient outcomes.

Kinetics and Process Studies of the Potential for Transformation of Biogas to Biomethane and Liquefaction using Cryogenic Liquid for Domestic Applications

The present work dealt with the generation, purifying and liquefaction of biomethane to improve energy density using local materials for domestic applications. Cow dung was sourced at JKUAT dairy farm and experiments were conducted at JKUAT Bioenergy laboratory using biogas generated in laboratory scale 1 m3 bioreactors. Experiments were done in triplicates and repeated under different conditions to get the optimal conditions. The results showed that enhanced cow dung substrate displayed an improved fermentation process with increased biogas yields. Purified biogas optimized methane content from 56% ± 0.18% for raw biogas to 95% ± 0.98% for biomethane which was ideal for liquefaction.

Experimental Investigations of the Effects of Secondary Air Injection on Gaseous Emission Profiles (NOx, NO, NO2, CO) and Hydrocarbons (CxHx) in Cookstoves Using Charcoal from Eucalyptus glandis

Use of biomass in domestic cookstoves leads to the release of oxides of nitrogen (NOx), nitric oxide (NO), nitrogen dioxide (NO2), carbon monoxide (CO) and hydrocarbons CxHx that can be detrimental to health of the public and the environment. Attainment of complete combustion is the best strategy for mitigating the release of these emissions. This study sought to experimentally determine the effects of secondary air injection on the emission profiles of NOx (NO & NO2), CO and CxHx in a charcoal operated cookstove. Charcoal from Eucalyptus glandis was bought from Kakuzi PLC. Composites from three batches were analyzed for chemical composition and the stoichiometric air equivalent. Proximate analysis data show that the charcoal composed 58.72% ± 3.3% C, 15.95% ± 1.2% Volatile Matter, 4.69% ± 0.55% Moisture, 20.7% ± 0.8% Ash, High heat value (HHV) of 30.5 ± 1.1 and 29.3 ± 1.3 Low heat value (LHV) (MJ/kg) with a chemical formula of C18H2O and a stoichiometric air requirement of 5.28 ± 0.6 with a fuel flow rate of 1 kg fuel/hr. Emission profiles for CO and CxHx reduced significantly by 70% and 80% respectively with secondary air injection whereas those of NOx increased by between 15% and 20% for NO2 and NO. The study reveals that secondary air injection has potential to mitigate on emission release, however other measures are required to mitigate NOx emissions.

Economic Assessment of Selected Regulatory Ecosystem Services (RES) in the Elgeyo and Nyambene Watersheds Ecosystems in Kenya

Evidence of increased valuation of ecosystem services (ES) globally is significant. However, most of these studies focus on marketed subsets of ES at national and international levels. Ecosystems differ in spatial scale, biophysical and ecological structure, and functionality. This requires conducting studies at the local level to understand how, for example, the watershed ecosystem contributes to humanity locally and nationally. This study focuses on selected regulatory ecosystem services (RES) in Kenya’s catchment area ecosystems (Elgeyo and Nyambene). Field-based sampling and Landsat imagery with secondary information were used to generate biophysical and ecological data. The study used market price-based, cost-based, and unit transfer methods for RES valuation. The study estimates the total value of the six selected regulatory ecosystem services (RES) at KES 41.4 billion (US$386.7 million) and KES 14.73 billion (US$137.71 million) for Elgeyo and Nyambene, respectively. This equates to KES 1.64 million (US$15,331.19) and KES 2.72 million (US$25,375) per hectare per year. Extrapolating the study estimates to the national level, the country’s regulatory ecosystem services would range from US$18.4 billion to US$30.45 billion annually. This equates to between 16.7% and 27.7% of Kenya’s GDP in 2021, underscoring the importance of watersheds to the national economy.

Mixed-Effects Parametric Proportional Hazard Model with Generalized Log-Logistic Baseline Distribution

Clustered survival data are widely observed in a variety of setting. Most survival models incorporate clustering and grouping of data accounting for between-cluster variability that creates correlation in order to prevent underestimate of the standard errors of the parameter estimators but do not include random effects. In this study, we developed a mixed-effect parametric proportional hazard (MEPPH) model with a generalized log-logistic distribution baseline. The parameters of the model were estimated by the application of the maximum likelihood estimation technique with an iterative optimization procedure (quasi-Newton Raphson). The developed MEPPH model’s performance was evaluated using Monte Carlo simulation. The Leukemia dataset with right-censored data was used to demonstrate the model’s applicability. The results revealed that all covariates, except age in PH models, were significant in all considered distributions. Age and Townsend score were significant when the GLL distribution was used in MEPPH, while sex, age and Townsend score were significant in MEPPH model when other distributions were used. Based on information criteria values, the Generalized Log-Logistic Mixed-Effects Parametric Proportional Hazard model (GLL-MEPPH) outperformed other models.

