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.

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.

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.

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.

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.

Diversity, Abundance, Richness, and Birds of Conservation Interest in Nyando Sugar Belt, Muhoroni Sub-County, Lake Victoria Basin, Western Kenya

Thus far, no expedition has comprehensively surveyed the composition of bird species in the dilapidated habitats of Nyando sugar belt, Western Kenya. This has made it difficult unearthing equilibrium between agricultural growth and bird species conservation. In response, we conducted bird assessment by stratifying the expedition area into farmlands and shrub-land. We then sampled birds by the standard point count method and opportunistic counts within a 30 m radius parcel of land. We exhaustively observed 1450 birds of 122 species. The farmland recorded a density of 2.065 ± 1.11 birds per hectare whereas the shrub-land had a density of 1.644 ± 0.70 birds per hectare. Nyando sugar belt was a diverse community with a Shannon diversity index value (H’) of 3.225 regardless of the birds being constrained in certain habitats. The magnitude of the disparity in true diversity indicated that the farmland was 4 times more diverse than the shrub-land. The facts promoted by this research validate the incorporation of bird conservation in the farmland and formulation of avian conservation strategies.

A Retrospective Analysis of Wastewater Confirms Dominant Circulation of SARS-CoV-2 Delta Variant in Nairobi, Kenya, between April 2021 and August 2021

Wastewater surveillance has been applied in various parts of the world to monitor the introduction and transmissions of SARS-CoV-2 variants in a population. The knowledge of SARS-CoV-2 variants circulating in a population is critical to COVID-19 management and timing of the application of public health countermeasures. Contrary to the routine clinical surveillance of SARS-CoV-2 where cases from asymptomatic patients are often underreported, wastewater surveillance offers an unbiased tool for monitoring the extent of SARS-CoV-2 transmissions in a community. The present study aimed to characterize SARS-CoV-2 variants that circulated in Nairobi County, Kenya, between April 2021 and August 2021 utilizing wastewater samples. Viral RNA was extracted from wastewater samples, followed by SARS-CoV-2 screening by real-time RT-qPCR before targeted sequencing of the Spike gene. Forty samples were analyzed, of which 50% (n = 20) tested positive for SARS-CoV-2 by real-time RT-qPCR. Of these, 45% (n = 9) were successfully amplified by RT-PCR and sequenced. The majority (78%, 7/9) of the viruses belonged to the Delta (B.1.617.2) lineage of SARS-CoV-2, while a minority (22%) belonged to the Alpha (B.1.1.7) and Alpha-Delta lineages. Phylogenetic analysis of the SARS-COV-2 delta lineage strains revealed scattered clustering of the Kenyan viruses among the global strains included in the analysis, suggesting different introductory routes into the country. On the whole, our results confirm previous clinical findings that SARS-CoV-2 variants belonging to the Alpha (B.1.1.7) and Delta (B.1.617.2) lineages circulated in Nairobi County, Kenya during the study period, with the latter predominating. This is the first study to describe the diversity of SARS-CoV-2 variants circulating in Kenya, through wastewater analysis.

Insect Pollinator Diversity and Their Influence on Yield and Quality of <i>Capsicum annuum</i>Linné(Solanaceae), Machakos, Kenya

Capsicum annuum (L.) yields have remained low due to poor quality fruits in developing countries such as Kenya, which could be attributed to inadequate insect pollination among other factors. The present study was conducted after the short and long rain seasons in 2018 to assess the diversity and abundance of insect pollinators of C. annuum and to determine their influence on yield. The experiment was laid out in a Randomized Complete Block Design with bagged and un-bagged pollination treatments. Insect pollinator assessment was conducted between 07:00 hours to 21:00 hours for one month during each season. Yield and quality were compared between the pollination treatments. During the entire study 13 insect pollinator species (3 orders, 7 families) were recorded onC. annuum flowers. Apis mellifera was the most abundant insect pollinator during the two seasons. The highest species diversity was recorded after the long rain season (H' = 1.85). With respect to time, species richness was the highest in the afternoon after the short rains and the highest in the morning after the long rains. The average yield parameters from both seasons showed that open pollination treatments had increased fruit weight (66.5%), seed weight (54.5%) fruit length (28%) and fruit diameter (30%) when compared to treatments bagged throughout. Findings from this study have shown that insect pollinator diversity varies seasonally and significantly influences the yield and quality of C. annuum. This calls for the need to practice sustainable agriculture so as to conserve insect pollinators of C. annuum for improved vegetable production in semiarid lands of Kenya.

