Journal of Agriculture, Science and Technology

Kenya Medical Training College lecturer perception on adequacy of teaching and learning resources in the advent of COVID-19.

2023-10-30T10:36:00+00:00

Strategies set up to achieve sustainable development goals 3 and 4 in Kenya are via expansion of the health workforce by training healthcare workers. However, challenges such as lack of adequate human resources, physical facilities, teaching aids, and finances existed. In the advent of the COVID-19 pandemic, the above challenges were made worse by the health requirements to mitigate against the pandemic. For instance, the need for more human resources, expansion of physical facilities, adoption of relevant eLearning, and the much-needed financial resources. In order to conduct this study, the researcher adopted a pragmatism theoretical framework. The study investigated lecturers' perceptions of the adequacy of human resources, physical facilities, and teaching aids, in the Department of Medical Laboratory Sciences at Kenya Medical Training College in the advent of COVID-19. This was a descriptive cross-sectional study using a triangulation mixed method design. The study setting was the eastern region, selected out of the eight regions of Kenya through simple random sampling. The four campuses with a Medical Laboratory Science department within the eastern region were all selected through census sampling. The study sample was all the 18 lecturers selected through census sampling. The data collection instruments were interview guides and self-administered closed and open-ended questionnaires. The data was analyzed using Statistical Package for the Social Sciences for the descriptive statistics (measures of central tendency) and then presented in frequency tables. The response rate from the four campuses with the Medical Laboratory Science department was 100% (18 lecturers). The majority of the lecturers had a mean age of 38 years. The majority of lecturers were not satisfied with the adequacy of human resources with a mean of 1.5833 and (std. deviation=.51493). The majority of lecturers perceived the state of physical facilities to be in a poor state with an average mean of 1.6. and (std. deviation =0.418). The satisfaction level of the majority of lecturers on teaching aids was a mean of 3.01 (std. deviation=1.459), representing neutral to very inadequate. The majority of lecturers perceived that there were insufficient human resources, physical facilities, and teaching aids in the Department of Medical Laboratory Sciences at Kenya Medical Training College. Kenya Medical Training College board of directors should provide and avail adequate teaching and learning resources notably human resources, physical facilities, and teaching aids to the Department of Medical Laboratory Sciences.

Biological treatment of agro-processing industrial effluents from tannery, coffee and dairy plants using green algae (Chlorella Ssp.) cultured in a photo bioreactor

2023-10-30T10:49:28+00:00

Due to increased environmental pollution as a result of high emission rates from agro-processing industries, the effluents must be cleaned up before being released into the environment. This study outlines the use of green algae for nutrient removal from agro-processing effluents discharged from three agro-industries (namely coffee, dairy, and tannery) in Kenya and how they can be used for the propagation of microalgae for biofuel production. Green algae were grown inside a photobioreactor containing the three agro-industrial effluents as nutrient media for 21 days. Thereafter, the algae were harvested and evaluated for biofuel production. The effectiveness of green algae (chlorella ssp) to extract the cations from the various agro-processing effluents was used to gauge how well they performed. Additionally, the algae growth rate, quantity of lipids, and biomass generated were used to evaluate the strains' effectiveness in producing biofuel. The results indicate that the highest maximum algae growth rate of 14.528 mg/mL occurred in the dairy effluent. The corresponding values for the coffee and tannery effluents were 13.016 mg/mL and 10.866 mg/mL, respectively. Biochemical analysis was done to establish the amount of biomass in the algae. The results showed that there was higher biomass productivity per day of 293.944, 124.849, and 91.997 μg/mL for the dairy, coffee, and tannery effluents, respectively. The contents of linolenic acid in the chlorella strain in the dairy, coffee, and tannery effluents were 13.21, 12.86, and 15.98%, respectively. The values obtained were slightly above the recommended lower limit value of 12% (EN 14214, 2004) for the production of quality biofuels. The results further show that high chemical oxygen demand removal (maximum of 47.7–67.8%) and total phosphorus removal (maximum of 95%) were achieved in all three effluents. Finally, the fatty acid methyl ester profiles produced indicated that the lipid content of the cultivated green algae was appropriate for the production of biofuel.

Comparison of machine learning methods for the prediction of type 2 diabetes in primary care setting using EHR data

2023-10-30T10:57:50+00:00

Diabetes remains a major global public health challenge, thus the need for better methods for managing diabetes. Machine learning could provide reliable solutions to the need for early detection and management of diabetes. This study conducted experiments to compare a number of selected machine learning approaches to determine their suitability for early detection of diabetes in the primary care setting. A retrospective study was conducted using EHR dataset of confirmed cases of diabetes collected during routine care at Nairobi Hospital. Institutional ethical approvals were obtained, and data were retrieved from the database through stratified sampling based on gender. Diagnoses were confirmed using the ICD-10 codes. Records with 5% or so of missing values were excluded from this analysis. Data were processed by correction of errors and replacement of missing values using measures of central tendency. The data were transformed through normalization using the decimal-scaling method. Data analysis was conducted using selected supervised and unsupervised learning algorithms. Model performances were validated using metrics for the evaluation of classification and clustering results, respectively. Random Forest had the highest accuracy (0.95) and error rate (0.05), while Gradient Boosting and Multilayer Perceptron (MLP) with 3 hidden layers obtained accuracy (0.94) and error rate (0.06), respectively. The process of selecting machine learning algorithms needs to explore both supervised and unsupervised learning techniques. In addition, an appropriate architectural design of an MLP could present astounding results for classification tasks in primary care settings.