Mass Valuation of Unimproved Land Value Case Study: Nairobi County

The purpose of this study is to investigate mass valuation of unimproved land value using machine learning techniques. The study was conducted in Nairobi County. It is one of the 47 Kenyan Counties under the 2010 constitution. A total of 1440 geocoded data points containing the market selling price of vacant land in Nairobi were web scraped from major property listing websites. These data points were adopted as dependent variables given as unit price of vacant land per square meter. The Covariates used in this study were categorized into Accessibility, Environmental, Physical and Socio-Economic Factors. Due to multi-collinearity problem present in the covariates, PLS and PCA methods were adopted to transform the observed features using a set of vectors. These methods resulted in an uncorrelated set of components that were used in training machine learning algorithms. The dependent variable and uncorrelated components derived feature reduction methods were used as training data for training different machine learning regression models namely;Random forest, support vector regression and extreme gradient boosting regression (XGboost regression). PLS performed better than PCA because the former maximizes the covariance between dependent and independent variables while the latter maximizes variance between the independent variables only and ignores the relationship between predictors and response. The first 9 components were identified as significant both by PLS and PCA methods. The spatial distribution of vacant land value within Nairobi County was consistent for all the three machine learning models. It was also noted that the land value pattern was higher in the central business district and the pattern spread northwards and westwards relative to the CBD. A relative low vacant land value pattern was observed on the eastern side of the county and also at the extreme periphery of Nairobi County boundary. From the accuracy metrics of R-squared and MAPE, Random Forest Regression model performed better than XGBoost and SVR models. This confirms the capability of random forest model to predict valid estimates of vacant land value for purposes of property taxation in Nairobi County.

Predicting the Volume Fraction of Martensite in Welded Mild Steel Joint Reinforced with Titanium Alloy Powder

Welded mild steel is used in different applications in engineering. To strengthen the joint, the weld can be reinforced by adding titanium alloy powder to the joint. This results in the formation of incomplete martensite in a welded joint. The incomplete martensite affects mechanical properties. Therefore, this study aims to predict the volume fraction of martensite in reinforced butt welded joints to understand complex phenomena during microstructure formation. To do so, a combination of the finite element method to predict temperature history, and the Koistinen and Marburger equation, were used to predict the volume fraction of martensite. The martensite start temperature was calculated using chemical elements obtained from the dilution-based mixture rule. The curve shape of martensite evolution was observed to be relatively linear due to the small quantity of martensite volume fraction. The simulated result correlated with experimental work documented in the literature. The model can be used in other powder addition techniques where the martensite can be observed in the final microstructure.

A Hybrid Spatial Dependence Model Based on Radial Basis Function Neural Networks (RBFNN) and Random Forest (RF)

The majority of spatial data reveal some degree of spatial dependence. The term “spatial dependence” refers to the tendency for phenomena to be more similar when they occur close together than when they occur far apart in space. This property is ignored in machine learning (ML) for spatial domains of application. Most classical machine learning algorithms are generally inappropriate unless modified in some way to account for it. In this study, we proposed an approach that aimed to improve a ML model to detect the dependence without incorporating any spatial features in the learning process. To detect this dependence while also improving performance, a hybrid model was used based on two representative algorithms. In addition, cross-validation method was used to make the model stable. Furthermore, global moran’s I and local moran were used to capture the spatial dependence in the residuals. The results show that the HM has significant with a R2 of 99.91% performance compared to RBFNN and RF that have 74.22% and 82.26% as R2 respectively. With lower errors, the HM was able to achieve an average test error of 0.033% and a positive global moran’s of 0.12. We concluded that as the R2 value increases, the models become weaker in terms of capturing the dependence.

Modelling COVID-19 Cumulative Number of Cases in Kenya Using a Negative Binomial INAR (1) Model

In this paper, a Negative Binomial (NB) Integer-valued Autoregressive model of order 1, INAR (1), is used to model and forecast the cumulative number of confirmed COVID-19 infected cases in Kenya independently for the three waves starting from 14th March 2020 to 1st February 2021. The first wave was experienced from 14th March 2020 to 15th September 2020, the second wave from around 15th September 2020 to 1st February 2021 and the third wave was experienced from 1st February 2021 to 3rd June 2021. 5, 10, and 15-day-ahead forecasts are obtained for these three waves and the performance of the NB-INAR (1) model analysed.