Occurrence of Highly Resistant Microorganisms in Ruai Wastewater Treatment Plant and Dandora Dumpsite in Nairobi County, Kenya

Wastewater treatment plants and solid dump sites act as potential reservoirs of highly resistant bacterial strains. This study presents information on the patterns of antimicrobial resistance among pathogenic bacteria in the sewage treatment plants and solid dump site in Nairobi County, Kenya. We employed standard microbiological methods to isolate and identify bacteria from these selected sites. Members of Escherichia, Klebsiella, Serratia, Shigella, Pseudomonas, Citrobacter spp, Enterobacter, Salmonella, Staphylococcus, Bacillus, Yersinia, Vibro cholera, Providencia, Morganella and Proteus spp were isolated. Escherichia and Klebsiella recorded the highest occurrence at 13.9% and 10.3% in wastewater. Shigella, Serratia Citrobacterfreundii, Pseudomonas, Staphylococcus, Escherichia and Klebsiella recorded the highest level of occurrence at 15.1%, 14.1%, 12%, 11%, 10.4%, 10.3%, and 8.3% in dumpsite respectively. Drug sensitivity using the Disc diffusion method showed that Ampicillin, Ceftazidime, Cefotaxime, Amoxicillin, and Cefepime had the highest levels of inactivity at (68.5%, 89.5%), (64.2%, 87%), (54.5%, 80.2%), (52%, 74%) and (49.1%, 72.4%) respectively while Levofloxacin and Gentamicin showed the smallest level of antibiotic resistance at (4.8%, 4.7%) and (4.2%, 2.6%) in both sites. Members of Escherichia, Klebsiella, Proteus vulgaris, Salmonella, Pseudomonas, Shigella and Serratia showed the highest level of ESBLs production at 28.9%, 18.4%, 15.8%, 10.5%, 7.9%, 7.9%, 7.9% and 7.9% in the wastewater whereas Shigella, Escherichia, Serratia, Pseudomonas, Klebsiella, and Proteus vulgaris showed the highest level ESBLs production at 45.5%, 10.9%, 9.1%, 9.1%, 7.3% and 7.3% from the dump site. All the antibiotics tested except for Gentamicin, Chloramphenicol and Ciprofloxacin differed significantly in terms of resistance levels (p < 0.05). The study concluded that, wastewater treatment plant and dump sites bear heavy burdens of potentially virulent resistant bacterial strains which may constitute major public health hazards to scavengers and those living near these sites. There is need therefore to educate people on the use of appropriate protective materials and the settlement patterns of individuals and communities near these sites.

Experimental Investigation of Lime Treated Palm Kernel Shell and Sugarcane Bagasse Ash as Partial Replacement of Coarse Aggregate and Cement Respectively in Concrete

This experimental research is focused on the effect of concrete made by incorporating lime treated Palm Kernel Shell (PKS) & Sugarcane Bagasse Ash (SCBA) as partial replacements of coarse aggregates and Ordinary Portland Cement (OPC) respectively. An experimental analysis for concrete grade 30 with a mix design ratio of 1:1.97:3.71 of cement:fine aggregates:coarse aggregates with a constant water to cement ratio of 0.5, was used. Physical tests such as workability on fresh concrete and water absorption on hardened concrete of each batch were carried out. Mechanical tests like compressive strength and split tensile strength were carried out on hardened concrete cubes (100 mm × 100 mm × 100 mm) and cylinders (100 mm × 200 mm) at 7 and 28 days. The experimental results obtained in this study indicate the possibility of using up 15% of lime treated PKS and 10% of SCBA for production of structural concrete.

