National Research Repository
The National Research Fund facilitates research for the advancement of Science, Technology and Innovation. One of our core functions is to compile and maintain a national database of research and innovation projects funded by the Fund and other agencies as per the STI Act of 2013.
Browse Collections
Select a community to browse its collections.
Registry of Repositories in Kenya (RoRiK)
NRF is developing a Registry of Research Repositories in Kenya (RoRiK) in an effort to promote access to research data in the country.
Recent Submissions
EFFICACY AND KINETICS OF ADSORPTION OF SINGLE AND MULTIPLE HEAVY METAL CATIONS FROM AQUEOUS SOLUTIONS BY FRUIT WASTE PRODUCTS
(2016-12) NTHIGA ESTHER WANJA
Removal of toxic heavy metals from water has been a major challenge, especially in
rural areas. Various methods have been used for this purpose; among them is biosorption based technology. The technology has been recognized as an economical and
eco-friendly method for removal of toxic anions and cations from wastewater.
Moreover, the efficacy of biomass in adsorption of cations and anions can be improved
by different modifications, including treatment with sulphuric acid. Numerous
approaches have been developed for adsorption of single ions in solution. However,
toxic metallic or non-metallic ions rarely occur singly in wastewater. The presence of
multiple ions in solution may often have agonistic or antagonistic effect on the
efficiency of an adsorbent, and there is insufficient information on the efficacy of
different methods for removing multiple ions. This study assessed the efficacy and
mechanism of acid treated (modified) and raw (unmodified) biosorbents (derived from
lemon, sweet yellow passion, banana, watermelon peels, and avocado seeds) for
removal of toxic cations (Cd, Pb and Cu) from water. Functional groups of adsorbents
were identified by mid-infrared spectroscopy (MIR) and their surface morphology was
probed by scanning electron microscopy (SEM). the efficacy of each adsorbent was
evaluated by quantifying the kinetics and levels of cations adsorbed at different pH of
the solution, initial concentrations, contact time and adsorbent dose. Desorption
experiments were conducted to determine the possibility of recovering ions and reusing
the sorbents for next cycle of deployment. Experimental data of each metal ion was
described by either Freundlich isotherm or Langmuir isotherm. The acid treated fruit
peels and avocado seeds recorded higher efficacy as compared to raw adsorbents.
Generally, acid treated watermelon peels demonstrated the highest uptake of 130.23
mg/g of Pb (II), followed by 114.234 mg/g of Cu (II) and 97.14 mg/g of Cd (II) ions.
Further adsorption trials with binary and ternary metal blends on showed significant
reductions in metal uptake capacities of evaluated adsorbents as compared to single
metal systems. On account of metal preference, the selectivity order for metal ions
towards the all the studied biomass was Pb (II) > Cu (II) > Cd (II). Time-course
measurements indicated involvement of pseudo-second-order kinetics in adsorptions.
Desorption efficacies were high on acid-treated adsorbents: 99.97 % ofPb(II) ions from
acid treated avocado seeds; and 99.79 % of Cu(II) and 99.23 % of Cd (II) from acid
treated watermelon peels. The results show good performance of the fruit peels and
avocado seeds in adsorbing single and multiple metal ions, and the potential of using
such wastes for purifying drinking and cooking water at household level.
Machine Learning Prediction Models for Postpartum Depression, a Review of Literature
(International Journal of Computer Applications Technology and Research, 2022) George Kimwomi, Mvurya Mgala, Fullgence Mwakondo, Pamela Kimeto
Postpartum depression is a medical condition which continue to affect many mothers after delivery even though
the disease can be prevented. It consequently exposes mothers and family members to illness and even death. Families,
governments and other stakeholders incur heavy expenditure in the management of the disease. Research studies have been
done to develop machine learning models for prediction of mothers at risk of postpartum depression during pregnancy for
preventive measures. This paper presents a literature review of the machine learning prediction models which have been
developed for the condition with specific focus on feature selection methods, algorithms used and the resulting performance.