Genetic Variation among Sorghum (<i>Sorghum bicolor</i>L. Moench) Landraces from Eritrea under Post-Flowering Drought Stress Conditions

Sorghum (Sorghum bicolour (L.) Moench) grown under rain-fed conditions is usually affected by drought stress at different stages, resulting in reduced yield. The assessment of variation in morpho-physiological traits contributing towards drought tolerance at these stages is of vital importance. This study was conducted using a split plot design with three replications to evaluate 25 sorghum accessions at post flowering stage under well watered and drought stress conditions at Hamelmalo Agricultural College. The data of 14 different morpho-physiological traits were subjected to analysis of variance, estimation of genetic variability and heritability and principal component analysis. We analyzed variance for seedling vigor, number of leaves, leaf area, stay-green, peduncle exsertion, panicle length and width, plant height, days to flowering and maturity, grain yield, biomass and harvest index under drought stress and irrigated conditions. The results showed that genotypic differences were significant at P 1 explaining 74.6% of the total variation with grain yield, biomass, stay-green, leaf area, peduncle exsertion and days to flowering and maturity being the most important characters in PC1 and PC2. This research demonstrated high diversity for the characters studied. Moreover, the result showed that drought stress reduced the yield of some genotypes, though others were tolerant to drought. Accessions EG 885, EG 469, EG 481, EG 849, Hamelmalo, EG 836 and EG 711 were identified as superior for post-flowering drought tolerance and could be used by breeders in improvement programs.

Consistency of the <i>φ</i>-Divergence Based Change Point Estimator

This paper utilizes a change-point estimator based on the φ-divergence. Since we seek a near perfect translation to reality, then locations of parameter change within a finite set of data have to be accounted for since the assumption of stationary model is too restrictive especially for long time series. The estimator is shown to be consistent through asymptotic theory and finally proven through simulations. The estimator is applied to the generalized Pareto distribution to estimate changes in the scale and shape parameters.

Fusion of Model-Based and Data Driven Based Fault Diagnostic Methods for Railway Vehicle Suspension

Transportation of freight and passengers by train is one of the oldest types of transport, and has now taken root in most of the developing countries especially in Africa. Recently, with the advent and development of high-speed trains, continuous monitoring of the railway vehicle suspension is of significant importance. For this reason, railway vehicles should be monitored continuously to avoid catastrophic events, ensure comfort, safety, and also improved performance while reducing life cycle costs. The suspension system is a very important part of the railway vehicle which supports the car-body and the bogie, isolates the forces generated by the track unevenness at the wheels and also controls the attitude of the car-body with respect to the track surface for ride comfort. Its reliability is directly related to the vehicle safety. The railway vehicle suspension often develops faults;worn springs and dampers in the primary and secondary suspension. To avoid a complete system failure, early detection of fault in the suspension of trains is of high importance. The main contribution of the research work is the prediction of faulty regimes of a railway vehicle suspension based on a hybrid model. The hybrid model framework is in four folds;first, modeling of vehicle suspension system to generate vertical acceleration of the railway vehicle, parameter estimation or identification was performed to obtain the nominal parameter values of the vehicle suspension system based on the measured data in the second fold, furthermore, a supervised machine learning model was built to predict faulty and healthy state of the suspension system components (damage scenarios) based on support vector machine (SVM) and lastly, the development of a new SVM model with the damage scenarios to predict faults on the test data. The level of degradation at which the spring and damper becomes faulty for both primary and secondary suspension system was determined. The spring and damper becomes faulty when the nominal values degrade by 50% and 40% and 30% and 40% for the secondary and primary suspension system respectively. The proposed model was able to predict faulty components with an accuracy of 0.844 for the primary and secondary suspension system.

Location Based Services Acceptance and Adoption Evaluation: An Investigation on Mobile Phone Users’ Response to Awareness and Utilization of LBS Technology in Nairobi Kenya

Location Based Navigation System (LBNS) is a specific Location Based Service (LBS) purely for navigational purpose. These systems resolve position of a user by using GNSS/GPS positioning technologies, to which supplementary information on goods and services are tagged. The navigation services have become popular and can be installed on mobile phones to provide route information, location of points of interest and user’s current location. LBS has continued to face challenges which include “communication” process towards user reference. Location Based Service System conveys suitable information through a mobile device for effective decision making and reaction within a given time span. This research was geared at understanding the state of LBS technology acceptance and adoption by users in Nairobi Kenya. To do this a quantitative study was carried out through a questionnaire, to investigate mobile phone users’ response on awareness and use of LBS technology. Testing the growth of this technology in this region compared to predictions in previous studies using Technology Acceptance Model (TAM), it is evident that many users may be aware of GPS functionality in mobile phones but are certainly yet to fully embrace the technology as they rarely use it. This points to some underlying challenges towards this technology within this part of the World, thereby recommending for deliberate monitoring and evaluation of LBS technology for sustenance growth based on user satisfaction and acceptance for improved usability.

