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Recent Submissions
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.
Amplicon-based assessment of bacterial diversity and community structure in three tropical forest soils in Kenya
(Published by Elsevier Ltd., 2022-11-07) Eucharia Kenya, Grace Kinyanjui, Alex Kipnyargis, Franklin Kinyua, Mary Mwangi, Fathiya Khamis, Romano Mwirichia
Forest soils provide a multitude of habitats for diverse communities of bacteria. In this study, we selected three
tropical forests in Kenya to determine the diversity and community structure of soil bacteria inhabiting these
regions. Kakamega and Irangi are rainforests, whereas Gazi Bay harbors mangrove forests. The three natural
forests occupy different altitudinal zones and differ in their environmental characteristics. Soil samples were
collected from a total of 12 sites and soil physicochemical parameters for each sampling site were analyzed. We
used an amplicon-based Illumina high-throughput sequencing approach. Total community DNA was extracted
from individual samples using the phenol-chloroform method. The 16S ribosomal RNA gene segment spanning
the V4 region was amplified using the Illumina MiSeq platform. Diversity indices, rarefaction curves, hierarchical
clustering, principal component analysis (PCA), and non-metric multidimensional scaling (NMDS) analyses were
performed in R software. A total of 13,410 OTUs were observed at 97% sequence similarity. Bacterial communities were dominated by Proteobacteria, Bacteroidetes, Firmicutes, Actinobacteria, and Acidobacteria in both
rainforest and mangrove sampling sites. Alpha diversity indices and species richness were higher in Kakamega
and Irangi rainforests compared to mangroves in Gazi Bay. The composition of bacterial communities within and
between the three forests was also significantly differentiated (R ¼ 0.559, p ¼ 0.007). Clustering in both PCA and
NMDS plots showed that each sampling site had a distinct bacterial community profile. The NMDS analysis also
indicated that soil EC, sodium, sulfur, magnesium, boron, and manganese contributed significantly to the
observed variation in the bacterial community structure. Overall, this study demonstrated the presence of diverse
taxa and heterogeneous community structures of soil bacteria inhabiting three tropical forests of Kenya. Our
results also indicated that variation in soil chemical parameters was the major driver of the observed bacterial
diversity and community structure in these forests
OPTIMIZATION OF PARAMETERS FOR BIO-ETHANOL PRODUCTION FROM SWEET SORGHUM (Sorghum bicolor (L.) Moench) STALK JUICE AND FINGER MILLET MALT USING TAGUCHI METHOD
(Bull. Chem. Soc. Ethiop., 2023-11-02) Dolphene Okoth, Stephen Otieno, Francis Kiema, David Onyango and Chrispin Kowenje
ABSTRACT. Bio-ethanol is a promising renewable energy but its production is expensive from high cost of
feedstocks. In this study, sweet sorghum (Sorghum bicolor (L.) Moench) stalk juice was investigated for bio-ethanol
production. Most reports on bio-ethanol productions use commercial Saccharomyces cerevisiae as yeast. However,
this study used finger millet (Eleusine coracana) malt with already high adaptation to local conditions and high
economic viability as it is being utilized by the indigenous communities. Five sweet sorghum varieties of IESV92001-DL (V1), NTJ (V2), 15233-IESV (V3), 92008-DJ (V4) and IESV-92028-DL (V5) were planted at
0°3'45.4644" North, 34°17'16.1052" South, in Kenya. °Brix content of juice was determined at 11th to 16th weeks
after sowing. Highest °Brix for all varieties were at 15th week where V1 was highest at 22.07. V1 was then harvested
for the juice. Factors affecting fermentation; temperature, time, pH and yeast to substrate ratio were optimized using
Taguchi method and were obtained as 30 ℃, 48 hours, pH 5 and 5 g/L, respectively. Kinetics parameters of Vmax
and Km were 0.35 g/L/h and 12.56 g/L, respectively. The optimized and kinetic parameters were within literature
values and therefore finger millet malt has a great potential, as a substitute yeast source, in commercial bio-ethanol
production.