Tuberculosis and HIV co-infection among miners at selected mining sites in Migori County, Kenya

2023-10-30T16:09:56+00:00

Tuberculosis (TB) disease is caused by acid-fast bacilli called Mycobacterium tuberculosis. The emergence of drug-resistant TB variants as well as co-infection with the Human Immunodeficiency Virus (HIV) have complicated treatment for TB. Kenya currently implements TB screening and HIV testing for all patients and their caretakers, regardless of their reason for visiting the hospitals. Drug resistance is tested in all HIV-positive TB suspects to guide the choice of drugs in cases of TB-HIV co-infection and minimise delays in treatment initiation. These services are, however, confined to the health facilities, and all those who do not visit the hospitals are missed. The objective of our study was to evaluate TB missed opportunities among the mining community of Osiri-Matanda gold mines in Nyatike sub-county, Migori County, Kenya. Community health outreaches were conducted in collaboration with the Ministry of Health. Demographic information was recorded for all clients consenting to the study. HIV testing was done using the HIV rapid testing kits. Sputum samples were tested in the local laboratory using Ziel-Neelsen (ZN) staining. All TB-positive outcomes were further tested using GeneXpert MTB/RIF to determine whether or not they were resistant to Rifampicin. A total of 297 participants were enrolled in the study. Among these, 49.5% were males and 50.5% were females. The youngest participant was 15 years old, while the oldest was 63 years old. A 15.5% TB prevalence was found after testing, with the age range of 35–44 having the highest prevalence at 39%. 71.7% of TB infections were male, while 28.3% were female. HIV-TB co-infections accounted for 37% of cases. None of the TB cases were RIF-resistant. This data shows that males are more at risk of TB and HIV infections than females. Collaborative health outreaches that include screening and testing for both TB and HIV could help in early detection and minimise missed opportunities.

Chromium (VI) adsorption from steel industry wastewater using biomass and agro-based adsorbents: A Review

2023-10-30T16:26:09+00:00

In recent years, increased steel production has resulted in an increased release of Cr (VI) effluent from electroplating processes. The current conventional technologies for Cr (VI) removal, for instance, chemical precipitation, ion exchange, membranes, and electrochemical technologies, have proven to be expensive and not eco-friendly. Adsorption using biomass or agro-based adsorbents has been demonstrated to be an eco-friendly method. In this study, the potential of several biomass and agro-based adsorbents for Cr (VI) removal from steel industry wastewater has been reviewed. The application of adsorption parameters, isotherms, and kinetic models has also been reviewed. The study review reveals that the maximum adsorption of Cr (VI) ions from wastewater was dependent on the pH, temperature, contact time, type of adsorbent material, and initial metal concentration. The literature review demonstrated that maximum Cr (VI) adsorption efficiency ranged from 90–100%, with optimum pH, contact time, and temperature ranging from 2–5, 30-180 min, and 25–30 °C, respectively. The equilibrium adsorption data was best described by the Langmuir and Freundlich isotherms and the pseudo-second-order kinetic model. The thermodynamic studies revealed that the adsorption process was spontaneous and endothermic. It has been found that biomass and agro-based adsorbents are potentially eco-friendly alternatives for Cr (VI) removal from industrial wastewater. However, several gaps have been identified to improve the Cr (VI) removal efficiency, spent adsorbent reuse and safe disposal, optimisation, and commercial application of agro-based adsorbents.

A model for enhancing the application of value management in construction projects in Kenya

2023-10-30T16:37:04+00:00

Value management (VM) has been widely used in the construction industry to facilitate projects for decades in many countries around the globe. The concept began in the US, and then other nations such as China, Japan, South Africa, and Nigeria followed and tested its effectiveness by assessing its impacts on costs, quality, and time. A VM application has a low cost and a high benefit process, and thus, if well-practiced and integrated into project management at the beginning of a project, it would lead to its successful completion. It also helps in improving the value of a project by ensuring low costs, high quality, and completion within the stipulated time frame. In Kenya, there remains a gap in that very little information has been documented about VM applications in construction projects. Therefore, this study seeks to evaluate and develop a model to enhance the application of value management to construction projects. The study sampled all registered key stakeholders in construction projects within Kenya. The stakeholders are comprised of engineers, architects, quantity surveyors, contractors, and project managers. A stratified sampling strategy was employed, and data analysis was conducted using Statistical Package for Social Sciences (SPSS) version 25. The analysis methods used in this research were both descriptive and inferential in nature. Data summaries in terms of frequencies, means, and standard deviations were obtained under descriptive analysis. The inferential analysis comprised Pearson's correlation and regression analysis, which determined the significant determinants of VM application and were used to establish a predictive model. The study showed that team dynamics, procurement policy, government regulations, and VM knowledge are significant predictors of the level of VM application. A model has been developed that can assist in enhancing the application of value management in construction projects in Kenya.