Effects of Sand Quality on Compressive Strength of Concrete: A Case of Nairobi County and Its Environs, Kenya

Failure of concrete structures leading to collapse of buildings has initiated various researches on the quality of construction materials. Collapse of buildings resulting to injuries, loss of lives and investments has been largely attributed to use of poor quality concrete ingredients. Information on the effect of silt and clay content and organic impurities present in building sand being supplied in Nairobi County and its environs as well as their effect to the compressive strength of concrete was lacking. The objective of this research was to establish level of silt, clay and organic impurities present in building sand and its effect on compressive strength of concrete. This paper presents the findings on the quality of building sand as sourced from eight supply points in Nairobi County and its environs and the effects of these sand impurities to the compressive strength of concrete. 27 sand samples were tested for silt and clay contents and organic impurities in accordance with BS 882 and ASTM C40 respectively after which 13 sand samples with varying level of impurities were selected for casting of concrete cubes. 150 mm × 150 mm × 150 mm concrete cubes were cast using concrete mix of 1:1.5:3:0.57 (cement:sand:coarse aggregates:water) and were tested for compressive strength at the age of 7, 14 and 28 days. The investigation used cement, coarse aggregates (crushed stones) and water of similar characteristics while sand used had varying levels of impurities and particle shapes and texture. The results of the investigations showed that 86.2% of the sand samples tested exceeded the allowable limit of silt and clay content while 77% exceeded the organic content limit. The level of silt and clay content ranged from 42% to 3.3% for while organic impurities ranged from 0.029 to 0.738 photometric ohms for the unwashed sand samples. With regard to compressive strength, 38% of the concrete cubes made from sand with varying sand impurities failed to meet the design strength of 25 Mpa at the age of 28 days. A combined regression equation of with R2 = 0.444 was generated predicting compressive strength varying levels of silt and clay impurities (SCI), and organic impurities (ORG) in sand. This implies that 44% of concrete’s compressive strength is contributed by combination of silt and clay content and organic impurities in sand. Other factors such as particle shapes, texture, workability and mode of sand formation also play a key role in determination of concrete strength. It is concluded that sand found in Nairobi County and its environs contain silt and clay content and organic impurities that exceed the allowable limits and these impurities result in significant reduction in concrete’s compressive strength. It is recommended that the concrete design mix should always consider the strength reduction due to presence of these impurities to ensure that target strength of the resultant concrete is achieved. Formulation of policies governing monitoring of quality of building sand in Kenya and other developed countries is recommended.

Effect of Exhaust Gas Recirculation on Performance and Emission Characteristics of a Diesel-Piloted Biogas Engine

In this research, a Direct Injection Compression Ignition (DICI) engine was modified into a dual-fuel engine that used biogas as the primary fuel and diesel as pilot fuel, with the focus on reduction of harmful exhaust emissions while maintaining high thermal efficiency. The effect of exhaust gas recirculation (EGR) on engine performance and emission characteristics was studied. The EGR system was developed and tested with different EGR percentages, i.e. 0%, 10%, 20% and 30%. The effect of EGR on exhaust gas temperature and performance parameters like brake specific fuel consumption, brake power and brake thermal efficiency was studied. The performance and emission characteristics of the modified engine were compared with those of the conventional diesel engine. The results showed that EGR led to a decrease in specific fuel consumption and an increase in brake thermal efficiency. With increase in percent (%) of EGR, the percentage increase in brake thermal efficiency was up to 10.3% at quarter load and up to 14.5% at full load for single fuel operation while for dual-fuel operation an increase up to 9.5% at quarter load and up to 11.2% at full load was observed. The results also showed that EGR caused a decrease in exhaust gas temperature;hence it’s potential to reduce NOX emission. However, emissions of HC and CO increased slightly with EGR.

Molecular Markers for Genetic Diversity Studies in African Leafy Vegetables

African leafy vegetables are becoming important crops in tackling nutrition and food security in many parts of sub-Saharan Africa, since they provide important micronutrients and vitamins, and help resource-poor farm families bridge lean periods of food shortage. Genetic diversity studies are essential for crop improvement programmes as well as germplasm conservation efforts, and research on genetic diversity of these vegetables using molecular markers has been increasing over time. Diversity studies have evolved from the use of morphological and biochemical markers to molecular markers. Molecular markers provide valuable data, since they detect mostly selectively neutral variations at the DNA level. They are well established and their strengths and limitations have been described. New marker types are being developed from a combination of the strengths of the basic techniques to improve sensitivity, reproducibility, polymorphic information content, speed and cost. This review discusses the principles of some of the established molecular markers and their application to genetic diversity studies of African leafy vegetables with a main focus on the most common Solanum, Amaranthus, Cleome and Vigna species.