Location Based Navigation Service Technology: Development of an Optimization Algorithm for Multivariate Contextual Cartographic Content in Location Based Navigation Service

User response or reaction to navigation applications is influenced by relevance in geographic information, in terms of cartographic context and content delivered within a definite time, providing a direct impact to outcome or consequence based on decision making and hence user reaction. Location Based Navigation Services (LBNS) have continuously advanced in cartographic visualization, making maps interpretation easy and ubiquitous to any user, as compared to pre-historic times when maps were a preserve of a few. Despite rapid growth in LBNS, there exist challenges that may be characterized as technical and non-technical challenges, among them being process of conveying geospatial information to user. LBNS system deliver appropriate information to a user through smartphone (mobile device) for effective decision making and response within a given time span. This research focuses on optimization of cartographic content for contextual information in LBNS to users, based on prevailing circumstances of various components that constitute it. The research looks into Geographic Information Retrieval (GIR), as a technical challenge centered on a non-technical issue of social being of user satisfaction, leading to decision making in LBNS, hence response and outcome. Though advanced technologically, current LBNS on information sourcing depends on user manual web pages navigation and maneuver, this can be painstaking and time consuming that it may cause unnecessary delay in information delivery, resulting to delayed information response time (DIRT). This in turn may lead to unappropriate decision making with erroneous reaction or response being taken, resulting in loss of opportunity, resources, time and even life. Optimization in LBNS is achieved by a mathematical relationship developed between user status, mobile device variables against cartographic content. The relationship is in turn applied in LBNS android application to fulfill optimization solution for user consumption.

Molecular Identification of Co-Existence of Carbapenemase and Extended-Spectrum <i>β</i>-Lactamase Genes in <i>Klebsiella pneumoniae</i>Clinical Isolates, and Their Phylogenetic Patterns in Kenya

The increasing incidence of multidrug-resistant Klebsiella pneumoniae strains has become a serious global healthcare problem. Additionally, the carriage of both extended-spectrum ß-lactamase and carbapenemase genes on plasmid and genomic DNA in K. pneumoniae clinical isolates has not been documented in Kenya. This study aimed to assess the presence of extended spectrum β-lactamase (ESBL) and carbapenemase genes on genomic and plasmid DNA in K. pneumoniae, and classify these super-bug clinical isolates based on their phylogenetic patterns. The identification of Klebsiella-like clinical isolates (n = 20) collected from Kenyatta National Hospital in Nairobi was performed using API 20E Kit. Screening and confirmation for ESBL and carbapenemase phenotypes were conducted using Kirby-Bauer disk diffusion susceptibility test protocol. Conventional PCR technique was used to characterize ESBL and carbapenemase resistant genes on both genomic and plasmid DNA. Subsequently, 16S rRNA gene amplification and sequencing were performed. The 16S rRNA gene contiguous sequences of the bacterial isolates were analyzed using the ChromasPro. The gene sequence was compared with the sequences in GenBank database, using the BLAST program of NCBI to obtain the nearest phylogenetic neighbours from the databases. Then, the sequences of MDR K. pneumoniae and its relatives were aligned using ClustalW. The evolutionary history was inferred by using the maximum likelihood algorithm in MEGA MX. The phenotypic data of antibiotic susceptibility testing revealed that 2/20 (10%) clinical isolates were resistant both to imipenem and meropenem and producers of carbapenemase. These isolates were carbapenemase producers but not extended β-lactamases. However, 3/20 (15%) isolates that co-harboured blaNDM-1, blaIMP, blaTEM, and bla-OXA were identified during genotypic analysis. The positive control used separately yielded the expected band sizes for blaIMP (275 bp), blaOXA-48 (438 bp), and BlaKPC (798). The phylogenetic analysis showed the dual ESBL and carbapenemase producing Klebsiella pneumoniae could be classified as K. pneumoniae strain DSM 30104 and K. pneumonia subsp. pneumoniae strain GMH1080. This study confirmed the co-existence of ESBL and carbapenemase genes in Klebsiella pneumoniae on both bacterial genomic and Plasmid DNA, and demonstrated that the isolates are evolutionarily distinct. These findings raise a concern about the genotypic diversity of antibiotic resistance genes in bacterial isolates and their location. We, therefore, recommend an alternative management approach to combat these MDR bacterial isolates as well as frequent molecular surveillance programs to support antimicrobial stewardship.