Literature review was done with google scholar integrated to an online institutional account for e-resources from e-databases
accessed by subscription or free access. Inclusion involved all articles with the key words “machine learning, prediction model,
postpartum depression” in the articles dated from 2018 to 2022 and sorted by relevance. A total of 3430 articles were listed
while only 17 which were accessible with full text were eligible and therefore selected for the study. Analyzes were done
using Microsoft Excel and descriptive analysis. Findings and conclusions will inform scientists on the status of research in the
area to guide new studies, and inform the market on the potential benefits of integrating machine learning models in their
systems.
An Appropriate Feature Selection Technique for Use on Socio-Demographic Predictor Variables to Enable Early Detection of Preeclampsia: A Review of Literature
(Computer Engineering and Intelligent Systems, 2022-08-31) Arina A. Jamwa, Mgala Mvurya, Antony Luvanda, Pamela Kimetto
Preeclampsia is categorized by the World Health Organization as one of the leading causes of high morbidity
and mortality in infant and mothers around the world. It accounts for between 3% to 5% of all pregnancy related
complications reported worldwide. This condition is much higher among women aged between 30 and 40 years
in developing nations especially those in the sub-Saharan region, where the figures range between 5.6% to 6.5%
of all reported pregnancies. Preeclampsia is a condition normally detected in the third trimester of pregnancy that
is characterized by high risk factors such as sudden High Blood Pressure, High levels of protein in Urine,
Chronic kidney disease and Type 1 or 2 diabetes. If preeclampsia is not detected early, it can advance to
eclampsia or result to maternal and fetal death. This study sought to identify the optimal features as predictors to
enable early detection of preeclampsia through a systematic review of relevant literature. The predictors under
consideration were; Maternal age, Occupation, Education, ANC Attendance, BMI, Blood Pressure, Medical
History, Urine dipstick, Gravida, Ethnicity, Gestation weeks as identified from literature.
REPARAMETERIZATION OF AUTOREGRESSIVE DISTRIBUTED LAG TO ERROR CORRECTION MODEL TO STUDY YOUTH UNEMPLOYMENT IN KENYA
(2019-07) Shem Otio Odhiambo Sam
The research provides statistical basis for assessing and prioritizing investment policies, initiatives and projects to maximise youth employment by scrutinizing in uence of macroeconomic variables. The macroeconomic variables considered are gross domestic product (GDP), external debt (ED), foreign domestic investment (FDI), private investment(PI), youth unemployment(YUN), literacy rate (LR), and youth population (POP). The research approach taken uses predictive analytics such as impulse response functions and variance decomposition from vector error corrections model (VECM) and cointegration regression in autoregressive distributed lag (ARDL) to identify key determinants of youth unemployment to prioritize investment. This research analyzes reparameterization of ARDL to VECM through cointegration of time series. First, the time series data undergo logarithm transformation to reduce outlier e ects and have elasticity interpreted in terms of percentage. The study scrutinizes the e ects of macroeconomic shocks on youth unemployment in Kenya. For this purpose, the Augmented Dickey-Fuller test is conducted to assess stationarity of the variables used. Then Johansen Cointegration test is carried out to establish the rank at which the series are cointegrated. The unit root test has been performed on YUN, GDP, ED, FDI, PI, and LR, and POP to assess stationarity. The cointegrated dynamic ARDL model is estimated using ordinary least squares (OLS) and e ects of variables and their lags interpreted. The results reveal that Gross Domestic Product (GDP) and its second lag have negative e ect on youth unemployment, that is, one unit increase in (GDP) and GDP lag 2 reduce youth unemployment by 0.207922% and 0.2052705% respectively. Also, one unit of External Debt (ED) and ED lag 2 reduce youth unemployment by 0.07303% and 