Environmental analysis of heavy metal pollution in Mtondia dumpsite, Kilifi County, Kenya.

2023-10-30T16:45:58+00:00

Dumping and open burning of waste with no regard to environmental implications is a common occurrence in Kenya. In several health surveys in communities close to dump sites, a wide range of human health problems, such as respiratory symptoms, allergies, irritation of the skin, and gastrointestinal problems, have been reported. The issue of heavy metal pollution brought about by the dump sites has become of concern in recent studies. Heavy metal contamination occurs when their concentrations exceed the recommended limits in the soil, water, or plant resources and have negative effects on humans or animals. The accumulation of heavy metals in agricultural soils is of great concern due to food safety issues, potential health risks, and detrimental effects on the soil’s ecosystems. The samples were obtained through random sampling and analysed in the laboratory using the Energy Dispersive X-ray Fluorescence (EDXRF) technique. The EDXRF technique software was used to calculate the qualitative and quantitative aspects of the heavy metals. SPSS version 22 was used to calculate Pearson's correlation coefficient. It was observed that the soil and plants sampled from the dumpsite recorded higher levels of heavy metals than the control samples. This showed that the soils at the dumpsite were more polluted with heavy metals as compared to those at the farms. The mean concentrations of the heavy metals in soil during the dry season followed the trend Zn>Mn>Pb>Cu>Ni>Co>Cr>As. In conclusion, it was observed that the resources (soil, plants, water, and fish) were indeed polluted with various heavy metals. However, the levels do not exceed the permissible limits set by the WHO and FAO.

Evaluation of flash point and calorific value of nanostructured rapeseed oil biodiesel as an automotive fuel

2023-10-30T16:52:55+00:00

The growing need for energy and the emphasis on reducing emissions have brought attention to the importance of developing renewable energy sources, specifically biofuels. However, using vegetable oils directly in diesel engines is hindered by their high viscosity, low ability to evaporate, and high content of polyunsaturated fats. Nanoemulsions, which are created without traditional surfactants, have demonstrated remarkable thermodynamic stability. This offers a potential solution to the challenges encountered with biofuels. This paper aimed to study the system of rapeseed oil-ethanol and 1-heptanol to understand the effects these nanoemulsions have on the calorific value and flash point. Ternary fuel blends with varying amounts of ethanol, rapeseed oil, and 1-heptanol were studied at constant weight percentages of 20% 1-heptanol and 30% rapeseed oil. The calorific value was determined by using a bomb calorimeter, and the results show that increasing the amount of 1-heptanol and rapeseed oil was able to increase the calorific value of the blend and maintain it close to that of diesel fuel. The flash point was determined using the Pensky Martens Closed Cup Method according to the American Society for Testing and Materials D93 standard (ASTM Standard D93-02a, 2002), and results show that only blends at 20% and 30% 1-heptanol at a constant of 30% rapeseed oil complied with the flash point requirement for diesel. This finding highlights the potential of the tricomponent fuel being used in an internal combustion engine without modification to the engine. The improved calorific value and flash point of the blends could be attributed to the co-solvent behaviour of 1-heptanol on rapeseed oil and ethanol.

Correlation of mechanical and optical properties of polypropylene plastic waste for application in composite panels

2023-10-30T17:03:12+00:00

Sustainable plastic waste management systems are needed to reduce environmental pollution impacts as they are non-biodegradable and emit greenhouse gases when burned. This study investigates the feasibility of using recycled polypropylene (PP) from commonly used plastics to process composite panels. The samples were obtained from household plastics with a Resin Identification Code (RIC) of 5. In addition, other common polymer samples were obtained from plastic coffee cups (W) and red basin tubs (R) and moulded using an injection moulding machine. The mechanical strengths of the resultant composites were plotted and compared with the theoretical value of the pure polymer. The ultimate tensile strength of the white cups and the red basin tubs was found to be 36 MPa and 14.55 MPa, while their yield points were 14.45 MPa and 4.55 MPa, respectively. Infrared spectra of the absorption of the samples were obtained using Fourier transform infrared (FTIR) spectroscopy. The band gaps were analysed from Tauc’s plot and were found to be of white cups and the red bath tubs at 3.392 eV and 3.646 eV, respectively. These properties were found to be suitable for recycled plastics to be applied to process value-added panels with no significant adverse influence on material properties.