Nanoencapsulation of Thymol and Eugenol with Chitosan Nanoparticles and the Effect against <i>Ralstonia solanacearum</i>

Essential oils (EOs) are natural bioactive compounds with antibacterial activity against a variety of microorganisms including phytopathogens. The use of EOs and their components as viable therapeutic antibacterials is however greatly compromised by their volatile nature, hydrophobicity and instability when exposed to environmental and physiological factors. Encapsulation of these compounds in an appropriate carrier system can alleviate these challenges. This study therefore aimed at developing, characterizing and evaluating the efficacy of antibacterial potential of thymol and eugenol loaded chitosan nanoparticles (TCNPs and ECNPs) against Ralstonia solanacearum, the bacterial wilt-causing pathogen in potatoes. Synthesis of TCNP and ECNP was achieved via ionic gelation method and the prepared nanoparticles characterized by their particle size distributions, encapsulation efficiency, loading capacity (LC) and in-vitro release characteristics. Antibacterial activities of the nanoparticles were investigated using agar dilution and colony counting methods and their minimum inhibitory concentration (MIC) determined by 96-well broth micro-dilution method. Scanning electron microscope images of TCNPs and ECNPs showed that the nanoparticles were spherical in shape and were well separated with an average particle size of 590 nm and 555 nm respectively. The average size of chitosan nanoparticles alone was however 375 nm. The encapsulation efficiency was 72.9% for TCNP (with 48.3% LC) and 71.7% for ECNP (with 49.5% LC). The release of thymol and eugenol from the microcapsules was found to be pH dependent with the highest release at pH 1.5. The growth inhibition of R. solanacearum was 92% and 94% for TCNP and ECNPs respectively. The MIC of thymol and eugenol before encapsulation was 175 μg/ml and 275 μg/ml respectively, but this reduced significantly to 22.5 μg/ml and 45 μg/ml after encapsulation. Thus, encapsulation of thymol and eugenol in chitosan nanoparticles has shown promising potential as a bactericide alternative for R. solanacearum and could be useful in managing the soil borne phytopathogen.

Molecular Investigation of Genetic Signatures of Selection in <i>Plasmodium falciparum</i>Actin-Binding Protein Coronin, Cysteine Desulfurase, and Plasmepsin 2 Gene in Mbita Field Isolates, Western Kenya

Background: Plasmodium falciparum (Pf) resistance to antimalarial drugs is a major impediment to malaria control. The Pf.Kelch 13 (PfK13) gene has been largely reported to be associated with artemisinin resistance. However, recent studies have shown artemisinin resistance without Kech13 mutations suggesting the implication of others genes in artemisinin resistance. In this current study, we focused on mutations in Pf.actin-binding protein coronin, Pf.cysteine desulfurase and Pf.plasmepsin 2 gene, three putative candidates recently were reported to be involved in artemisinin, lumefantrine and piperaquine resistance respectively. Method: Archived blood samples previously collected from asymptomatic school children from December 2016 to October 2018 were used in this study. Genomic DNA was extracted using ISOLATE II Genomic DNA kit. After PCR amplification, amplicons were purified and sequenced by capillary sequencing. Reads were analyzed for the identification of point mutations previously reported to be involved in drug selection. Results: Mutations R100K, and G50E involved in reduced artemisinin susceptibility were detected in Pfcoronin. From 2016/17 to 2018 the allele 100k increased frequency (11.2%);while 50E was only observed in 2018 time point reaching 11.1%. Lumefantrine selection marker K65, in codon (K65Q) was observed at 14.2% in Pfcysteine desulfurase, and the mutant’ allele 65Q gradually increased frequency from 28.5% in 2016/17 to 57.1% in 2018. Pf.plasmepsin 2 was the less polymorphic gene. Several other polymorphism codons and single nucleotide variants were detected. Conclusion: The findings indicate the presence of mutations associated with reduced artemisinin susceptibility and lumefantrine selection marker. Therefore, the results call for continuous monitoring of molecular makers in Mbita parasites.

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.