0.009116% respectively. Furthermore, unit increase in one year lag of youth literacy rate reduces youth unemployment by 0.0892691%. Lastly, lag one and three of population reduce youth unemployment by 0.2590455% and 4.3093119% respectively. The Johansen Cointegration Analysis has revealed three long run relationships which can be interpreted as a GDP e ect; External Debt e ect and Foreign Direct Investment e ect relations. A structural VECM has been described through restrictions taken from the Cointegration Analysis. Based on the results of the Impulse-Response Function and variance decomposition analyses of the Structural VECM, it is concluded that GDP, literacy level, population, and FDI shocks have signi cant iii e ects on Kenyan youth unemployment in the long run. On the superiority of the two models, whereas ARDL captures the in uence of past shocks through coe cients of lags, VECM predicts the e ects of current shocks and resulting movement of variables more than 10 unit steps ahead. Also, Granger causality present in ARDL does not exist in reparameterized VECM. The F-test and t-test reveal that the two models are signi cant at 95% c
SCREENING FOR SALT STRESS TOLERANCE, IN VITRO REGENERABILITY AND RELATIVE GROWTH AMONG SELECTED KENYAN SWEETPOTATO Ipomoea batatas L. Lam GENOTYPES
(2018-12) Nzaro Gona Makenzi
Salinity affects about 40% of the global area mainly the arid and semi-arid
regions. In Kenya the ASALs cover approximately 80% of the total area where
agricultural production constraints include water scarcity, salinity and sodicity.
Sweetpotato Ipomoea batatas L. (Lam.) is the third most economically important
root crop after potato and cassava in the world cultivated for human consumption,
animal feed and industrial uses. The production of sweetpotato by smallholder
farmers in the ASALs is affected by abiotic stresses including salinity. However,
data on levels of salinity stress tolerance among Kenyan sweetpotato genotypes is
limited. The objective of the study was to determine physiological response of
sweetpotato to salinity stress and assess in vitro regeneration among selected
Kenyan sweetpotato genotypes. Fifteen Kenyan selected sweetpotato genotypes
Ksp 36, Ksp 20, Ksp28, Kemb 36, Kemb 10, Kemb 23, Kalamb Nyerere, Mweu
Mutheki, Enaironi, Mugande, Zambezi, Spk 004, Spk 013, Spk203 and Jewel were
used for the present study. In vitro shoot organogenesis using TDZ was used in
detrmining regenrability while sudden shock treatment and an incremental stress
regime were used for studying the physiological resposes of sweetpotato
genotypes to osmotic and salinity stresses. Physiological responses was assessed
by measuring the leaf photosynthetic pigment content, vine and leaf length,
relative water content and yield. All data collected were analyzed using ANOVA
at 95 % confidence interval using SAS statistical software. Mean separation was
carried out using pairwise comparison test at 5 % probability level. Results shows
that the highest number of adventitious bud; 8.00 (Kalamb nyerere) was produced
in the dark at 0.25 mg/l TDZ hormone level. Regeneration frequencies of
adventitious buds recovered in the dark was the highest, 83.20% (Jewel) at 0.10
mg/l NAA hormone level. The best genotypes for direct shoot organogenesis were
Kalamb nyerere, Kemb 36 and Spk 004. Growth analysis shows that the
sweetpotato genotypes with the highest mean growth rates were Kalamb Nyerere,
Spk203, Enaironi, and Mweu Mutheki. Results show that at high in vitro osmotic
and salinity stresses all genotypes had reduced amount of photosynthetic
pigments. Best performing genotypes under in vitro osmotic and salinity stress
were Ksp 36, Ksp 28 and Zambezi. Results of in vivo salinity stress shows that all
the genotypes had reduced vine length except Spk 013, Spk 203 and Kemb 23.
Yield was negatively affected by in vivo salinity stress but was lowest in Spk004
(-31.13%), Mweu Mutheki (-31.43%) and Ksp 36 (-35.29%). Using the combined
morphophysological approach the following genotypes were found to be salt
tolerant Spk 004, Mweu Mutheki, Ksp 36, Kemb 36 and Kalamb Nyerere and can
be incooperated in breeding programs so as to introgress tolerance to sensitive
genotypes.
